程序代写代做代考 javascript assembly hadoop html distributed system CGI concurrency database Elm Excel c++ chain cache c# ant game data structure compiler Functional Dependencies algorithm hbase Hive Java gui asp graph file system finance flex C ER go case study data mining JDBC clock Fortran decision tree DATABASE SYSTEMS

DATABASE SYSTEMS
Design, Implementation, and Management
12e
Carlos Coronel | Steven Morris
Australia • Brazil • Mexico • Singapore • United Kingdom • United States
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Database Systems: Design, Implementation, and Management, 12th Edition
Carlos Coronel and Steven Morris
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Dedication
To the treasures in my life: To Victoria, for 26 wonderful years. Thank you for your un- ending support, for being my angel, my sweetie, and most importantly, my best friend. To Carlos Anthony, who is an awesome older brother to all. Thank you for your words of wisdom, hard-working attitude, and for giving us reasons to be happy. You are still young; your best times are still to come. To Gabriela Victoria, who is the image of bril- liance, beauty, and faithfulness. Thank you for being the sunshine in my cloudy days. Your future is bright and endless. To Christian Javier, who is smarter than of all of us. Thank you for being the youthful reminder of life’s simple beauties. Keep challenging yourself to new highs. To my parents, Sarah and Carlos, thank you for your sacrifice and example. To all of you, you are all my inspiration. “TQTATA.”
Carlos Coronel
To Pamela, from high school sweetheart through 26 years of marriage, you are the beau- tiful love of my life who has supported, encouraged, and inspired me. More than anyone else, you are responsible for whatever successes I have achieved. To my son, Alexander Logan, your depth of character is without measure. You are my pride and joy. To my daughter, Lauren Elizabeth, your beauty and intensity take my breath away. You are my heart and soul. Thank you all for the sacrifices you have made that enabled me to pur- sue this dream. I love you so much more than I can express. To my mother, Florence Maryann, and to the memory of my father, Alton Lamar, together they instilled in me the desire to learn and the passion to achieve. To my mother-in-law, Connie Duke, and to the memory of my father-in-law, Wayne Duke, they taught me to find joy in all things. To all of you, with all my love, I dedicate this book.
Steven Morris
For Peter
To longtime colleague and friend, Peter Rob: Your drive and dedication to your students started this book. Your depth of knowledge, attention to detail, and pursuit of excellence made it succeed. Your patience and guidance continue to light our path. It is our sincere hope that, as we move forward, we can continue to live up to your standard. Enjoy your retirement, my friend; you have surely earned it.
Carlos Coronel and Steven Morris
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Dedication iii

Brief Contents
Preface, xiv
Text Features, xix Additional Features, xxi Acknowledgments, xxiii
Part 1: Database Concepts 1
1. Database Systems, 2
2. Data Models, 35
Part 2: Design Concepts 71
3. The Relational Database Model, 72
4. Entity Relationship (ER) Modeling, 117
5. Advanced Data Modeling, 169
6. Normalization of Database Tables, 201
Part 3: Advanced Design and Implementation 245
7. Introduction to Structured Query Language (SQL), 246
8. Advanced SQL , 340
9. Database Design, 439
Part 4: Advanced Database Concepts 481
10. Transaction Management and Concurrency Control, 482
11. Database Performance Tuning and Query Optimization, 515
12. Distributed Database Management Systems, 553
13. Business Intelligence and Data Warehouses, 589
14. Big Data Analytics and NoSQL, 648
Part 5: Databases and the Internet 679 15. Database Connectivity and Web Technologies, 680
Part 6: Database Administration 721 16. Database Administration and Security, 722
Glossary, 769
Index, 783
iv Brief Contents
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The following appendixes are included on the Instructor and Student Companion Sites at www.cengagebrain.com.
Appendix A1: Appendix A2: Appendix B: Appendix C: Appendix D: Appendix E: Appendix F: Appendix G: Appendix H: Appendix I: Appendix J: Appendix K: Appendix L: Appendix M: Appendix N: Appendix O:
Designing Databases with Visio Professional 2010: A Tutorial
Designing Databases with Visio 2013: A Tutorial
The University Lab: Conceptual Design
The University Lab: Conceptual Design Verification, Logical Design, and Implementation Converting an ER Model into a Database Structure
Comparison of ER Model Notations Client/Server Systems
Object-Oriented Databases
Unified Modeling Language (UML) Databases in Electronic Commerce
Web Database Development with ColdFusion The Hierarchical Database Model
The Network Database Model
MS Access Tutorial
Creating a New Database Using Oracle 12c Data Warehouse Implementation Factors
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Brief Contents v

Contents
Part 1: Database Concepts 1 Chapter1:DatabaseSystems 2
1-1 1-2 1-3
1-4 1-5
Why Databases? 3
Data versus Information 4 Introducing the Database 6
1-3a Role and Advantages of the DBMS 6 1-3b Types of Databases 8
Why Database Design is Important 11
Evolution of File System Data Processing 14
1-5a Manual File Systems 14
1-5b Computerized File Systems 15
1-5c File System Redux: Modern End-User Productivity Tools 17
Problems with File System Data Processing 18 1-6a Structural and Data Dependence 19
1-6b Data Redundancy 20
1-6c Data Anomalies 21
Database Systems 21
1-7a The Database System Environment 22
1-7b DBMS Functions 24
1-7c Managing the Database System: A Shift in Focus 28
Preparing for Your Database Professional Career 28
Summary 30 • Key Terms 31 • Review Questions 32 • Problems 32
1-6
1-7
1-8
Chapter 2: Data Models 35
2-1 2-2 2-3 2-4
2-5
2-6
Data Modeling and Data Models The Importance of Data Models 37
36 Data Model Basic Building Blocks 37
Business Rules 39
2-4a Discovering Business Rules 39
2-4b Translating Business Rules into Data Model Components 40 2-4c Naming Conventions 41
The Evolution of Data Models 41
2-5a Hierarchical and Network Models 41
2-5b The Relational Model 43
2-5c The Entity Relationship Model 45
2-5d The Object-Oriented (OO) Model 48
2-5e Object/Relational and XML 49
2-5f Emerging Data Models: Big Data and NoSQL 50 2-5g Data Models: A Summary 56
Degrees of Data Abstraction 57 2-6a The External Model 60
2-6b The Conceptual Model 61 2-6c The Internal Model 62
2-6d The Physical Model 63
Summary 64 • Key Terms 65 • Review Questions 65 • Problems 66
Part 2: Design Concepts 71
Chapter 3: The Relational Database Model 72
3-1 A Logical View of Data 73
3-1a Tables and Their Characteristics 73
vi Contents
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3-2 Keys 76
3-2a Dependencies 76
3-2b Types of Keys 77 3-3 Integrity Rules 80
3-4 Relational Algebra 82
3-4a Formal Definitions and Terminology 82 3-4b Relational Set Operators 83
3-5 The Data Dictionary and the System Catalog 91 3-6 Relationships within the Relational Database 93
3-6a The 1:M Relationship 93 3-6b The 1:1 Relationship 95 3-6c The M:N Relationship 97
3-7 Data Redundancy Revisited 101
3-8 Indexes 103
3-9 Codd’s Relational Database Rules 104
Summary 106 • Key Terms 107 • Review Questions 107 • Problems 110
Chapter 4: Entity Relationship (ER) Modeling 117
4-1 The Entity Relationship Model (ERM) 118 4-1a Entities 118
4-1b Attributes 118
4-1c Relationships 124
4-1d Connectivity and Cardinality 125 4-1e Existence Dependence 126
4-1f Relationship Strength 126
4-1g Weak Entities 129
4-1h Relationship Participation 131
4-1i Relationship Degree 134
4-1j Recursive Relationships 136
4-1k Associative (Composite) Entities 138
4-2 Developing an ER Diagram 140
4-3 Database Design Challenges: Conflicting Goals 147
Summary 152 • Key Terms 153 • Review Questions 153 • Problems 156 • Cases 161
Chapter 5: Advanced Data Modeling 169
5-1 The Extended Entity Relationship Model 170 5-1a Entity Supertypes and Subtypes 170
5-1b Specialization Hierarchy 171
5-1c Inheritance 172
5-1d Subtype Discriminator 174
5-1e Disjoint and Overlapping Constraints 174 5-1f Completeness Constraint 175
5-1g Specialization and Generalization 176
5-2 Entity Clustering 176
5-3 Entity Integrity: Selecting Primary Keys 177
5-3a Natural Keys and Primary Keys 178
5-3b Primary Key Guidelines 178
5-3c When To Use Composite Primary Keys 178 5-3d When To Use Surrogate Primary Keys 180
5-4 Design Cases: Learning Flexible Database Design 182
5-4a Design Case 1: Implementing 1:1 Relationships 182
5-4b Design Case 2: Maintaining History of Time-Variant Data 183 5-4c Design Case 3: Fan Traps 186
5-4d Design Case 4: Redundant Relationships 187
Summary 188 • Key Terms 189 • Review Questions 189 • Problems 190 • Cases 192
Chapter 6: Normalization of Database Tables 201
6-1 Database Tables and Normalization 202 6-2 The Need For Normalization 202
6-3 The Normalization Process 206
6-3a Conversion To First Normal Form 208 6-3b Conversion To Second Normal Form 211 6-3c Conversion To Third Normal Form 213
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Contents vii

viii Contents
6-4 Improving the Design 215
6-5 Surrogate Key Considerations 219 6-6 Higher-Level Normal Forms 220
6-6a The Boyce-Codd Normal Form 221
6-6b Fourth Normal Form (4NF) 224
6-7 Normalization and Database Design 226 6-8 Denormalization 229
6-9 Data-Modeling Checklist 232
Summary 234 • Key Terms 235 • Review Questions 235 • Problems 237
Part 3: Advanced Design and Implementation 245 Chapter 7: Introduction to Structured Query Language (SQL) 246
7-1 Introduction to SQL 247
7-2 Data Definition Commands 249
7-2a The Database Model 249 7-2b Creating The Database 251 7-2c The Database Schema 251 7-2d Data Types 252
7-2e Creating Table Structures 255 7-2f SQL Constraints 259
7-2g SQL Indexes 263
7-3 Data Manipulation Commands 264 7-3a Adding Table Rows 264
7-3b Saving Table Changes 266
7-3c Listing Table Rows 266
7-3d Updating Table Rows 268
7-3e Restoring Table Contents 269
7-3f Deleting Table Rows 269
7-3g Inserting Table Rows with a Select Subquery 270
7.4 SELECT Queries 271
7-4a Selecting Rows with Conditional Restrictions 271 7-4b Arithmetic Operators: The Rule of Precedence 276 7-4c Logical Operators: AND, OR, and NOT 277
7-4d Special Operators 279
7-5 Additional Data Definition Commands 283
7-5a Changing a Column’s Data Type 284
7-5b Changing a Column’s Data Characteristics 284 7-5c Adding a Column 284
7-5d Dropping a Column 285
7-5e Advanced Data Updates 285
7-5f Copying Parts of Tables 287
7-5g Adding Primary and Foreign Key Designations 289 7-5h Deleting a Table from the Database 290
7-6 Additional SELECT Query Keywords 290 7-6a Ordering a Listing 290
7-6b Listing Unique Values 292 7-6c Aggregate Functions 292 7-6d Grouping Data 297
7-7 Joining Database Tables 300
7-7a Joining Tables with an Alias 303 7-7b Recursive Joins 303
Summary 305 • Key Terms 306 • Review Questions 306 • Problems 307 • Cases 331
Chapter 8: Advanced SQL 340
8-1 SQL Join Operators 341 8-1a Cross Join 342
8-1b Natural Join 343
8-1c JOIN USING Clause 344 8-1d JOIN ON Clause 345 8-1e Outer Joins 347
8-2 Subqueries and Correlated Queries 349 8-2a WHERE Subqueries 351
8-2b IN Subqueries 352
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8-2c HAVING Subqueries 353
8-2d Multirow Subquery Operators: ANY and ALL 353 8-2e FROM Subqueries 355
8-2f Attribute List Subqueries 356
8-2g Correlated Subqueries 358
8-3 SQL Functions 361
8-3a Date and Time Functions 361 8-3b Numeric Functions 366
8-3c String Functions 366
8-3d Conversion Functions 368
8-4 Relational Set Operators 371 8-4a UNION 371
8-4b UNION ALL 373
8-4c INTERSECT 373
8-4d EXCEPT (MINUS) 375 8-4e Syntax Alternatives 377
8-5 Virtual Tables: Creating a View 377 8-5a Updatable Views 379
8-6 Sequences 382
8-7 Procedural SQL 387
8-7a Triggers 392
8-7b Stored Procedures 401
8-7c PL/SQL Processing with Cursors 407 8-7d PL/SQL Stored Functions 409
8-8 Embedded SQL 410
Summary415 • KeyTerms416 • ReviewQuestions417 • Problems418 • Cases435
Chapter 9: Database Design 439
9-1 The Information System 440
9-2 The Systems Development Life Cycle 442
9-2a Planning 442
9-2b Analysis 443
9-2c Detailed Systems Design 444 9-2d Implementation 444
9-2e Maintenance 445
9-3 The Database Life Cycle 445
9-3a The Database Initial Study 445 9-3b Database Design 450
9-3c Implementation and Loading 451 9-3d Testing and Evaluation 454
9-3e Operation 456
9-3f Maintenance and Evolution 457
9-4 Conceptual Design 457
9-4a Data Analysis and Requirements 459
9-4b Entity Relationship Modeling and Normalization 461 9-4c Data Model Verification 464
9-4d Distributed Database Design 467
9-5 DBMS Software Selection 467 9-6 Logical Design 468
9-6a Map the Conceptual Model to the Logical Model 468
9-6b Validate the Logical Model Using Normalization 470
9-6c Validate Logical Model Integrity Constraints 470
9-6d Validate the Logical Model Against User Requirements 471
9-7 Physical Design 471
9-7a Define Data Storage Organization 472 9-7b Define Integrity and Security Measures 472 9-7c Determine Performance Measures 473
9-8 Database Design Strategies 473
9-9 Centralized Versus Decentralized Design 474
Summary 477 • Key Terms 477 • Review Questions 477 • Problems 478
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Contents ix

x Contents
Part 4: Advanced Database Concepts 481 Chapter10:TransactionManagementandConcurrencyControl 482
10-1 What Is a Transaction? 483
10-1a Evaluating Transaction Results 484 10-1b Transaction Properties 487
10-1c Transaction Management with SQL 488 10-1d The Transaction Log 489
10-2 Concurrency Control 490 10-2a Lost Updates 490
10-2b Uncommitted Data 491 10-2c Inconsistent Retrievals 492 10-2d The Scheduler 493
10-3 Concurrency Control with Locking Methods 495 10-3a Lock Granularity 496
10-3b Lock Types 498
10-3c Two-Phase Locking to Ensure Serializability 500 10-3d Deadlocks 500
10-4 Concurrency Control with Time Stamping Methods 502 10-4a Wait/Die and Wound/Wait Schemes 502
10-5 Concurrency Control with Optimistic Methods 503 10-6 ANSI Levels of Transaction Isolation 504
10-7 Database Recovery Management 506
10-7a Transaction Recovery 506
Summary 510 • Key Terms 511 • Review Questions 511 • Problems 512
Chapter 11: Database Performance Tuning
and Query Optimization 515
11-1 Database Performance-Tuning Concepts 516 11-1a Performance Tuning: Client and Server 517 11-1b DBMS Architecture 518
11-1c Database Query Optimization Modes 520 11-1d Database Statistics 521
11-2 Query Processing 522
11-2a SQL Parsing Phase 523
11-2b SQL Execution Phase 524
11-2c SQL Fetching Phase 525
11-2d Query Processing Bottlenecks 525
11-3 Indexes and Query Optimization 526 11-4 Optimizer Choices 528
11-4a Using Hints to Affect Optimizer Choices 530 11-5 SQL Performance Tuning 531
11-5a Index Selectivity 531
11-5b Conditional Expressions 533 11-6 Query Formulation 534
11-7 DBMS Performance Tuning 536 11-8 Query Optimization Example 538
Summary 546 • Key Terms 547 • Review Questions 547 • Problems 548
Chapter 12: Distributed Database Management Systems 553
12-1 The Evolution of Distributed Database Management Systems 554 12-2 DDBMS Advantages and Disadvantages 556
12-3 Distributed Processing and Distributed Databases 556
12-4 Characteristics of Distributed Database Management Systems 559 12-5 DDBMS Components 560
12-6 Levels of Data and Process Distribution 561
12-6a Single-Site Processing, Single-Site Data 561 12-6b Multiple-Site Processing, Single-Site Data 562 12-6c Multiple-Site Processing, Multiple-Site Data 563
12-7 Distributed Database Transparency Features 564 12-8 Distribution Transparency 565
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12-9 Transaction Transparency 568
12-9a Distributed Requests and Distributed Transactions 568 12-9b Distributed Concurrency Control 571
12-9c Two-Phase Commit Protocol 571
12-10 Performance and Failure Transparency 573 12-11 Distributed Database Design 575
12-11a Data Fragmentation 575 12-11b Data Replication 578 12-11c Data Allocation 580
12-12 The CAP Theorem 581
12-13 C. J. Date’s 12 Commandments for Distributed Databases 583
Summary 584 • Key Terms 585 • Review Questions 585 • Problems 586
Chapter 13: Business Intelligence and Data Warehouses 589
13-1 The Need for Data Analysis 590 13-2 Business Intelligence 590
13-2a Business Intelligence Architecture 592
13-2b Business Intelligence Benefits 598
13-2c Business Intelligence Evolution 598
13-2d Business Intelligence Technology Trends 601
13-3 Decision Support Data 602
13-3a Operational Data Versus Decision Support Data 602 13-3b Decision Support Database Requirements 605
13-4 The Data Warehouse 607 13-4a Data Marts 610
13-4b Twelve Rules That Define a Data Warehouse 610 13-5 Star Schemas 610
13-5a Facts 611
13-5b Dimensions 611
13-5c Attributes 612
13-5d Attribute Hierarchies 614
13-5e Star Schema Representation 616
13-5f Performance-Improving Techniques for the Star Schema 617
13-6 Online Analytical Processing 621
13-6a Multidimensional Data Analysis Techniques 621 13-6b Advanced Database Support 623
13-6c Easy-to-Use End-User Interfaces 623
13-6d OLAP Architecture 623
13-6e Relational OLAP 626
13-6f Multidimensional OLAP 628
13-6g Relational versus Multidimensional OLAP 628
13-7 SQL Extensions for OLAP 629 13-7a The ROLLUP Extension 630
13-7b The CUBE Extension 631 13-7c Materialized Views 633
Summary 636 • Key Terms 637 • Review Questions 637 • Problems 639
Chapter 14: Big Data Analytics and NoSQL 648
14-1 Big Data 649
14-1a Volume 651
14-1b Velocity 652
14-1c Variety 653
14-1d Other Characteristics 654
14-2 Hadoop 655 14-2a HDFS 655
14-2b MapReduce 658
14-2c Hadoop Ecosystem 660 14-3 NoSQL 662
14-3a Key-Value Databases 663
14-3b Document Databases 664
14-3c Column-Oriented Databases 665 14-3d Graph Databases 668
14-3e NewSQL Databases 669
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Contents xi

xii Contents
14-4 Data Analytics 670 14-4a Data Mining 671
14-4b Predictive Analytics 673
Summary 675 • Key Terms 676 • Review Questions 677
Part 5: Databases and the Internet 679
Chapter 15: Database Connectivity and Web Technologies 680
15-1 Database Connectivity 681
15-1a Native SQL Connectivity 682
15-1b ODBC, DAO, and RDO 683
15-1c OLE-DB 685
15-1d ADO.NET 687
15-1e Java Database Connectivity (JDBC) 691
15-2 Database Internet Connectivity 692
15-2a Web-to-Database Middleware: Server-Side Extensions 693 15-2b Web Server Interfaces 695
15-2c The Web Browser 696
15-2d Client-Side Extensions 697
15-2e Web Application Servers 698
15-2f Web Database Development 699
15-3 Extensible Markup Language (XML) 702
15-3a Document Type Definitions (DTD) and XML Schemas 704 15-3b XML Presentation 706
15-3c XML Applications 708
15-4 Cloud Computing Services 709
15-4a Cloud Implementation Types 712
15-4b Characteristics of Cloud Services 712
15-4c Types of Cloud Services 713
15-4d Cloud Services: Advantages and Disadvantages 714 15-4e SQL Data Services 716
Summary 717 • Key Terms 718 • Review Questions 718 • Problems 719
Part 6: Database Administration 721 Chapter 16: Database Administration and Security 722
16-1 Data as a Corporate Asset 723
16-2 The Need for a Database and its Role in an Organization 724 16-3 Introduction of a Database: Special Considerations 726
16-4 The Evolution of Database Administration 727
16-5 The Database Environment’s Human Component 731
16-5a The DBA’s Managerial Role 733
16-5b The DBA’s Technical Role 738 16-6 Security 745
16-6a Security Policies 746
16-6b Security Vulnerabilities 746 16-6c Database Security 748
16-7 Database Administration Tools 749 16-7a The Data Dictionary 750
16-7b Case Tools 752
16-8 Developing a Data Administration Strategy 755
16-9 The DBA’s Role in the Cloud 756
16-10 The DBA at Work: Using Oracle for Database Administration 757
16-10a Oracle Database Administration Tools 758
16-10b Ensuring that the RDBMS Starts Automatically 758 16-10c Creating Tablespaces and Datafiles 760
16-10d Managing Users and Establishing Security 762
16-10e Customizing the Database Initialization Parameters 763
Summary 765 • Key Terms 766 • Review Questions 767 Glossary 769
Index 783
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Preface
xiv Preface
It is our great pleasure to present the twelfth edition of Database Systems. We are grateful and humbled that so many of our colleagues around the world have chosen this text to support their classes. We wrote the first edition of this book because we wanted to explain the complexity of database systems in a language that was easy for students to understand. Over the years, we have maintained this emphasis on reaching out to students to explain complex concepts in a practical, approachable manner. This book has been successful through eleven editions because the au- thors, editors, and the publisher paid attention to the impact of technology and to adopter ques- tions and suggestions. We believe that this twelfth edition successfully reflects the same attention to such factors.
In many respects, rewriting a book is more difficult than writing it the first time. If the book is successful, as this one is, a major concern is that the updates, inserts, and deletions will adversely affect writing style and continuity of coverage. The combination of superb reviewers and editors, plus a wealth of feedback from adopters and students of the previous editions, helped make this new edition the best yet.
Changes to The Twelfth Edition
In this twelfth edition, we added some new features and reorganized some coverage to provide a better flow of material. Aside from enhancing the already strong coverage of database design, we made other improvements in the topical coverage. In particular, the continued growth of Big Data and NoSQL technologies have challenged the status quo in the database industry. Therefore, we created an entire new chapter, Big Data Analytics and NoSQL, to help students grasp the key aspects of these complex new technologies and challenges. The twelfth edition also presents a ma- jor step forward in the integration of digital content with the text through online, automatically graded exercises to improve student outcomes. Here are a few of the highlights of changes in the twelfth edition:
• New coverage of Big Data challenges beyond the traditional 3Vs
• Expanded coverage of Hadoop, the Hadoop Distributed File System (HDFS), and MapReduce
• Updated coverage of cloud data services and their impact on DBAs
• Expanded coverage of NoSQL databases, including key-value databases, document databases, column-oriented database, and graph databases
• New coverage of the emerging NewSQL technologies
• Improved coverage of data visualization
• Added coverage of new sequence and identity capabilities in Oracle and SQL Server
• Complete redesign of the look and feel of the text and layout to improve readability and visual appeal
• Embedded key term definitions within the text
This twelfth edition continues to provide a solid and practical foundation for the design, im- plementation, and management of database systems. This foundation is built on the notion that, while databases are very practical, their successful creation depends on understanding the im- portant concepts that define them. It’s not easy to come up with the proper mix of theory and practice, but the previously mentioned feedback suggests that we largely succeeded in our quest to maintain the proper balance.
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

The Approach: A Continued Emphasis
On Design
As the title suggests, Database Systems: Design, Implementation, and Management covers three broad aspects of database systems. However, for several important reasons, special attention is given to database design.
• The availability of excellent database software enables people with little experience to cre- ate databases and database applications. Unfortunately, the “create without design” approach usually paves the road to a number of database disasters. In our experience, many database system failures are traceable to poor design and cannot be solved with the help of even the best programmers and managers. Nor is better DBMS software likely to overcome problems created or magnified by poor design. Even the best bricklayers and carpenters can’t create a good building from a bad blueprint.
• Most vexing problems of database system management seem to be triggered by poorly de- signed databases. It hardly seems worthwhile to use scarce resources to develop excellent da- tabase management skills merely to use them on crises induced by poorly designed databases.
• Design provides an excellent means of communication. Clients are more likely to get what they need when database system design is approached carefully and thoughtfully. In fact, clients may discover how their organizations really function once a good database design is completed.
• Familiarity with database design techniques promotes understanding of current database technologies. For example, because data warehouses derive much of their data from opera- tional databases, data warehouse concepts, structures, and procedures make more sense when the operational database’s structure and implementation are understood.
Because the practical aspects of database design are stressed, we have covered design concepts and procedures in detail, making sure that the numerous end-of-chapter problems and cases are sufficiently challenging so students can develop real and useful design skills. We also make sure that students understand the potential and actual conflicts between database design elegance, information requirements, and transaction processing speed. For example, it makes little sense to design databases that meet design elegance standards while they fail to meet end-user informa- tion requirements. Therefore, we explore the use of carefully defined trade-offs to ensure that the databases meet end-user requirements while conforming to high design standards.
Topical Coverage
The Systems View
The book’s title begins with Database Systems. There- fore, we examine the database and design concepts covered in Chapters 1–6 as part of a larger whole by placing them within the systems analysis framework of Chapter 9. Database designers who fail to understand that the database is part of a larger system are likely to overlook important design requirements. In fact, Chapter 9, Database Design, provides the map for the advanced database design developed in Appendixes B and C. Within the larger systems framework, we can also explore issues such as transaction management and concurrency control (Chapter 10), distributed da- tabase management systems (Chapter 12), business in- telligence and data warehouses (Chapter 13), database connectivity and web technologies (Chapter 15), and database administration and security (Chapter 16).
PART 1
Database Concepts
1 Database Systems 2 Data Models
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Preface xv

Database Design
The first item in the book’s subtitle is Design, and our examination of database design is comprehensive. For example, Chapters 1 and 2 examine the development and future of databases and data models, and illustrate the need for design. Chapter 3 examines the details of the relational database model; Chapter 4 provides ex- tensive, in-depth, and practical database design cover- age; and Chapter 5 explores advanced database design topics. Chapter 6 is devoted to critical normalization issues that affect database efficiency and effectiveness. Chapter 9 examines database design within the systems framework and maps the activities required to success- fully design and implement the complex, real-world database developed in Appendixes B and C. Appendix A, Designing Databases with Visio Professional: A Tu- torial, provides a good introductory tutorial for the use of a database design tool.
Because database design is affected by real-world transactions, the way data is distributed, and ever-in- creasing information requirements, we examine major database features that must be supported in current-gen- eration databases and models. For example, Chapter 10, Transaction Management and Concurrency Control, focuses on the characteristics of database transactions and how they affect database integrity and consistency. Chapter 11, Database Performance Tuning and Query
Optimization, illustrates the need for query efficiency in a world that routinely generates and uses tera- byte-size databases and tables with millions of records. Chapter 12, Distributed Database Management Systems, focuses on data distribution, replication, and allocation. In Chapter 13, Business Intelligence and Data Warehouses, we explore the characteristics of databases that are used in decision support and online analytical processing. Chapter 14, Big Data Analytics and NoSQL, explores the challenges of designing nonrelational databases to use vast global stores of unstructured data. Chapter 15, Database Connectivity and Web Technologies, covers the basic database connectivity issues in a web-based data world, development of web-based database front ends, and emerging cloud-based services.
Implementation
The second portion of the subtitle is Implementation. We use Structured Query Language (SQL) in Chap- ters 7 and 8 to show how relational databases are implemented and managed. Appendix M, Microsoft Access Tutorial, provides a quick but comprehensive guide to implementing an MS Access database. Ap- pendixes B and C demonstrate the design of a da- tabase that was fully implemented; these appendix- es illustrate a wide range of implementation issues. We had to deal with conflicting design goals: design elegance, information requirements, and operation- al speed. Therefore, we carefully audited the initial design in Appendix B to check its ability to meet end-user needs and establish appropriate implemen- tation protocols. The result of this audit yielded the fi- nal design developed in Appendix C. While relational databases are still the appropriate database technolo- gy to use in the vast majority of situations, Big Data issues have created an environment in which special
Chapter 9
Database Design
In this chapter, you will learn:
• That a sound database design is the foundation for a successful information system, and that the database design must reflect the information system of which the database is a part
• That successful information systems are developed within a framework known as the Systems Development Life Cycle (SDLC)
• That within the information system, the most successful databases are subject to frequent evaluation and revision within a framework known as the Database Life Cycle (DBLC)
• How to conduct evaluation and revision within the SDLC and DBLC frameworks
• About database design strategies: top-down versus bottom-up design and centralized versus
decentralized design
Preview
Databases are a part of a larger picture called an information system. Database designs that fail to recognize this fact are not likely to be successful. Database designers must rec- ognize that the database is a critical means to an end rather than an end in itself. Managers want the database to serve their management needs, but too many databases seem to force managers to alter their routines to fit the database requirements.
Information systems don’t just happen; they are the product of a carefully staged devel- opment process. Systems analysis is used to determine the need for an information system and to establish its limits. Within systems analysis, the actual information system is cre- ated through a process known as systems development.
The creation and evolution of information systems follows an iterative pattern called the Systems Development Life Cycle (SDLC), which is a continuous process of creation, maintenance, enhancement, and replacement of the information system. A similar cycle applies to databases: the database is created, maintained, enhanced, and eventually replaced. The Database Life Cycle (DBLC) is carefully traced in this chapter, and is shown in the context of the larger Systems Development Life Cycle.
At the end of the chapter, you will be introduced to some classical approaches to data- base design: top-down versus bottom-up and centralized versus decentralized.
Note
Data Files Available on cengagebrain.com
Because it is purely conceptual, this chapter does not reference any data files.
PART 3
Advanced Design and Implementation
7 Introduction to Structured Query Language (SQL) 8 Advanced SQL
9 Database Design
xvi Preface
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requirements can call for the use of new, nonrela- tional technologies. Chapter 14, Big Data Analyt- ics and NoSQL, describes the types of data that are appropriate for these new technologies and the ar- ray of options available in these special cases. The special issues encountered in an Internet database environment are addressed in Chapter 15, Database Connectivity and Web Technologies, and in Appen- dix J, Web Database Development with ColdFusion.
Management
The final portion of the subtitle is Management. We deal with database management issues in Chapter 10, Transaction Management and Concurrency Control; Chapter 12, Distributed Database Man- agement Systems; and Chapter 16, Database Ad- ministration and Security. Chapter 11, Database Performance Tuning and Query Optimization, is a valuable resource that illustrates how a DBMS man- ages data retrieval. In addition, Appendix N, Cre- ating a New Database Using Oracle 12c, walks you through the process of setting up a new database.
Teaching Database: A Matter of
Focus
Given the wealth of detailed coverage, instructors can “mix and match” chapters to produce the desired coverage. Depending on where database courses fit into the curriculum, instructors may choose to emphasize database design or database management. (See Figure 1.)
The hands-on nature of database design lends itself particularly well to class projects in which students use instructor-selected software to prototype a system that they design for the end user. Several end-of-chapter problems are sufficiently complex to serve as projects, or an instructor may work with local businesses to give students hands-on experience. Note that some elements of the database design track are also found in the database management track, because it is difficult to manage database technologies that are not well understood.
The options shown in Figure 1 serve only as a starting point. Naturally, instructors will tailor their coverage based on their specific course requirements. For example, an instructor may decide to make Appendix I an outside reading assignment and make Appendix A a self-taught tutori- al, and then use that time to cover client/server systems or object-oriented databases. The latter choice would serve as a gateway to UML coverage.
PART 6
Database Administration
16 Database Administration and Security
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Preface xvii

FIGURE 1
Core Coverage
(1) Database Systems
(2) Data Models
(3) The Relational Database Model
(4) Entity Relationship (ER) Modeling
(6) Normalization of Database Tables
(7) Introduction to Structured Query Language (SQL)
Database Design and Implementation Focus Database Management Focus
Supplementary Reading Supplementary Reading
(5) Advanced Data Modeling
(8) Advanced SQL
(9) Database Design
(A) Designing Databases with Visio Professional (D) Converting an ER Model into a Database Structure (E) Comparison of ER Model Notations
(H) Unified Modeling Language (UML)
(14) Big Data Analytics and NoSQL
(15) Database Connectivity and Web Technologies
(10) Transaction Management and Concurrency Control (11) Database Performance Tuning and Query Optimization (12) Distributed Database Management Systems
(13) Business Intelligence and Data Warehouses
(15) Database Connectivity and Web Technologies (16) Database Administration and Security
(F) Client/Server Systems
(G) Object Oriented Databases
(B) The University Lab: Conceptual Design
(C) The University Lab: Conceptual Design Verification, Logical Design, and Implementation
(M) Microsoft Access Tutorial
(J) Web Database Development with ColdFusion
(K) The Hierarchical Database Model
(L) The Network Database Model
(9) Database Design
(M) Microsoft Access Tutorial
(N) Creating a New Database Using Oracle 12c (O) Data Warehouse Implementation Factors (I) Databases in Electronic Commerce
(J) Web Database Development with ColdFusion
xviii Preface
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Text Features
Online Content
All of the databases used to illustrate the material in this chapter (see the Data Files list at the beginning of the chapter) are available at www.cengagebrain. com. The database names match the data- base names shown in the figures.
Note
re 3.1, you can draw the following conclu-
Online Content boxes
draw attention to material
a two-dimensional structure composed of
at www.cengagebrain.com for this text and provide ideas for incorporating
ribes a single entity occurrence within the
this content into the
by the STUDENT table.) For example, row
course.
d Walter H. Oblonski. Given the table con-
he attribute’s characteristics. For example, olumn contains only STU_GPA entries for ed according to its format and func- erent data types, most support at
Notes highlights important facts about the concepts introduced in the chapter.
A variety of four-color figures, including
ER models and implementations, tables, and illustra- tions, clearly illustrate difficult concepts.
A null is no value at all. It does not mean a zero or a space. A null is created when you press the Enter key or the Tab key to move to the next entry without making an entry of any kind. Pressing the Spacebar creates a blank (or a space).
FIGURE 1.11 ILLUSTRATING METADATA WITH MICROSOFT SQL SERVER EXPRESS
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Text Features xix
u e c
e

Summary
• An information system is designed to help transform data into information and to manage both data and information. Thus, the database is a very important part of the information system. Systems analysis is the process that establishes the need for an information system and its extent. Systems development is the process of creating an information system.
A robust Summary at the end of each chapter ties together the major concepts and serves
as a quick review for students.
An alphabetic list of Key Terms summarizes important terms.
Key Terms
bottom-up design
boundaries
centralized design
clustered tables
cohesivity
computer-aided software engineering (CASE)
conceptual design database development database fragment
Database Life Cycle (DBLC) database role decentralized design description of operations differential backup
full backup information system logical design minimal data rule module
module coupling
physical design
scope
systems analysis
systems development
Systems Development Life Cycle (SDLC)
top-down design transaction log backup virtualization
Review Questions
1. What is an information system? What is its purpose?
2. How do systems analysis and systems development fit into a discussion about infor- mation systems?
3. What does the acronym SDLC mean, and what does an SDLC portray?
4. What does the acronym DBLC mean, and what does a DBLC portray?
5. Discuss the distinction between centralized and decentralized conceptual database design.
Problems
In the following exercises, you will set up database connectivity using MS Excel.
1. Use MS Excel to connect to the Ch02_InsureCo MS Access database using ODBC, and retrieve all of the AGENTs.
2. Use MS Excel to connect to the Ch02_InsureCo MS Access database using ODBC, and retrieve all of the CUSTOMERs.
xx Text Features
Review Questions
challenge students to apply the skills learned in each chapter.
Problems become progressively more complex as students draw on the lessons learned from the completion of preceding problems.
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Additional Features
MindTap® combines learning tools—such as readings, multimedia, activities, and assessments— into a singular learning path that guides students through the course. You’ll find a full ebook as well as a robust set of auto-gradable homework problems. Multiple-choice homework questions developed from the end-of-chapter review questions confirm students’ understanding of core concepts and key terms. Higher-level assignments enable students to practice database design concepts in an automated environment, and chapter quizzes help prepare students for exams. Students will also benefit from the chapter-opening videos created by the authors, as well as study tools such as crossword puzzles and key-term flashcards.
Appendixes
Fifteen online appendixes provide additional material on a variety of important areas, such as using Microsoft® Visio® and Microsoft® Access®, ER model notations, UML, object-oriented da- tabases, databases and electronic commerce, and Adobe® ColdFusion®.
Database, SQL Script, and ColdFusion Files
The online materials for this book include all of the database structures and table contents used in the text. For students using Oracle®, MySQL, and Microsoft SQL ServerTM, SQL scripts are included to help students create and load all tables used in the SQL chapters (7 and 8). In addition, all Cold- Fusion scripts used to develop the web interfaces in Appendix J are included.
Instructor Resources
Database Systems: Design, Implementation, and Management, Twelfth Edition, includes teaching tools to support instructors in the classroom. The ancillary material that accompanies the text- book is listed below. They are available on the web at www.cengagebrain.com.
Instructor’s Manual
The authors have created this manual to help instructors make their classes informative and inter- esting. Because the authors tackle so many problems in depth, instructors will find the Instructor’s Manual especially useful. The details of the design solution process are shown in the Instructor’s Manual, as well as notes about alternative approaches that may be used to solve a particular problem.
SQL Script Files for Instructors
The authors have provided teacher’s SQL script files to allow instructors to cut and paste the SQL code into the SQL windows. (Scripts are provided for Oracle, MySQL, and MS SQL Server.) The SQL scripts, which have all been tested by Cengage Learning, are a major convenience for instructors. You won’t have to type in the SQL commands, and the use of the scripts eliminates typographical errors that are sometimes difficult to trace.
ColdFusion Files for Instructors
The ColdFusion web development solutions are provided. Instructors have access to a menu- driven system that allows teachers to show the code as well as its execution.
MindTap® for Database Systems 12e
MindTap® is designed to be fully integrated with any Learning Management System and can be used as a stand-alone product or in conjunction with a print textbook.
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Additional Features xxi

Databases
For many chapters, Microsoft® Access® instructor databases are available that include features not found in the student databases. For example, the databases that accompany Chapters 7 and 8 include many of the queries that produce the problem solutions. Other Access databases, such as the ones that accompany Chapters 3, 4, 5, and 6, include implementations of the design problem solutions to allow instructors to illustrate the effect of design decisions. In addition, instructors have access to all the script files for Oracle, MySQL, and MS SQL Server so that all the databases and their tables can be converted easily and precisely.
Cengage Learning Testing Powered by Cognero
A flexible, online system that allows you to:
• Author, edit, and manage test bank content from multiple Cengage Learning solutions
• Create multiple test versions in an instant
• Deliver tests from your LMS, your classroom, or wherever you want Start right away!
Cengage Learning Testing Powered by Cognero works on any operating system or browser.
• No special installs or downloads needed
• Create tests from school, home, the coffee shop—anywhere with Internet access What will you find?
• Simplicity at every step. A desktop-inspired interface features drop-down menus and familiar, intuitive tools that take you through content creation and management with ease.
• Full-featured test generator. Create ideal assessments with your choice of 15 question types (including true/false, multiple-choice, opinion scale/Likert, and essay). Multi-language sup- port, an equation editor, and unlimited metadata help ensure your tests are complete and compliant.
• Cross-compatible capability. Import and export content into other systems.
PowerPoint® Presentations
Microsoft PowerPoint slides are included for each chapter. Instructors can use the slides in a vari- ety of ways—for example, as teaching aids during classroom presentations or as printed handouts for classroom distribution. Instructors can modify these slides or include slides of their own for additional topics introduced to the class.
Figure Files
Figure files for solutions are presented in the Instructor’s Manual to allow instructors to create their own presentations. Instructors can also manipulate these files to meet their particular needs.
xxii Additional Features
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Acknowledgments
Regardless of how many editions of this book are published, they will always rest on the solid foundation created by the first edition. We remain convinced that our work has become successful because that first edition was guided by Frank Ruggirello, a former Wadsworth senior editor and publisher. Aside from guiding the book’s development, Frank also managed to solicit the great Peter Keen’s evaluation (thankfully favorable) and subsequently convinced Peter Keen to write the foreword for the first edition. Although we sometimes found Frank to be an especially demanding taskmaster, we also found him to be a superb professional and a fine friend. We suspect Frank will still see his fingerprints all over our current work. Many thanks.
A difficult task in rewriting a book is deciding what new approaches, topical coverage, and changes to depth of coverage are appropriate for a product that has successfully weathered the test of the marketplace. The comments and suggestions made by the book’s adopters, students, and reviewers play a major role in deciding what coverage is desirable and how that coverage is to be treated.
Some adopters became extraordinary reviewers, providing incredibly detailed and well-rea- soned critiques even as they praised the book’s coverage and style. Dr. David Hatherly, a superb database professional who is a senior lecturer in the School of Information Technology, Charles Sturt University–Mitchell, Bathhurst, Australia, made sure that we knew precisely what issues led to his critiques. Even better for us, he provided the suggestions that made it much easier for us to improve the topical coverage in earlier editions. All of his help was given freely and without prompting on our part. His efforts are much appreciated, and our thanks are heartfelt.
We also owe a debt of gratitude to Professor Emil T. Cipolla, who teaches at St. Mary College. Professor Cipolla’s wealth of IBM experience turned out to be a valuable resource when we tack- led the embedded SQL coverage in Chapter 8.
Every technical book receives careful scrutiny by several groups of reviewers selected by the publisher. We were fortunate to face the scrutiny of reviewers who were superbly qualified to of- fer their critiques, comments, and suggestions—many of which strengthened this edition. While holding them blameless for any remaining shortcomings, we owe these reviewers many thanks for their contributions:
Mubarak Banisaklher, Bethune Cookman University
David Bell, Pacific Union College
Yurii Boreisha, Minnesota State University, Moorhead
Laurie Crawford, Franklin University
Mel Goetting, Shawnee State University
Jeff Guan, University of Louisville
William Hochstettler, Franklin University
Laurene Hutchinson, Louisiana State University, Baton Rouge
Nitin Kale, University of Southern California, Los Angeles
Gerald Karush, Southern New Hampshire University
Michael Kelly, Community College of Rhode Island
Timothy Koets, Grand Rapids Community College
Klara Nelson, The University of Tampa
Chiso Okafor, Roxbury Community College
Brandon Olson, The College of St. Scholastica
James Reneau, Shawnee State University
Julio Rivera, University of Alabama at Birmingham
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Acknowledgments xxiii

xxiv Acknowledgments
Ruth Robins, University of Houston, Downtown
Samuel Sambasivam, Azusa Pacific University
Paul Seibert, North Greenville University
Ronghua Shan, Dakota State University
Andrew Smith, Marian University
Antonis Stylianou, University of North Carolina, Charlotte
Brian West, University of Louisiana at Lafayette
Nathan White, McKendree University
In some respects, writing books resembles building construction: When 90 percent of the work seems done, 90 percent of the work remains to be done. Fortunately for us, we had a great team on our side.
• We are deeply indebted to Deb Kaufmann for her help and guidance. Deb has been everything we could have hoped for in a development editor and more. Deb has been our editor for al- most all the editions of this book, and the quality of her work shows in the attention to detail and the cohesiveness and writing style of the material in this book.
• After writing so many books and twelve editions of this book, we know just how difficult it can be to transform the authors’ work into an attractive product. The production team, both at Cengage Learning (Nadia Saloom) and Cenveo Publisher Services (Saravanakumar Dharman), have done an excellent job.
• We also owe Jennifer King and Ted Knight, our Content Developers, special thanks for their ability to guide this book to a successful conclusion.
We also thank our students for their comments and suggestions. They are the reason for writing this book in the first place. One comment stands out in particular: “I majored in systems for four years, and I finally discovered why when I took your course.” And one of our favorite comments by a former student was triggered by a question about the challenges created by a real-world in- formation systems job: “Doc, it’s just like class, only easier. You really prepared me well. Thanks!”
Special thanks go to a very unique and charismatic gentleman. For over 20 years, Peter Rob has been the driving force behind the creation and evolution of this book. This book originated as a product of his drive and dedication to excellence. For over 22 years, he was the voice of Database Systems and the driving force behind its advancement. We wish him peace in his retirement, time with his loved ones, and luck on his many projects.
Last, and certainly not least, we thank our families for their solid support at home. They gra- ciously accepted the fact that during more than a year’s worth of rewriting, there would be no free weekends, rare free nights, and even rarer free days. We owe you much, and the dedications we wrote are but a small reflection of the important space you occupy in our hearts.
Carlos Coronel and Steven Morris
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PART 1
Database Concepts
1 Database Systems 2 Data Models
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Chapter 1 Database Systems
In this chapter, you will learn:
• The difference between data and information
• What a database is, the various types of databases, and why they are valuable assets for
decision making
• The importance of database design
• How modern databases evolved from file systems
• About flaws in file system data management
• The main components of the database system
• The main functions of a database management system (DBMS)
Preview
Organizations use data to keep track of their day-to-day operations. Such data is used to generate information, which in turn is the basis for good decisions. Data is likely to be managed most efficiently when it is stored in a database. Databases are involved in almost all facets and activities of our daily lives: from school, to work, to medical care, govern- ment, nonprofit organizations, and houses of worship. In this chapter, you will learn what a database is, what it does, and why it yields better results than other data management methods. You will also learn about various types of databases and why database design is so important.
Databases evolved from computer file systems. Although file system data management is now largely outmoded, understanding the characteristics of file systems is important because file systems are the source of serious data management limitations. In this chap- ter, you will also learn how the database system approach helps eliminate most of the shortcomings of file system data management.
Data Files and Available Formats
CH01_Text P P P P CH01_Problems P P P P CH01_Design_Example P P P P
Data Files Available on cengagebrain.com
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

1-1 Why Databases?
So, why do we need databases? In today’s world, data is ubiquitous (abundant, global, every- where) and pervasive (unescapable, prevalent, persistent). From birth to death, we generate and consume data. The trail of data starts with the birth certificate and continues all the way to a death certificate (and beyond!). In between, each individual produces and consumes enormous amounts of data. As you will see in this book, databases are the best way to store and manage data. Databases make data persistent and shareable in a secure way. As you look at Figure 1.1, can you identify some of the data generated by your own daily activities?
FIGURE 1.1 THE PERVASIVE NATURE OF DATABASES
Before leaving for work, Susan checks her Facebook and Twitter accounts
Where is the data about the friends and groups stored?
Where are the “likes” stored and what would they be used for?
Users Friends Posts
On her lunch break, she picks up her prescription at the pharmacy
Where is the pharmacy inventory data stored?
What data about each product will be in the inventory data?
What data is kept about each customer and where is it stored?
Products Sales Customers
After work, Susan goes to the grocery store
Where is the product data stored?
Is the product quantity in stock updated at checkout?
Does she pay with a credit card?
Products Sales Customers
At night, she plans for a trip and buys airline tickets and hotel reservations online
Where does the online travel website get the airline and hotel data from?
What customer data would be kept by the website?
Where would the customer data be stored?
Flights
Hotels Customers
Then she makes a few online purchases
www.abc.com
Where are the product and stock data stored?
Where does the system get the data to generate product “recommendations” to the customer?
Where would credit card information be stored?
Products Sales Customers
A Day In Susan’s Life
See how many databases she interacts with each day
Data is not only ubiquitous and pervasive, it is essential for organizations to survive and prosper. Imagine trying to operate a business without knowing who your customers are, what products you are selling, who is working for you, who owes you money, and to whom you owe money. All businesses have to keep this type of data and much more. Just as important, they must have that data available to decision makers when necessary. It can be argued that the ultimate purpose of all business information systems is to help businesses use information as an organizational resource. At the heart of all of these systems are the collection, storage, aggregation, manipulation, dissemination, and man- agement of data.
Depending on the type of information system and the characteristics of the busi- ness, this data could vary from a few megabytes on just one or two topics to terabytes covering hundreds of topics within the business’s internal and external environment.
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Chapter 1
Database Systems 3
COCA

4 Part 1
Database Concepts
Telecommunications companies, such as Sprint and AT&T, are known to have systems that keep data on trillions of phone calls, with new data being added to the system at speeds up to 70,000 calls per second! Not only do these companies have to store and man- age immense collections of data, they have to be able to find any given fact in that data quickly. Consider the case of Internet search staple Google. While Google is reluctant to disclose many details about its data storage specifications, it is estimated that the company responds to over 91 million searches per day across a collection of data that is several terabytes in size. Impressively, the results of these searches are available almost instantly.
How can these businesses process this much data? How can they store it all, and then quickly retrieve just the facts that decision makers want to know, just when they want to know it? The answer is that they use databases. Databases, as explained in detail through- out this book, are specialized structures that allow computer-based systems to store, manage, and retrieve data very quickly. Virtually all modern business systems rely on databases. Therefore, a good understanding of how these structures are created and their proper use is vital for any information systems professional. Even if your career does not take you down the amazing path of database design and development, databases will be a key component of the systems that you use. In any case, you will probably make decisions in your career based on information generated from data. Thus, it is important that you know the difference between data and information.
1-2 Data versus Information
To understand what drives database design, you must understand the difference between data and information. Data consists of raw facts. The word raw indicates that the facts have not yet been processed to reveal their meaning. For example, suppose that a uni- versity tracks data on faculty members for reporting to accrediting bodies. To get the data for each faculty member into the database, you would provide a screen to allow for convenient data entry, complete with drop-down lists, combo boxes, option buttons, and other data-entry validation controls. Figure 1.2(a) shows a simple data-entry form from a software package named Sedona. When the data is entered into the form and saved, it is placed in the underlying database as raw data, as shown in Figure 1.2(b). Although you now have the facts in hand, they are not particularly useful in this format. Reading through hundreds of rows of data for faculty members does not provide much insight into the overall makeup of the faculty. Therefore, you transform the raw data into a data summary like the one shown in Figure 1.2(c). Now you can get quick answers to questions such as “What percentage of the faculty in the Information Systems (INFS) department are adjuncts?” In this case, you can quickly determine that 20 percent of the INFS faculty members are adjunct faculty. Because graphics can enhance your ability to quickly extract meaning from data, you show the data summary pie chart in Figure 1.2(d).
Information is the result of processing raw data to reveal its meaning. Data process- ing can be as simple as organizing data to reveal patterns or as complex as making fore- casts or drawing inferences using statistical modeling. To reveal meaning, information requires context. For example, an average temperature reading of 105 degrees does not mean much unless you also know its context: Is this reading in degrees Fahrenheit or Celsius? Is this a machine temperature, a body temperature, or an outside air tempera- ture? Information can be used as the foundation for decision making. For example, the data summary for the faculty can provide accrediting bodies with insights that are useful in determining whether to renew accreditation for the university.
Keep in mind that raw data must be properly formatted for storage, processing, and presentation. For example, dates might be stored in Julian calendar formats within the data- base, but displayed in a variety of formats, such as day-month-year or month/day/year, for
arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
data
Raw facts, or facts that have not yet been processed to reveal their meaning to the end user.
information
The result of processing raw data to reveal its meaning. Information consists of transformed data and facilitates decision making.
Copyright 2017 Cengage Le Editorial review has deemed that

different purposes. Respondents’ yes/no responses might need to be converted to a Y/N or 0/1 format for data storage. More complex formatting is required when working with com- plex data types, such as sounds, videos, or images.
In this “information age,” production of accurate, relevant, and timely information is the key to good decision making. In turn, good decision making is the key to business survival in a global market. We are now said to be entering the “knowledge age.”1
Data is the foundation of information, which is the bedrock of knowledge—that is, the body of information and facts about a specific subject. Knowledge implies familiarity, awareness, and understanding of information as it applies to an envi- ronment. A key characteristic of knowledge is that “new” knowledge can be derived from “old” knowledge.
Let’s summarize some key points:
• Data constitutes the building blocks of information.
• Information is produced by processing data.
• Information is used to reveal the meaning of data.
• Accurate, relevant, and timely information is the key to good decision making.
• Good decision making is the key to organizational survival in a global environment.
1 Peter Drucker coined the phrase “knowledge worker” in 1959 in his book Landmarks of Tomorrow. In 1994, Esther Dyson, George Keyworth, and Dr. Alvin Toffler introduced the concept of the “knowledge age.”
knowledge
The body of information and facts about a specific subject. Knowledge implies familiarity, awareness, and understanding of information as it applies to an environment. A key characteristic is that new knowledge can
be derived from old knowledge.
Chapter 1
Database Systems 5
FIGURE 1.2 TRANSFORMING RAW DATA INTO INFORMATION
a) Data entry screen b) Raw data
c) Information in summary format
d) Information in graphical format
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6 Part 1
Database Concepts
Timely and useful information requires accurate data. Such data must be properly gen- erated and stored in a format that is easy to access and process. In addition, like any basic resource, the data environment must be managed carefully. Data management is a disci- pline that focuses on the proper generation, storage, and retrieval of data. Given the crucial role that data plays, it should not surprise you that data management is a core activity for any business, government agency, service organization, or charity.
1-3 Introducing the Database
Efficient data management typically requires the use of a computer database. A database is a
shared, integrated computer structure that stores a collection of the following:
• End-user data—that is, raw facts of interest to the end user
• Metadata, or data about data, through which the end-user data is integrated and managed
The metadata describes the data characteristics and the set of relationships that links the data found within the database. For example, the metadata component stores infor- mation such as the name of each data element, the type of values (numeric, dates, or text) stored on each data element, and whether the data element can be left empty. The meta- data provides information that complements and expands the value and use of the data. In short, metadata presents a more complete picture of the data in the database. Given the characteristics of metadata, you might hear a database described as a “collection of self-describing data.”
A database management system (DBMS) is a collection of programs that manages the database structure and controls access to the data stored in the database. In a sense, a database resembles a very well-organized electronic filing cabinet in which powerful software (the DBMS) helps manage the cabinet’s contents.
1-3a Role and Advantages of the DBMS
The DBMS serves as the intermediary between the user and the database. The database structure itself is stored as a collection of files, and the only way to access the data in those files is through the DBMS. Figure 1.3 emphasizes the point that the DBMS presents the end user (or application program) with a single, integrated view of the data in the database. The DBMS receives all application requests and translates them into the com- plex operations required to fulfill those requests. The DBMS hides much of the database’s internal complexity from the application programs and users. The application program might be written by a programmer using a programming language, such as Visual Basic. NET, Java, or C#, or it might be created through a DBMS utility program.
Having a DBMS between the end user’s applications and the database offers some important advantages. First, the DBMS enables the data in the database to be shared among multiple applications or users. Second, the DBMS integrates the many different users’ views of the data into a single all-encompassing data repository.
Because data is the crucial raw material from which information is derived, you must have a good method to manage such data. As you will discover in this book, the DBMS helps make data management more efficient and effective. In particular, a DBMS pro- vides these advantages:
• Improved data sharing. The DBMS helps create an environment in which end users have better access to more and better-managed data. Such access makes it possible for end users to respond quickly to changes in their environment.
arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
data management
A process that focuses on data collection, storage, and retrieval. Common data management functions include addition, deletion, modification, and listing.
database
A shared, integrated computer structure that houses a collection of related data. A database contains two types of data: end-user data (raw facts) and metadata.
metadata
Data about data; that is, data about data characteristics and relationships. See also data dictionary.
database management system (DBMS)
The collection of programs that manages the database structure and controls access to the data stored in the database.
Copyright 2017 Cengage Le Editorial review has deemed that

• Improved data security. The more users access the data, the greater the risks of data security breaches. Corporations invest considerable amounts of time, effort, and money to ensure that corporate data is used properly. A DBMS provides a framework for better enforcement of data privacy and security policies.
• Better data integration. Wider access to well-managed data promotes an inte- grated view of the organization’s operations and a clearer view of the big picture. It becomes much easier to see how actions in one segment of the company affect other segments.
• Minimized data inconsistency. Data inconsistency exists when different versions of the same data appears in different places. For example, data inconsistency exists when a company’s sales department stores a sales representative’s name as Bill Brown and the company’s personnel department stores that same person’s name as William G. Brown, or when the company’s regional sales office shows the price of a product as $45.95 and its national sales office shows the same product’s price as $43.95. The probability of data inconsistency is greatly reduced in a prop- erly designed database.
• Improved data access. The DBMS makes it possible to produce quick answers to ad hoc queries. From a database perspective, a query is a specific request issued to the DBMS for data manipulation—for example, to read or update the data. Simply put, a query is a question, and an ad hoc query is a spur-of-the-moment question. The DBMS sends back an answer (called the query result set) to the application. For example, when dealing with large amounts of sales data, end users might want quick answers to questions (ad hoc queries). Some examples include the following:
– What was the dollar volume of sales by product during the past six months?
– What is the sales bonus figure for each of our salespeople during the past three
months?
– How many of our customers have credit balances of $3,000 or more?
data inconsistency
A condition in which different versions of the same data yield different (inconsistent) results.
query
A question or task
asked by an end user
of a database in the form of SQL code. A specific request for data manipulation issued
by the end user or the application to the DBMS.
ad hoc query
A “spur-of-the-moment” question.
query result set
The collection of data rows returned by a query.
Chapter 1
Database Systems 7
FIGURE 1.3 THE DBMS MANAGES THE INTERACTION BETWEEN THE END USER AND THE DATABASE
End users
End users
Data
Application request
Database structure
Metadata
Customers Invoices Products
http://
DBMS (Database management system)
Single
View of data
Integrated
End-user data
Application request
Data
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8
Part 1
Database Concepts
data quality
A comprehensive approach to ensuring the accuracy, validity, and timeliness of data.
single-user database
A database that supports only one user at a time.
desktop database
A single-user database that runs on a personal computer.
multiuser database
A database that supports multiple concurrent users.
workgroup database
A multiuser database that usually supports fewer than 50 users or is used for a specific department in an organization.
enterprise database
The overall company data representation, which provides support for present and expected future needs.
centralized database
A database located at a single site.
distributed database
A logically related database that is stored in two or more physically independent sites.
cloud database
A database that
is created and maintained using cloud services, such as Microsoft Azure or Amazon AWS.
general-purpose database
A database that contains a wide variety of data used in multiple disciplines.
• Improved decision making. Better-managed data and improved data access make it possible to generate better-quality information, on which better decisions are based. The quality of the information generated depends on the quality of the underlying data. Data quality is a comprehensive approach to promoting the accu- racy, validity, and timeliness of the data. While the DBMS does not guarantee data quality, it provides a framework to facilitate data quality initiatives. Data quality concepts will be covered in more detail in Chapter 16, Database Administration and Security.
• Increased end-user productivity. The availability of data, combined with the tools that transform data into usable information, empowers end users to make quick, informed decisions that can make the difference between success and failure in the global economy.
The advantages of using a DBMS are not limited to the few just listed. In fact, you will discover many more advantages as you learn more about the technical details of databases and their proper design.
1-3b Types of Databases
A DBMS can be used to build many different types of databases. Each database stores a particular collection of data and is used for a specific purpose. Over the years, as tech- nology and innovative uses of databases have evolved, different methods have been used to classify databases. For example, databases can be classified by the number of users supported, where the data is located, the type of data stored, the intended data usage, and the degree to which the data is structured.
The number of users determines whether the database is classified as single user or multiuser. A single-user database supports only one user at a time. In other words, if user A is using the database, users B and C must wait until user A is done. A single-user database that runs on a personal computer is called a desktop database. In contrast, a multiuser database supports multiple users at the same time. When the multiuser database supports a relatively small number of users (usually fewer than 50) or a specific department within an organization, it is called a workgroup database. When the data- base is used by the entire organization and supports many users (more than 50, usually hundreds) across many departments, the database is known as an enterprise database.
Location might also be used to classify the database. For example, a database that supports data located at a single site is called a centralized database. A database that supports data distributed across several different sites is called a distributed database. The extent to which a database can be distributed and the way in which such distribution is managed are addressed in detail in Chapter 12, Distributed Data- base Management Systems.
Both centralized and decentralized (distributed) databases require a well-defined infrastructure (hardware, operating systems, network technologies, etc.) to implement and operate the database. Typically, the infrastructure is owned and maintained by the organization that creates and operates the database. But in recent years, the use of cloud databases has been growing in popularity. A cloud database is a database that is created and maintained using cloud data services, such as Microsoft Azure or Amazon AWS. These services, provided by third-party vendors, provide defined performance measures (data storage capacity, required throughput, and availability) for the database, but do not necessarily specify the underlying infrastructure to implement it. The data owner does not have to know, or be concerned about, what hardware and software is being used to support their database. The performance capabilities can be renegotiated with the
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cloud provider as the business demands on the database change. For example, during the 2012 presidential election in the United States, the Obama campaign used a cloud database hosted on infrastructure capabilities purchased from Amazon. The campaign did not have to buy, install, configure, or maintain any hardware, operating systems, or network devices. It simply purchased storage and processing capacity for its data and applications. As the demands on the database increased, additional processing and stor- age capabilities could be purchased as needed.
In some contexts, such as research environments, a popular way of classifying data- bases is according to the type of data stored in them. Using this criterion, databases are grouped into two categories: general-purpose and discipline-specific databases. General-purpose databases contain a wide variety of data used in multiple disci- plines—for example, a census database that contains general demographic data and the LexisNexis and ProQuest databases that contain newspaper, magazine, and journal articles for a variety of topics. Discipline-specific databases contain data focused on specific subject areas. The data in this type of database is used mainly for academic or research purposes within a small set of disciplines. Examples of discipline-specific databases include financial data stored in databases such as CompuStat or CRSP (Center for Research in Security Prices), geographic information system (GIS) data- bases that store geospatial and other related data, and medical databases that store confidential medical history data.
The most popular way of classifying databases today, however, is based on how they will be used and on the time sensitivity of the information gathered from them. For example, transactions such as product or service sales, payments, and supply purchases reflect critical day-to-day operations. Such transactions must be recorded accurately and immediately. A database that is designed primarily to support a com- pany’s day-to-day operations is classified as an operational database, also known as an online transaction processing (OLTP) database, transactional database, or production database. In contrast, an analytical database focuses primarily on storing historical data and business metrics used exclusively for tactical or stra- tegic decision making. Such analysis typically requires extensive “data massaging” (data manipulation) to produce information on which to base pricing decisions, sales forecasts, market strategies, and so on. Analytical databases allow the end user to perform advanced analysis of business data using sophisticated tools.
Typically, analytical databases comprise two main components: a data warehouse and an online analytical processing front end. The data warehouse is a specialized data- base that stores data in a format optimized for decision support. The data warehouse contains historical data obtained from the operational databases as well as data from other external sources. Online analytical processing (OLAP) is a set of tools that work together to provide an advanced data analysis environment for retrieving, processing, and modeling data from the data warehouse. In recent times, this area of database appli- cation has grown in importance and usage, to the point that it has evolved into its own discipline: business intelligence. The term business intelligence describes a compre- hensive approach to capture and process business data with the purpose of generating information to support business decision making. Chapter 13, Business Intelligence and Data Warehouses, covers this topic in detail.
Databases can also be classified to reflect the degree to which the data is structured. Unstructured data is data that exists in its original (raw) state—that is, in the format in which it was collected. Therefore, unstructured data exists in a format that does not lend itself to the processing that yields information. Structured data is the result of for- matting unstructured data to facilitate storage, use, and the generation of information. You apply structure (format) based on the type of processing that you intend to perform
discipline-specific database
A database that contains data focused on specific subject areas.
operational database
A database designed primarily to support a company’s day-to-day operations. Also known as a transactional database, OLTP database, or production database.
online transaction processing (OLTP) database
See operational database.
transactional database
See operational database.
production database
See operational database.
analytical database
A database focused primarily on storing historical data and business metrics used for tactical or strategic decision making.
data warehouse
A specialized database that stores historical and aggregated data in a format optimized for decision support.
online analytical processing (OLAP) A set of tools that provide advanced data analysis for retrieving, processing, and modeling data from the data warehouse.
business intelligence
A set of tools and processes used to capture, collect, integrate, store, and analyze data to support business decision making.
unstructured data
Data that exists in its original, raw state; that is, in the format in which it was collected.
Chapter 1
Database Systems 9
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10 Part 1
Database Concepts
on the data. Some data might not be ready (unstructured) for some types of processing, but they might be ready (structured) for other types of processing. For example, the data value 37890 might refer to a zip code, a sales value, or a product code. If this value rep- resents a zip code or a product code and is stored as text, you cannot perform mathemat- ical computations with it. On the other hand, if this value represents a sales transaction, it must be formatted as numeric.
To further illustrate the concept of structure, imagine a stack of printed paper invoices. If you want to merely store these invoices as images for future retrieval and display, you can scan them and save them in a graphic format. On the other hand, if you want to derive information such as monthly totals and average sales, such graphic storage would not be useful. Instead, you could store the invoice data in a (structured) spreadsheet format so that you can perform the requisite computations. Actually, most data you encounter is best classified as semistructured. Semistructured data has already been processed to some extent. For example, if you look at a typical webpage, the data is presented in a prearranged format to convey some information. The data- base types mentioned thus far focus on the storage and management of highly struc- tured data. However, corporations are not limited to the use of structured data. They also use semistructured and unstructured data. Just think of the valuable information that can be found on company emails, memos, and documents such as procedures, rules, and webpages. Unstructured and semistructured data storage and management needs are being addressed through a new generation of databases known as XML data- bases. Extensible Markup Language (XML) is a special language used to represent and manipulate data elements in a textual format. An XML database supports the storage and management of semistructured XML data.
Table 1.1 compares the features of several well-known database management systems.
structured data
Data that has been formatted to facilitate storage, use, and information generation.
semistructured data
Data that has already been processed to some extent.
Extensible Markup Language (XML)
A metalanguage
used to represent
and manipulate data elements. Unlike other markup languages, XML permits the manipulation of a document’s data elements.
TABLE 1.1
TYPES OF DATABASES
PRODUCT
NUMBER OF USERS
DATA LOCATION
DATA USAGE
XML
SINGLE USER
MULTIUSER
CENTRALIZED
DISTRIBUTED
OPERATIONAL
ANALYTICAL
WORKGROUP
ENTERPRISE
MS Access
X
X
X
X
MS SQL Server
X3
X
X
X
X
X
X
X
IBM DB2
X3
X
X
X
X
X
X
X
MySQL
X
X
X
X
X
X
X
X
Oracle RDBMS
X3
X
X
X
X
X
X
X
With the emergence of the World Wide Web and Internet-based technologies as the basis for the new “social media” generation, great amounts of data are being stored and analyzed. Social media refers to web and mobile technologies that enable “any- where, anytime, always on” human interactions. Websites such as Google, Facebook, Twitter, and LinkedIn capture vast amounts of data about end users and consumers. This data grows exponentially and requires the use of specialized database systems. For example, as of 2015, over 500 million tweets were posted every day on Twitter, and that number continues to grow. As a result, the MySQL database Twitter was using to store user content was frequently overloaded by demand.2,3 Facebook faces
2 Vendor offers single-user/personal DBMS version. 3 www.internetlivestats.com/twitter-statistics/
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
XML database
A database system that stores and manages semistructured XML data.
social media
Web and mobile technologies that enable “anywhere, anytime, always on” human interactions.
Copyright 2017 Cengage Lea Editorial review has deemed that

similar challenges. With over 500 terabytes of data coming in each day, it stores over 100 petabytes of data in a single data storage file system. From this data, its database scans over 200 terabytes of data each hour to process user actions, including status updates, picture requests, and billions of “Like” actions.4 Over the past few years, this new breed of specialized database has grown in sophistication and widespread usage. Currently, this new type of database is known as a NoSQL database. The term NoSQL (Not only SQL) is generally used to describe a new generation of database management systems that is not based on the traditional relational database model. NoSQL databases are designed to handle the unprecedented volume of data, variety of data types and structures, and velocity of data operations that are characteristic of these new business requirements. You will learn more about this type of system in Chapter 2, Data Models.
This section briefly mentioned the many different types of databases. As you learned earlier, a database is a computer structure that houses and manages end-user data. One of the first tasks of a database professional is to ensure that end-user data is properly structured to derive valid and timely information. For this, good database design is essential.
1-4 Why Database Design is Important
A problem that has evolved with the use of personal productivity tools such as spread- sheets and desktop database programs is that users typically lack proper data-modeling and database design skills. People naturally have a “narrow” view of the data in their environment. For example, consider a student’s class schedule. The sched- ule probably contains the student’s identification number and name, class code, class description, class credit hours, class instructor name, class meeting days and times, and class room number. In the mind of the student, these various data items compose a single unit. If a student organization wanted to keep a record of the schedules of its members, an end user might make a spreadsheet to store the schedule information. Even if the student makes a foray into the realm of desktop databases, he or she is likely to create a structure composed of a single table that mimics his or her view of the schedule data. As you will learn in the coming chapters, translating this type of narrow view of data into a single two-dimensional table structure is a poor database design choice.
Database design refers to the activities that focus on the design of the database structure that will be used to store and manage end-user data. A database that meets all user requirements does not just happen; its structure must be designed carefully. In fact, database design is such a crucial aspect of working with databases that most of this book is dedicated to the development of good database design techniques. Even a good DBMS will perform poorly with a badly designed database.
Data is one of an organization’s most valuable assets. Data on customers, employees, orders, and receipts is all vital to the existence of a company. Tracking key growth and performance indicators are also vital to strategic and tactical plans to ensure future suc- cess; therefore, an organization’s data must not be handled lightly or carelessly. Thorough planning to ensure that data is properly used and leveraged to give the company the most benefit is just as important as proper financial planning to ensure that the company gets the best use from its financial resources.
4 Josh Constine, “How big is Facebook’s data? 2.5 billion pieces of content and 500+ terabytes of data ingested every day,” Tech Crunch, August 22, 2012, http://techcrunch.com/2012/08/22/how-big-is-facebooks-data-2-5- billion-pieces-of-content-and-500-terabytes-ingested-every-day/
NoSQL
A new generation of database management systems that is not based on the traditional relational database model.
database design
The process that yields the description of the database structure
and determines the database components. The second phase of the Database Life Cycle.
Chapter 1
Database Systems 11
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12 Part 1
Database Concepts
Because current-generation DBMSs are easy to use, an unfortunate side effect is that many computer-savvy business users gain a false sense of confidence in their ability to build a functional database. These users can effectively navigate the creation of database objects, but without the proper understanding of database design, they tend to produce flawed, overly simplified structures that prevent the system from correctly storing data that corresponds to business realities, which produces incom- plete or erroneous results when the data is retrieved. Consider the data shown in Figure 1.4, which illustrates the efforts of an organization to keep records about its employees and their skills. Some employees have not passed a certification test in any skill, while others have been certified in several skills. Some certified skills are shared by several employees, while other skills have no employees that hold those certifications.
FIGURE 1.4 EMPLOYEE SKILLS CERTIFICATION IN A POOR DESIGN
Why are there blanks in rows 9 and 10?
How to produce an alphabetical listing of employees?
How to count how many employees are certified in Basic Database Manipulation?
Is Basic Database Manipulation the same as Basic DB Manipulation?
What if an employee acquires a fourth certification?
Do we add
Based on this storage of the data, notice the following problems:
• It would be difficult, if not impossible, to produce an alphabetical listing of employees based on their last names.
• To determine how many employees are certified in Basic Database Manipula- tion, you would need a program that counts the number of those certifications recorded in Skill1 and places it in a variable. Then the count of those certifications in Skill2 could be calculated and added to the variable. Finally, the count of those certifications in Skill3 could be calculated and added to the variable to produce the total.
• If you redundantly store the name of a skill with each employee who is certified in that skill, you run the risk of spelling the name differently for different employees. For example, the skill Basic Database Manipulation is also entered as Basic DB Manipula- tion for at least one employee in Figure 1.4, which makes it difficult to get an accurate count of employees who have the certification.
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another column?

• The structure of the database will have to be changed by adding more columns to the table when an employee is certified in a fourth skill. It will have to be modified again if an employee is certified in a fifth skill.
Contrast this poor design with that shown in Figure 1.5, where the design has been improved by decomposing the data into three related tables. These tables contain all of the same data that was represented in Figure 1.4, but the tables are structured so that you can easily manipulate the data to view it in different ways and answer simple questions.
With the improved structure in Figure 1.5, you can use simple commands in a standard data manipulation language to do the following:
• Produce an alphabetical listing of employees by last name: SELECT * FROM EMPLOYEE ORDER BY EMPLOYEE_LNAME;
• Determine how many employees are certified in Basic Database Manipulation:
SELECT Count(*)
FROM SKILL JOIN CERTIFIED ON SKILL.SKILL_ID = CERTIFIED.SKILL_ID WHERE SKILL_NAME = ‘Basic Database Manipulation’;
You will learn more about these commands in Chapter 7, Introduction to Structured Query Language.
Note that because each skill name is stored only once, the names cannot be spelled or abbreviated differently for different employees. Also, the additional certification
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Chapter 1
Database Systems 13
FIGURE1.5 EMPLOYEESKILLCERTIFICATIONSINAGOODDESIGN
Database name: Ch01_Text Table name: EMPLOYEE Table name: CERTIFIED
Table name: SKILL

14 Part 1
Database Concepts
of an employee with a fourth or fifth skill does not require changes to the structure of the tables.
Proper database design requires the designer to identify precisely the database’s expected use. Designing a transactional database emphasizes accurate and con- sistent data and operational speed. Designing a data warehouse database empha- sizes the use of historical and aggregated data. Designing a database to be used in a centralized, single-user environment requires a different approach from that used in the design of a distributed, multiuser database. This book emphasizes the design of transactional, centralized, single-user, and multiuser databases. Chapters 12 and 13 also examine critical issues confronting the designer of distributed and data warehouse databases.
Designing appropriate data repositories of integrated information using the two- dimensional table structures found in most databases is a process of decomposition. The integrated data must be decomposed properly into its constituent parts, with each part stored in its own table. Further, the relationships between these tables must be carefully considered and implemented so the integrated view of the data can be rec- reated later as information for the end user. A well-designed database facilitates data management and generates accurate and valuable information. A poorly designed database is likely to become a breeding ground for difficult-to-trace errors that may lead to poor decision making—and poor decision making can lead to the failure of an organization. Database design is simply too important to be left to luck. That’s why college students study database design, why organizations of all types and sizes send personnel to database design seminars, and why database design consultants often make an excellent living.
1-5 Evolution of File System Data Processing
Understanding what a database is, what it does, and the proper way to use it can be clar- ified by considering what a database is not. A brief explanation of the evolution of file system data processing can be helpful in understanding the data access limitations that databases attempt to overcome. Understanding these limitations is relevant to database designers and developers because database technologies do not make these problems magically disappear—database technologies simply make it easier to create solutions that avoid these problems. Creating database designs that avoid the pitfalls of earlier systems requires that the designer understand these problems and how to avoid them; otherwise, the database technologies are no better (and are potentially even worse!) than the tech- nologies and techniques they have replaced.
1-5a Manual File Systems
To be successful, an organization must develop systems for handling core business tasks. Historically, such systems were often manual, paper-and-pencil systems. The papers within these systems were organized to facilitate the expected use of the data. Typically, this was accomplished through a system of file folders and filing cabinets. As long as a collection of data was relatively small and an organization’s business users had few reporting requirements, the manual system served its role well as a data repository. However, as organizations grew and as reporting requirements became more complex, keeping track of data in a manual file system became more difficult. Therefore, compa- nies looked to computer technology for help.
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1-5b Computerized File Systems
Generating reports from manual file systems was slow and cumbersome. In fact, some business managers faced government-imposed reporting requirements that led to weeks of intensive effort each quarter, even when a well-designed manual system was used. Therefore, a data processing (DP) specialist was hired to create a computer-based sys- tem that would track data and produce required reports.
Initially, the computer files within the file system were similar to the manual files. A simple example of a customer data file for a small insurance company is shown in Figure 1.6. (You will discover later that the file structure shown in Figure 1.6, although typically found in early file systems, is unsatisfactory for a database.)
The description of computer files requires a specialized vocabulary. Every discipline develops its own terminology to enable its practitioners to communicate clearly. The basic file vocabulary shown in Table 1.2 will help you to understand subsequent discussions more easily.
Chapter 1
Database Systems 15
FIGURE 1.6 CONTENTS OF THE CUSTOMER FILE
C_NAME = Customer name C_PHONE = Customer phone C_ADDRESS = Customer address C_ZIP = Customer zip code
Database name: Ch01_Text
A_NAME = Agent name
A_PHONE = Agent phone
TP = Insurance type
AMT = Insurance policy amount, in thousands of $ REN = Insurance renewal date
TABLE 1.2
BASIC FILE TERMINOLOGY
TERM
DEFINITION
Data
Raw facts, such as a telephone number, a birth date, a customer name, and a year-to-date (YTD) sales value. Data has little meaning unless it has been organized in some logical manner.
Field
A character or group of characters (alphabetic or numeric) that has a specific meaning. A field is used to define and store data.
Record
A logically connected set of one or more fields that describes a person, place, or thing. For example, the fields that constitute a record for a customer might consist of the customer’s name, address, phone number, date of birth, credit limit, and unpaid balance.
File
A collection of related records. For example, a file might contain data about the students currently enrolled at Gigantic University.
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data processing (DP) specialist
The person responsible for developing
and managing a computerized file processing system.

16
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Online Content
Using the proper file terminology in Table 1.2, you can identify the file components shown in Figure 1.6. The CUSTOMER file contains 10 records. Each record is composed of 9 fields: C_NAME, C_PHONE, C_ADDRESS, C_ZIP, A_NAME, A_PHONE, TP, AMT, and REN. The 10 records are stored in a named file. Because the file in Figure 1.6 contains customer data for the insurance company, its filename is CUSTOMER.
When business users wanted data from the computerized file, they sent requests for the data to the DP specialist. For each request, the DP specialist had to create pro- grams to retrieve the data from the file, manipulate it in whatever manner the user had requested, and present it as a printed report. If a request was for a report that had been run previously, the DP specialist could rerun the existing program and provide the printed results to the user. As other business users saw the new and innovative ways in which customer data was being reported, they wanted to be able to view their data in similar fashions. This generated more requests for the DP specialist to create more computerized files of other business data, which in turn meant that more data man- agement programs had to be created, which led to even more requests for reports. For example, the sales department at the insurance company created a file named SALES, which helped track daily sales efforts. The sales department’s success was so obvious that the personnel department manager demanded access to the DP specialist to auto- mate payroll processing and other personnel functions. Consequently, the DP special- ist was asked to create the AGENT file shown in Figure 1.7. The data in the AGENT file was used to write checks, keep track of taxes paid, and summarize insurance coverage, among other tasks.
The
in each chapter are available at www. cengagebrain.com. Throughout the book, Online Content boxes highlight material related to chapter content on the website.
databases used
FIGURE 1.7 CONTENTS OF THE AGENT FILE
A_NAME A_PHONE A_ADDRESS ZIP
HIRED
= Agent name
= Agent phone
= Agent address
= Agent zip code
= Agent date of hire
YTD_PAY YTD_FIT YTD_FICA YTD_SLS DEP
Database name: Ch01_Text
= Year-to-date pay
= Year-to-date federal income tax paid = Year-to-date Social Security taxes paid = Year-to-date sales
= Number of dependents
As more and more computerized files were developed, the problems with this type of file system became apparent. While these problems are explored in detail in the next section, the problems basically centered on having many data files that contained related—often overlapping—data with no means of controlling or managing the data consistently across all of the files. As shown in Figure 1.8, each file in the system used its own application program to store, retrieve, and modify data. Also, each file was owned by the individual or the department that commissioned its creation.
The advent of computer files to store company data was significant; it not only estab- lished a landmark in the use of computer technologies, it also represented a huge step forward in a business’s ability to process data. Previously, users had direct, hands-on access to all of the business data. But they didn’t have the tools to convert that data into the information they needed. The creation of computerized file systems gave them improved tools for manipulating the company data that allowed them to create new
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Chapter 1
Database Systems 17
FIGURE 1.8 A SIMPLE FILE SYSTEM
File Management Programs
File Report Programs
File Management Programs
File Report Programs
Sales department
SALES
Personnel department
AGENT
information. However, it had the additional effect of introducing a schism between the end users and their data. The desire to close the gap between the end users and the data influenced the development of many types of computer technologies, sys- tem designs, and uses (and misuses) of many technologies and techniques. However, such developments also created a split between the ways DP specialists and end users viewed the data.
• From the DP specialist’s perspective, the computer files within the file system were created to be similar to the manual files. Data management programs were created to add to, update, and delete data from the file.
• From the end user’s perspective, the systems separated the users from the data. As the users’ competitive environment pushed them to make more and more decisions in less time, users became frustrated by the delay between conceiving of a new way to create information from the data and the point when the DP specialist actually created the programs to generate that information.
1-5c File System Redux: Modern End-User Productivity Tools
The users’ desire for direct, hands-on access to data helped to fuel the adoption of per- sonal computers for business use. Although not directly related to file system evolution, the ubiquitous use of personal productivity tools can introduce the same problems as the old file systems.
Personal computer spreadsheet programs such as Microsoft Excel are widely used by business users, and they allow the user to enter data in a series of rows and columns so the data can be manipulated using a wide range of functions. The popularity of spreadsheet applications has enabled users to conduct sophisticated data analysis that has greatly enhanced their ability to understand the data and make better decisions. Unfortunately, as in the old adage “When the only tool you have is a hammer, every problem looks like
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CUSTOMER e

18 Part 1
Database Concepts
a nail,” users have become so adept at working with spreadsheets that they tend to use them to complete tasks for which spreadsheets are not appropriate.
A common misuse of spreadsheets is as a substitute for a database. Interestingly, end users often take the limited data to which they have direct access and place it in a spreadsheet format similar to that of the traditional, manual data storage systems— which is precisely what the early DP specialists did when creating computerized data files. Due to the large number of users with spreadsheets, each making separate copies of the data, the resulting “file system” of spreadsheets suffers from the same problems as the file systems created by the early DP specialists, which are outlined in the next section.
1-6 ProblemswithFileSystemDataProcessing
The file system method of organizing and managing data was a definite improvement over the manual system, and the file system served a useful purpose in data manage- ment for over two decades—a very long time in the computer era. Nonetheless, many problems and limitations became evident in this approach. A critique of the file system method serves two major purposes:
• Understanding the shortcomings of the file system enables you to understand the development of modern databases.
• Many of the problems are not unique to file systems. Failure to understand such prob- lems is likely to lead to their duplication in a database environment, even though database technology makes it easy to avoid them.
The following problems associated with file systems, whether created by DP specialists or through a series of spreadsheets, severely challenge the types of information that can be created from the data as well as the accuracy of the information:
• Lengthy development times. The first and most glaring problem with the file system approach is that even the simplest data-retrieval task requires extensive programming. With the older file systems, programmers had to specify what must be done and how to do it. As you will learn in upcoming chapters, modern databases use a nonprocedural data manipulation language that allows the user to specify what must be done without specifying how.
• Difficulty of getting quick answers. The need to write programs to produce even the simplest reports makes ad hoc queries impossible. Harried DP specialists who worked with mature file systems often received numerous requests for new reports. They were often forced to say that the report will be ready “next week” or even “next month.” If you need the information now, getting it next week or next month will not serve your information needs.
• Complex system administration. System administration becomes more difficult as the number of files in the system expands. Even a simple file system with a few files requires creating and maintaining several file management programs. Each file must have its own file management programs that allow the user to add, modify, and delete records; to list the file contents; and to generate reports. Because ad hoc queries are not possible, the file reporting programs can multiply quickly. The problem is com- pounded by the fact that each department in the organization “owns” its data by creating its own files.
• Lack of security and limited data sharing. Another fault of a file system data repos- itory is a lack of security and limited data sharing. Data sharing and security
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are closely related. Sharing data among multiple geographically dispersed users introduces a lot of security risks. In terms of spreadsheet data, while many spread- sheet programs provide rudimentary security options, they are not always used, and even when they are, they are insufficient for robust data sharing among users. In terms of creating data management and reporting programs, security and data-sharing features are difficult to program and consequently are often omitted from a file system environment. Such features include effective password protec- tion, the ability to lock out parts of files or parts of the system itself, and other mea- sures designed to safeguard data confidentiality. Even when an attempt is made to improve system and data security, the security devices tend to be limited in scope and effectiveness.
• Extensive programming. Making changes to an existing file structure can be difficult in a file system environment. For example, changing just one field in the original CUSTOMER file would require a program that:
1. Reads a record from the original file.
2. Transformstheoriginaldatatoconformtothenewstructure’sstoragerequirements. 3. Writes the transformed data into the new file structure.
4. Repeats the preceding steps for each record in the original file.
In fact, any change to a file structure, no matter how minor, forces modifications in all of the programs that use the data in that file. Modifications are likely to produce errors (bugs), and additional time is spent using a debugging process to find those errors. Those limitations, in turn, lead to problems of structural and data dependence.
1-6a Structural and Data Dependence
A file system exhibits structural dependence, which means that access to a file is dependent on its structure. For example, adding a customer date-of-birth field to the CUSTOMER file shown in Figure 1.6 would require the four steps described in the pre- vious section. Given this change, none of the previous programs will work with the new CUSTOMER file structure. Therefore, all of the file system programs must be modified to conform to the new file structure. In short, because the file system application programs are affected by changes in the file structure, they exhibit structural dependence. Con- versely, structural independence exists when you can change the file structure without affecting the application’s ability to access the data.
Even changes in the characteristics of data, such as changing a field from integer to decimal, require changes in all the programs that access the file. Because all data access programs are subject to change when any of the file’s data storage characteristics change (that is, changing the data type), the file system is said to exhibit data dependence. Conversely, data independence exists when you can change the data storage character- istics without affecting the program’s ability to access the data.
The practical significance of data dependence is the difference between the logical data format (how the human being views the data) and the physical data format (how the computer must work with the data). Any program that accesses a file system’s file must tell the computer not only what to do but how to do it. Con- sequently, each program must contain lines that specify the opening of a specific file type, its record specification, and its field definitions. Data dependence makes the file system extremely cumbersome from the point of view of a programmer and database manager.
structural dependence
A data characteristic
in which a change in the database schema affects data access, thus requiring changes in all access programs.
structural independence
A data characteristic in which changes in the database schema do not affect data access.
data dependence
A data condition
in which data representation and manipulation are dependent on the physical data storage characteristics.
data independence
A condition in which data access is unaffected by changes in the physical data storage characteristics.
logical data format
The way a person views data within the context of a problem domain.
physical data format
The way a computer “sees” (stores) data.
Chapter 1
Database Systems 19
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20 Part 1
Database Concepts
1-6b Data Redundancy
Note
The file system’s structure makes it difficult to combine data from multiple sources, and its lack of security renders the file system vulnerable to security breaches. The organi- zational structure promotes the storage of the same basic data in different locations. (Database professionals use the term islands of information for such scattered data locations.) The dispersion of data is exacerbated by the use of spreadsheets to store data. In a file system, the entire sales department would share access to the SALES data file through the data management and reporting programs created by the DP specialist. With the use of spreadsheets, each member of the sales department can create his or her own copy of the sales data. Because data stored in different locations will probably not be updated consistently, the islands of information often contain different versions of the same data. For example, in Figures 1.6 and 1.7, the agent names and phone num- bers occur in both the CUSTOMER and the AGENT files. You only need one correct copy of the agent names and phone numbers. Having them occur in more than one place produces data redundancy. Data redundancy exists when the same data is stored unnecessarily at different places.
Uncontrolled data redundancy sets the stage for the following:
• Poor data security. Having multiple copies of data increases the chances for a copy of the data to be susceptible to unauthorized access. Chapter 16, Database Administra- tion and Security, explores the issues and techniques associated with securing data.
• Data inconsistency. Data inconsistency exists when different and conflicting ver- sions of the same data appear in different places. For example, suppose you change an agent’s phone number in the AGENT file. If you forget to make the correspond- ing change in the CUSTOMER file, the files contain different data for the same agent. Reports will yield inconsistent results that depend on which version of the data is used.
• Data-entry errors. Data-entry errors are more likely to occur when complex entries (such as 10-digit phone numbers) are made in several different files or recur frequently in one or more files. In fact, the CUSTOMER file shown in Figure 1.6 contains just such an entry error: the third record in the CUSTOMER file has transposed digits in the agent’s phone number (615-882-2144 rather than 615-882-1244).
• Data integrity problems. It is possible to enter a nonexistent sales agent’s name and phone number into the CUSTOMER file, but customers are not likely to be impressed if the insurance agency supplies the name and phone number of an agent who does not exist. Should the personnel manager allow a nonexistent agent to accrue bonuses and benefits? In fact, a data-entry error such as an incorrectly spelled name or an incorrect phone number yields the same kind of data integrity problems.
islands of information
In the old file system environment, pools
of independent,
often duplicated, and inconsistent data created and managed by different departments.
data redundancy
Exists when the
same data is stored unnecessarily at different places.
data integrity
In a relational database, a condition in which the data in the database complies with all entity and referential integrity constraints.
Data that displays data inconsistency is also referred to as data that lacks data integrity. Data integrity is defined as the condition in which all of the data in the database is consistent with the real-world events and conditions. In other words, data integrity means that:
• Data is accurate—there are no data inconsistencies.
• Data is verifiable—the data will always yield consistent results.
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1-6c Data Anomalies
The dictionary defines anomaly as “an abnormality.” Ideally, a field value change should be made in only a single place. Data redundancy, however, fosters an abnormal condition by forcing field value changes in many different locations. Look at the CUSTOMER file in Figure 1.6. If agent Leah F. Hahn decides to get married and move, the agent name, address, and phone number are likely to change. Instead of making these changes in a single file (AGENT), you must also make the change each time that agent’s name and phone number occur in the CUSTOMER file. You could be faced with the prospect of making hundreds of corrections, one for each of the customers served by that agent! The same problem occurs when an agent decides to quit. Each customer served by that agent must be assigned a new agent. Any change in any field value must be correctly made in many places to maintain data integrity. A data anomaly develops when not all of the required changes in the redundant data are made successfully. The data anomalies found in Figure 1.6 are commonly defined as follows:
• Update anomalies. If agent Leah F. Hahn has a new phone number, it must be entered in each of the CUSTOMER file records in which Ms. Hahn’s phone number is shown. In this case, only four changes must be made. In a large file system, such a change might occur in hundreds or even thousands of records. Clearly, the potential for data inconsistencies is great.
• Insertion anomalies. If only the CUSTOMER file existed and you needed to add a new agent, you would also add a dummy customer data entry to reflect the new agent’s addition. Again, the potential for creating data inconsistencies would be great.
• Deletion anomalies. If you delete the customers Amy B. O’Brian, George Williams, and Olette K. Smith, you will also delete John T. Okon’s agent data. Clearly, this is not desirable.
On a positive note, however, this book will help you develop the skills needed to design and model a successful database that avoids the problems listed in this section.
1-7 Database Systems
The problems inherent in file systems make using a database system very desirable. Unlike the file system, with its many separate and unrelated files, the database system consists of logically related data stored in a single logical data repository. (The “logical” label reflects the fact that the data repository appears to be a single unit to the end user, even though data might be physically distributed among multiple storage facilities and locations.) Because the database’s data repository is a single logical unit, the database represents a major change in the way end-user data is stored, accessed, and managed. The database’s DBMS, shown in Figure 1.9, provides numerous advantages over file sys- tem management, shown in Figure 1.8, by making it possible to eliminate most of the file system’s data inconsistency, data anomaly, data dependence, and structural depen- dence problems. Better yet, the current generation of DBMS software stores not only the data structures, but also the relationships between those structures and the access paths to those structures—all in a central location. The current generation of DBMS software also takes care of defining, storing, and managing all required access paths to those components.
data anomaly
A data abnormality
in which inconsistent changes have been made to a database. For example, an employee moves, but the address change is not corrected in all files in the database.
Chapter 1
Database Systems 21
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22 Part 1
Database Concepts
FIGURE 1.9 CONTRASTING DATABASE AND FILE SYSTEMS
Cengage Learning © 2015
A Database System Personnel dept.
Database
Employees Customers Sales Inventory Accounts
Sales dept.
Personnel dept.
Employees
DBMS
A File System Sales dept.
Accounting dept.
Accounting dept.
Remember that the DBMS is just one of several crucial components of a database sys- tem. The DBMS may even be referred to as the database system’s heart. However, just as it takes more than a heart to make a human being function, it takes more than a DBMS to make a database system function. In the sections that follow, you’ll learn what a database system is, what its components are, and how the DBMS fits into the picture.
1-7a The Database System Environment
The term database system refers to an organization of components that define and regulate the collection, storage, management, and use of data within a database environ- ment. From a general management point of view, the database system is composed of the five major parts shown in Figure 1.10: hardware, software, people, procedures, and data.
Let’s take a closer look at the five components shown in Figure 1.10:
• Hardware. Hardware refers to all of the system’s physical devices, including computers (PCs, tablets, workstations, servers, and supercomputers), storage devices, printers, network devices (hubs, switches, routers, fiber optics), and other devices (automated teller machines, ID readers, and so on).
• Software. Although the most readily identified software is the DBMS itself, three types of software are needed to make the database system function fully: operating system software, DBMS software, and application programs and utilities.
– Operating system software manages all hardware components and makes it possible for all other software to run on the computers. Examples of operating system soft- ware include Microsoft Windows, Linux, Mac OS, UNIX, and MVS.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
database system
An organization of components that defines and regulates the collection, storage, management, and use of data in a database environment.
Copyright 2017 Cengage Lea Editorial review has deemed that
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Chapter 1
Database Systems 23
FIGURE 1.10 THE DATABASE SYSTEM ENVIRONMENT
Procedures and standards
End users
use
writes and enforces
Analysts
supervises
Database
Database administrator administrator
System
designer
manages
designs Programmers
Hardware
Application programs
write
access
DBMS utilities
DBMS
– DBMS software manages the database within the database system. Some examples of DBMS software include Microsoft’s SQL Server, Oracle Corporation’s Oracle, Oracle’s MySQL, and IBM’s DB2.
– Application programs and utility software are used to access and manipulate data in the DBMS and to manage the computer environment in which data access and manipulation take place. Application programs are most commonly used to access data within the database to generate reports, tabulations, and other information to facilitate decision making. Utilities are the software tools used to help manage the database system’s computer components. For example, all of the major DBMS vendors now provide graphical user interfaces (GUIs) to help create database structures, control database access, and monitor database operations.
• People. This component includes all users of the database system. On the basis of primary job functions, five types of users can be identified in a database system: sys- tem administrators, database administrators, database designers, system analysts and programmers, and end users. Each user type, described next, performs both unique and complementary functions.
– System administrators oversee the database system’s general operations.
– Database administrators, also known as DBAs, manage the DBMS and ensure that the database is functioning properly. The DBA’s role is sufficiently import- ant to warrant a detailed exploration in Chapter 16, Database Administration and Security.
– Database designers design the database structure. They are, in effect, the database architects. If the database design is poor, even the best application programmers and the most dedicated DBAs cannot produce a useful database environment. Because organizations strive to optimize their data resources, the database designer’s job description has expanded to cover new dimensions and growing responsibilities.
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a
t
D
a

24 Part 1
Database Concepts
– System analysts and programmers design and implement the application programs. They design and create the data-entry screens, reports, and procedures through which end users access and manipulate the database’s data.
– End users are the people who use the application programs to run the organi- zation’s daily operations. For example, sales clerks, supervisors, managers, and directors are all classified as end users. High-level end users employ the informa- tion obtained from the database to make tactical and strategic business decisions.
• Procedures. Procedures are the instructions and rules that govern the design and use of the database system. Procedures are a critical, although occasionally forgotten, component of the system. Procedures play an important role in a company because they enforce the standards by which business is conducted within the organization and with customers. Procedures also help to ensure that companies have an organized way to monitor and audit the data that enter the database and the information gener- ated from those data.
• Data. The word data covers the collection of facts stored in the database. Because data is the raw material from which information is generated, determining which data to enter into the database and how to organize that data is a vital part of the database designer’s job.
A database system adds a new dimension to an organization’s management struc- ture. The complexity of this managerial structure depends on the organization’s size, its functions, and its corporate culture. Therefore, database systems can be created and managed at different levels of complexity and with varying adherence to precise stan- dards. For example, compare a local convenience store system with a national insur- ance claims system. The convenience store system may be managed by two people, the hardware used is probably a single computer, the procedures are probably simple, and the data volume tends to be low. The national insurance claims system is likely to have at least one systems administrator, several full-time DBAs, and many designers and programmers; the hardware probably includes several servers at multiple locations throughout the United States; the procedures are likely to be numerous, complex, and rigorous; and the data volume tends to be high.
In addition to the different levels of database system complexity, managers must also take another important fact into account: database solutions must be cost-effective as well as tactically and strategically effective. Producing a million-dollar solution to a thousand-dollar problem is hardly an example of good database system selection or of good database design and management. Finally, the database technology already in use is likely to affect the selection of a database system.
1-7b DBMS Functions
A DBMS performs several important functions that guarantee the integrity and consis- tency of the data in the database. Most of those functions are transparent to end users, and most can be achieved only through the use of a DBMS. They include data dictio- nary management, data storage management, data transformation and presentation, security management, multiuser access control, backup and recovery management, data integrity management, database access languages and application program- ming interfaces, and database communication interfaces. Each of these functions is explained as follows:
• Data dictionary management. The DBMS stores definitions of the data elements and their relationships (metadata) in a data dictionary. In turn, all programs that
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access the data in the database work through the DBMS. The DBMS uses the data dictionary to look up the required data component structures and relationships, thus relieving you from having to code such complex relationships in each pro- gram. Additionally, any changes made in a database structure are automatically recorded in the data dictionary, thereby freeing you from having to modify all of the programs that access the changed structure. In other words, the DBMS provides data abstraction, and it removes structural and data dependence from the system. For example, Figure 1.11 shows how Microsoft SQL Server Express presents the data definition for the CUSTOMER table.
• Data storage management. The DBMS creates and manages the complex structures required for data storage, thus relieving you from the difficult task of defining and programming the physical data characteristics. A modern DBMS provides storage not only for the data but for related data-entry forms or screen definitions, report definitions, data validation rules, procedural code, structures to handle video and picture formats, and so on. Data storage management is also important for database performance tuning. Performance tuning relates to the activities that make the database perform more efficiently in terms of storage and access speed. Although the user sees the database as a single data storage unit, the DBMS actu- ally stores the database in multiple physical data files. (See Figure 1.12.) Such data files may even be stored on different storage media. Therefore, the DBMS doesn’t have to wait for one disk request to finish before the next one starts. In other words, the DBMS can fulfill database requests concurrently. Data storage man- agement and performance tuning issues are addressed in Chapter 11, Database Performance Tuning and Query Optimization.
data dictionary
A DBMS component that stores metadata—data about data. The data dictionary contains
data definitions as well as data characteristics and relationships. May also include data that is external to the DBMS.
performance tuning
Activities that make a database perform more efficiently in terms of storage and access speed.
Chapter 1
Database Systems 25
FIGURE 1.11 ILLUSTRATING METADATA WITH MICROSOFT SQL SERVER EXPRESS
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26 Part 1
Database Concepts
FIGURE1.12 ILLUSTRATINGDATASTORAGEMANAGEMENTWITHORACLE
Database Name: PRODORA
The PRODORA database is actually stored in six physical datafiles organized into six logical tablespaces located on the E: drive of the database server computer
The Oracle Enterprise Manager Express interface also shows the amount of space used by each of the datafiles.
The Oracle Enterprise Manager Express GUI shows the data
storage management characteristics for the PRODORA database.
• Data transformation and presentation. The DBMS transforms entered data to con- form to required data structures. The DBMS relieves you of the chore of distin- guishing between the logical data format and the physical data format. That is, the DBMS formats the physically retrieved data to make it conform to the user’s logical expectations. For example, imagine an enterprise database used by a multinational company. An end user in England would expect to enter the date July 11, 2017, as “11/07/2017.” In contrast, the same date would be entered in the United States as “07/11/2017.” Regardless of the data presentation format, the DBMS must manage the date in the proper format for each country.
• Security management. The DBMS creates a security system that enforces user secu- rity and data privacy. Security rules determine which users can access the database, which data items each user can access, and which data operations (read, add, delete, or modify) the user can perform. This is especially important in multiuser database systems. Chapter 16, Database Administration and Security, examines data security and privacy issues in greater detail. All database users may be authenticated to the DBMS through a username and password or through biometric authentication such as a fingerprint scan. The DBMS uses this information to assign access privileges to various database components such as queries and reports.
• Multiuser access control. To provide data integrity and data consistency, the DBMS uses sophisticated algorithms to ensure that multiple users can access the database
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concurrently without compromising its integrity. Chapter 10, Transaction Manage- ment and Concurrency Control, covers the details of multiuser access control.
• Backup and recovery management. The DBMS provides backup and data recovery to ensure data safety and integrity. Current DBMS systems provide special utilities that allow the DBA to perform routine and special backup and restore procedures. Recovery management deals with the recovery of the database after a failure, such as a bad sector in the disk or a power failure. Such capability is critical to preserv- ing the database’s integrity. Chapter 16 covers backup and recovery issues.
• Data integrity management. The DBMS promotes and enforces integrity rules, thus minimizing data redundancy and maximizing data consistency. The data relation- ships stored in the data dictionary are used to enforce data integrity. Ensuring data integrity is especially important in transaction-oriented database systems. Data integrity and transaction management issues are addressed in Chapter 7, Intro- duction to Structured Query Language (SQL), and Chapter 10.
• Database access languages and application programming interfaces. The DBMS pro- vides data access through a query language. A query language is a nonprocedural language—one that lets the user specify what must be done without having to specify how. Structured Query Language (SQL) is the de facto query language and data access standard supported by the majority of DBMS vendors. Chapter 7, Introduction to Structure Query Language (SQL), and Chapter 8, Advanced SQL, address the use of SQL. The DBMS also provides application programming interfaces to procedural languages such as COBOL, C, Java, Visual Basic.NET, and C#. In addition, the DBMS provides administrative utilities used by the DBA and the database designer to create, implement, monitor, and maintain the database.
• Database communication interfaces. A current-generation DBMS accepts end-user requests via multiple, different network environments. For example, the DBMS might provide access to the database via the Internet through the use of web browsers such as Mozilla Firefox, Google Chrome, or Microsoft Internet Explorer. In this environment, communications can be accomplished in several ways:
– End users can generate answers to queries by filling in screen forms through their preferred web browser.
– The DBMS can automatically publish predefined reports on a website.
– The DBMS can connect to third-party systems to distribute information via email
or other productivity applications.
Database communication interfaces are examined in greater detail in Chapter 12, Distributed Database Management Systems; in Chapter 15, Database Connectivity and Web Technologies; and in Appendix I, Databases in Electronic Commerce. (Appendixes are available at www.cengagebrain.com.)
Note
Chapter 1
Database Systems 27
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Why a Spreadsheet Is Not a Database
While a spreadsheet allows for the manipulation of data in a tabular format, it does not support even the most basic database functionality such as support for self-documentation through metadata, enforcement of data types or domains to ensure consistency of data within a col- umn, defined relationships among tables, or constraints to ensure consistency of data across related tables. Most users lack the necessary training to recognize the limitations of spread- sheets for these types of tasks.
query language
A nonprocedural language that is used by a DBMS to manipulate its data. An example of a query language is SQL.
Structured Query Language (SQL)
A powerful and flexible relational database language composed of commands that enable users to create database and table structures, perform various types of data manipulation and data administration, and query the database to extract useful information.

28 Part 1
Database Concepts
1-7c Managing the Database System: A Shift in Focus
The introduction of a database system over the file system provides a framework in which strict procedures and standards can be enforced. Consequently, the role of the human component changes from an emphasis on programming (in the file system) to a focus on the broader aspects of managing the organization’s data resources and on the administration of the complex database software itself.
The database system makes it possible to tackle far more sophisticated uses of the data resources, as long as the database is designed to make use of that power. The kinds of data structures created within the database and the extent of the relationships among them play a powerful role in determining the effectiveness of the database system.
Although the database system yields considerable advantages over previous data management approaches, database systems do carry significant disadvantages:
• Increased costs. Database systems require sophisticated hardware and software and highly skilled personnel. The cost of maintaining the hardware, software, and person- nel required to operate and manage a database system can be substantial. Training, licensing, and regulation compliance costs are often overlooked when database sys- tems are implemented.
• Management complexity. Database systems interface with many different technolo- gies and have a significant impact on a company’s resources and culture. The changes introduced by the adoption of a database system must be properly managed to ensure that they help advance the company’s objectives. Because database systems hold cru- cial company data that are accessed from multiple sources, security issues must be assessed constantly.
• Maintaining currency. To maximize the efficiency of the database system, you must keep your system current. Therefore, you must perform frequent updates and apply the latest patches and security measures to all components. Because database technology advances rapidly, personnel training costs tend to be significant.
• Vendor dependence. Given the heavy investment in technology and personnel train- ing, companies might be reluctant to change database vendors. As a consequence, vendors are less likely to offer pricing point advantages to existing customers, and those customers might be limited in their choice of database system components.
• Frequent upgrade/replacement cycles. DBMS vendors frequently upgrade their prod- ucts by adding new functionality. Such new features often come bundled in new upgrade versions of the software. Some of these versions require hardware upgrades. Not only do the upgrades themselves cost money, it also costs money to train database users and administrators to properly use and manage the new features.
Now that you know what a database and DBMS are, and why they are necessary, you are ready to begin developing your career as a database professional.
1-8 PreparingforYourDatabase Professional Career
In this chapter, you were introduced to the concepts of data, information, databases, and DBMSs. You also learned that, regardless of what type of database you use (OLTP, OLAP, or NoSQL), or what type of database environment you are working in (e.g., Oracle, Microsoft, IBM, or Hadoop), the success of a database system greatly depends on how well the database structure is designed.
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Throughout this book, you will learn the building blocks that lay the foundation for your career as a database professional. Understanding these building blocks and devel- oping the skills to use them effectively will prepare you to work with databases at many different levels within an organization. A small sample of such career opportunities is shown in Table 1.3.
Chapter 1
Database Systems 29
TABLE 1.3
DATABASE CAREER OPPORTUNITIES
JOB TITLE
DESCRIPTION
SAMPLE SKILLS REQUIRED
Database Developer
Create and maintain database-based applications
Programming, database fundamentals, SQL
Database Designer
Design and maintain databases
Systems design, database design, SQL
Database Administrator
Manage and maintain DBMS and databases
Database fundamentals, SQL, vendor courses
Database Analyst
Develop databases for decision support reporting
SQL, query optimization, data warehouses
Database Architect
Design and implementation of database environments (conceptual, logical, and physical)
DBMS fundamentals, data modeling, SQL, hardware knowledge, etc.
Database Consultant
Help companies leverage database technologies to improve business processes and achieve specific goals
Database fundamentals, data modeling, database design, SQL, DBMS, hardware, vendor-specific technologies, etc.
Database Security Officer
Implement security policies for data administration
DBMS fundamentals, database administration, SQL, data security technologies, etc.
Cloud Computing Data Architect
Design and implement the infrastructure for next-generation cloud database systems
Internet technologies, cloud storage technologies, data security, performance tuning, large databases, etc.
As you also learned in this chapter, database technologies are constantly evolving to address new challenges such as large databases, semistructured and unstructured data, increasing processing speed, and lowering costs. While database technologies can change quickly, the fundamental concepts and skills do not. It is our goal that after you learn the database essentials in this book, you will be ready to apply your knowledge and skills to work with traditional OLTP and OLAP systems as well as cutting-edge, complex data- base technologies such as the following:
• Very Large Databases (VLDB). Many vendors are addressing the need for databases that support large amounts of data, usually in the petabyte range. (A petabyte is more than 1,000 terabytes.) VLDB vendors include Oracle Exadata, IBM’s Netezza, HP’s Vertica, and Teradata. VLDB are now being overtaken in market interest by Big Data databases.
• Big Data databases. Products such as Cassandra (Facebook) and BigTable (Google) are using “columnar-database” technologies to support the needs of database appli- cations that manage large amounts of “nontabular” data. See more about this topic in Chapter 2.
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30 Part 1
Database Concepts
• In-memory databases. Most major database vendors also offer some type of in-memory database support to address the need for faster database processing. In-memory databases store most of their data in primary memory (RAM) rather than in slower secondary storage (hard disks). In-memory databases include IBM’s solidDB and Oracle’s TimesTen.
• Cloud databases. Companies can now use cloud database services to quickly add database systems to their environment while simultaneously lowering the total cost of ownership of a new DBMS. A cloud database offers all the advantages of a local DBMS, but instead of residing within your organization’s network infrastructure, it resides on the Internet. See more about this topic in Chapter 15.
We address some of these topics in this book, but not all—no single book can cover the entire realm of database technologies. This book’s primary focus is to help you learn database fundamentals, develop your database design skills, and master your SQL skills so you will have a head start in becoming a successful database professional. However, you first must learn about the tools at your disposal. In the next chapter, you will learn different approaches to data management and how these approaches influ- ence your designs.
Summary
• Data consists of raw facts. Information is the result of processing data to reveal its meaning. Accurate, relevant, and timely information is the key to good decision making, and good decision making is the key to organizational survival in a global environment.
• Data is usually stored in a database. To implement a database and to manage its con- tents, you need a database management system (DBMS). The DBMS serves as the intermediary between the user and the database. The database contains the data you have collected and “data about data,” known as metadata.
• Database design defines the database structure. A well-designed database facili- tates data management and generates accurate and valuable information. A poorly designed database can lead to poor decision making, and poor decision making can lead to the failure of an organization.
• Databases can be classified according to the number of users supported, where the data is located, the type of data stored, the intended data usage, and the degree to which the data is structured.
• Databases evolved from manual and then computerized file systems. In a file system, data is stored in independent files, each requiring its own data management programs. Although this method of data management is largely outmoded, understanding its characteristics makes database design easier to comprehend.
• Some limitations of file system data management are that it requires extensive pro- gramming, system administration can be complex and difficult, making changes to existing structures is difficult, and security features are likely to be inadequate. Also, independent files tend to contain redundant data, leading to problems of structural and data dependence.
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• Database management systems were developed to address the file system’s inherent weaknesses. Rather than depositing data in independent files, a DBMS presents the database to the end user as a single data repository. This arrangement promotes data sharing, thus eliminating the potential problem of islands of information. In addition, the DBMS enforces data integrity, eliminates redundancy, and promotes data security.
• Knowledge of database technologies leads to many career opportunities in the ever-expanding IT industry. There is a variety of specialization within the database arena for a wide range of skills and expertise.
Chapter 1
Database Systems 31
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Key Terms
ad hoc query
analytical database
business intelligence
centralized database
cloud database
data
data anomaly
data dependence
data dictionary
data inconsistency
data independence
data integrity
data management
data processing (DP) specialist
data quality
data redundancy data warehouse database database design
database management system (DBMS)
database system
desktop database
discipline-specific database
distributed database
enterprise database
Extensible Markup Language (XML)
field
file
general-purpose database
information
islands of information
knowledge
logical data format
metadata
multiuser database
NoSQL
online analytical processing (OLAP)
online transaction processing (OLTP) database
operational database performance tuning physical data format production database query
query language
query result set
record
semistructured data
single-user database
social media
structural dependence
structural independence
structured data
Structured Query Language (SQL)
transactional database unstructured data workgroup database XML database
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengage brain.com.

32 Part 1 Database Concepts Review Questions
1. Define each of the following terms: a. data
b. field c. record d. file
2. Whatisdataredundancy,andwhichcharacteristicsofthefilesystemcanleadtoit?
3. What is data independence, and why is it lacking in file systems?
4. What is a DBMS, and what are its functions?
5. What is structural independence, and why is it important?
6. Explain the differences among data, information, and a database.
7. WhatistheroleofaDBMS,andwhatareitsadvantages?Whatareitsdisadvantages?
8. List and describe the different types of databases.
9. What are the main components of a database system?
10. What is metadata?
11. Explain why database design is important.
12. What are the potential costs of implementing a database system?
13. Use examples to compare and contrast unstructured and structured data. Which type is more prevalent in a typical business environment?
14. What are some basic database functions that a spreadsheet cannot perform?
15. What common problems does a collection of spreadsheets created by end users share with the typical file system?
16. Explain the significance of the loss of direct, hands-on access to business data that end users experienced with the advent of computerized data repositories.
17. Explain why the cost of ownership may be lower with a cloud database than with a traditional, company database.
Problems
FIGURE P1.1 THE FILE STRUCTURE FOR PROBLEMS 1–4
Online Content
The file structures you see in this problem set are sim- ulated in a Microsoft Access database named Ch01_ Problems, which is available at www.cengagebrain.com.
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Given the file structure shown in Figure P1.1, answer Problems 1–4.
1. How many records does the file contain? How many fields are there per record?
2. What problem would you encounter if you wanted to produce a listing by city? How would you solve this problem by altering the file structure?
3. If you wanted to produce a listing of the file contents by last name, area code, city, state, or zip code, how would you alter the file structure?
4. What data redundancies do you detect? How could those redundancies lead to anomalies?
5. Identify and discuss the serious data redundancy problems exhibited by the file structure shown in Figure P1.5.
6. Looking at the EMP_NAME and EMP_PHONE contents in Figure P1.5, what change(s) would you recommend?
7. Identify the various data sources in the file you examined in Problem 5.
8. Given your answer to Problem 7, what new files should you create to help eliminate the data redundancies found in the file shown in Figure P1.5?
Chapter 1
Database Systems 33
FIGURE P1.5 THE FILE STRUCTURE FOR PROBLEMS 5–8
FIGURE P1.9 THE FILE STRUCTURE FOR PROBLEMS 9–10
9. Identify and discuss the serious data redundancy problems exhibited by the file structure shown in Figure P1.9. (The file is meant to be used as a teacher class assignment schedule. One of the many problems with data redundancy is the likely occurrence of data inconsistencies—two different initials have been entered for the teacher named Maria Cordoza.)
10. GiventhefilestructureshowninFigureP1.9,whatproblem(s)mightyouencounter if building KOM were deleted?
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34 Part 1
Database Concepts
11. Using your school’s student information system, print your class schedule. The schedule probably would contain the student identification number, student name, class code, class name, class credit hours, class instructor name, the class meeting days and times, and the class room number. Use Figure P1.11 as a template to com- plete the following actions.
FIGURE P1.11 STUDENT SCHEDULE DATA FORMAT
STU_ID
STU_ NAME
CLASS_ CODE
CLASS_ NAME
CLASS_ CREDHRS
INSTR_ NAME
CLASS_ DAYS
CLASS_ TIMES
ROOM
a. Create a spreadsheet using the template shown in Figure P1.11 and enter your current class schedule.
b. Enter the class schedule of two of your classmates into the same spreadsheet.
c. Discuss the redundancies and anomalies caused by this design.
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Chapter 2
Data Models
In this chapter, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill • How data models can be classified by their level of abstraction
Preview
This chapter examines data modeling. Data modeling is the first step in the database design journey, serving as a bridge between real-world objects and the computer database. One of the most vexing problems of database design is that designers, programmers, and end users see data in different ways. Consequently, different views of the same data can lead to database designs that do not reflect an organization’s actual operation, thus failing to meet end-user needs and data efficiency requirements. To avoid such failures, database designers must obtain a precise description of the data’s nature and many uses within the organization. Communication among database designers, programmers, and end users should be frequent and clear. Data modeling clarifies such communication by reducing the complexities of database design to more easily understood abstractions that
define entities, relations, and data transformations.
First, you will learn some basic data-modeling concepts and how current data models
developed from earlier models. Tracing the development of those database models will help you understand the database design and implementation issues that are addressed in the rest of this book. In chronological order, you will be introduced to the hierarchical and network models, the relational model, and the entity relationship (ER) model. You will also learn about the use of the entity relationship diagram (ERD) as a data-modeling tool and the different notations used for ER diagrams. Next, you will be introduced to the object-oriented model and the object/relational model. Then, you will learn about the emerging NoSQL data model and how it is being used to fulfill the current need to man- age very large social media data sets efficiently and effectively. Finally, you will learn how various degrees of data abstraction help reconcile varying views of the same data.
Data Files and Available Formats
CH02_InsureCo P P P P CH02_DealCo P P P P CH02_TinyCollege P P P P
Data Files Available on cengagebrain.com
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
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36 Part 1
Database Concepts
2-1 Data Modeling and Data Models
Database design focuses on how the database structure will be used to store and manage end-user data. Data modeling, the first step in designing a database, refers to the process of creating a specific data model for a determined problem domain. (A problem domain is a clearly defined area within the real-world environment, with a well-defined scope and boundaries that will be systematically addressed.) A data model is a relatively simple representation, usually graphical, of more complex real-world data structures. In general terms, a model is an abstraction of a more complex real-world object or event. A model’s main function is to help you under- stand the complexities of the real-world environment. Within the database environ- ment, a data model represents data structures and their characteristics, relations, constraints, transformations, and other constructs with the purpose of supporting a specific problem domain.
Data modeling is an iterative, progressive process. You start with a simple under- standing of the problem domain, and as your understanding increases, so does the level of detail of the data model. When done properly, the final data model effectively is a “blueprint” with all the instructions to build a database that will meet all end-user requirements. This blueprint is narrative and graphical in nature, meaning that it con- tains both text descriptions in plain, unambiguous language and clear, useful diagrams depicting the main data elements.
data modeling
The process of creating a specific data model for a determined problem domain.
data model
A representation, usually graphic, of a complex “real-world” data structure. Data models are used in the database design phase of the Database Life Cycle.
Note
The terms data model and database model are often used interchangeably. In this book, the term database model is used to refer to the implementation of a data model in a specific database system.
Note
An implementation-ready data model should contain at least the following components:
• A description of the data structure that will store the end-user data
• A set of enforceable rules to guarantee the integrity of the data
• A data manipulation methodology to support the real-world data transformations
Traditionally, database designers relied on good judgment to help them develop a good data model. Unfortunately, good judgment is often in the eye of the beholder, and it often develops after much trial and error. For example, if each student in this class has to create a data model for a video store, it is very likely that each will come up with a different model. Which one would be correct? The simple answer is “the one that meets all the end-user requirements,” and there may be more than one correct solution! For- tunately, database designers make use of existing data-modeling constructs and power- ful database design tools that substantially diminish the potential for errors in database modeling. In the following sections, you will learn how existing data models are used to represent real-world data and how the different degrees of data abstraction facilitate data modeling.
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2-2 The Importance of Data Models
Data models can facilitate interaction among the designer, the applications programmer, and the end user. A well-developed data model can even foster improved understanding of the organization for which the database design is developed. In short, data models are a communication tool. This important aspect of data modeling was summed up neatly by a client whose reaction was as follows: “I created this business, I worked with this business for years, and this is the first time I’ve really understood how all the pieces really fit together.”
The importance of data modeling cannot be overstated. Data constitutes the most basic information employed by a system. Applications are created to manage data and to help transform data into information, but data is viewed in different ways by different people. For example, contrast the view of a company manager with that of a company clerk. Although both work for the same company, the manager is more likely to have an enterprise-wide view of company data than the clerk.
Even different managers view data differently. For example, a company president is likely to take a universal view of the data because he or she must be able to tie the com- pany’s divisions to a common (database) vision. A purchasing manager in the same com- pany is likely to have a more restricted view of the data, as is the company’s inventory manager. In effect, each department manager works with a subset of the company’s data. The inventory manager is more concerned about inventory levels, while the purchasing manager is more concerned about the cost of items and about relationships with the suppliers of those items.
Applications programmers have yet another view of data, being more concerned with data location, formatting, and specific reporting requirements. Basically, applications programmers translate company policies and procedures from a variety of sources into appropriate interfaces, reports, and query screens.
The different users and producers of data and information often reflect the fable of the blind people and the elephant: the blind person who felt the elephant’s trunk had quite a different view from the one who felt the elephant’s leg or tail. A view of the whole ele- phant is needed. Similarly, a house is not a random collection of rooms; to build a house, a person should first have the overall view that is provided by blueprints. Likewise, a sound data environment requires an overall database blueprint based on an appropriate data model.
When a good database blueprint is available, it does not matter that an applications programmer’s view of the data is different from that of the manager or the end user. Con- versely, when a good database blueprint is not available, problems are likely to ensue. For instance, an inventory management program and an order entry system may use con- flicting product-numbering schemes, thereby costing the company thousands or even millions of dollars.
Keep in mind that a house blueprint is an abstraction; you cannot live in the blueprint. Similarly, the data model is an abstraction; you cannot draw the required data out of the data model. Just as you are not likely to build a good house without a blueprint, you are equally unlikely to create a good database without first creating an appropriate data model.
2-3 Data Model Basic Building Blocks
The basic building blocks of all data models are entities, attributes, relationships, and con-
straints. An entity is a person, place, thing, or event about which data will be collected
entity
A person, place, thing, concept, or event for which data can be stored. See also attribute.
Chapter 2
Data Models 37
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38 Part 1
Database Concepts
attribute
A characteristic of an entity or object. An attribute has a name and a data type.
relationship
An association between entities.
one-to-many (1:M or 1..*) relationship Associations among two or more entities that are used by data models.
In a 1:M relationship, one entity instance is associated with many instances of the related entity.
many-to-many (M:N or *..*) relationship Association among two or more entities in which one occurrence of an entity is associated with many occurrences of a related entity and one occurrence of the related entity is associated with many occurrences of the first entity.
one-to-one (1:1 or 1..1) relationship Associations among two or more entities that are used by data models.
In a 1:1 relationship,
one entity instance is associated with only one instance of the related entity.
constraint
A restriction placed
on data, usually expressed in the form of rules. For example,
“A student’s GPA must be between 0.00 and 4.00.” Constraints are important because they help to ensure data integrity.
and stored. An entity represents a particular type of object in the real world, which means an entity is “distinguishable”—that is, each entity occurrence is unique and distinct. For example, a CUSTOMER entity would have many distinguishable customer occurrences, such as John Smith, Pedro Dinamita, and Tom Strickland. Entities may be physical objects, such as customers or products, but entities may also be abstractions, such as flight routes or musical concerts.
An attribute is a characteristic of an entity. For example, a CUSTOMER entity would be described by attributes such as customer last name, customer first name, customer phone number, customer address, and customer credit limit. Attributes are the equiva- lent of fields in file systems.
A relationship describes an association among entities. For example, a relationship exists between customers and agents that can be described as follows: an agent can serve many customers, and each customer may be served by one agent. Data models use three types of relationships: one-to-many, many-to-many, and one-to-one. Database designers usually use the shorthand notations 1:M or 1..*, M:N or *..*, and 1:1 or 1..1, respectively. (Although the M:N notation is a standard label for the many-to-many relationship, the label M:M may also be used.) The following examples illustrate the distinctions among the three relationships.
• One-to-many (1:M or 1..*) relationship. A painter creates many different paintings, but each is painted by only one painter. Thus, the painter (the “one”) is related to the paintings (the “many”). Therefore, database designers label the relationship “PAINTER paints PAINTING” as 1:M. Note that entity names are often capitalized as a conven- tion, so they are easily identified. Similarly, a customer (the “one”) may generate many invoices, but each invoice (the “many”) is generated by only a single customer. The “CUSTOMER generates INVOICE” relationship would also be labeled 1:M.
• Many-to-many (M:N or *..*) relationship. An employee may learn many job skills, and each job skill may be learned by many employees. Database designers label the relationship “EMPLOYEE learns SKILL” as M:N. Similarly, a student can take many classes and each class can be taken by many students, thus yielding the M:N label for the relationship expressed by “STUDENT takes CLASS.”
• One-to-one (1:1 or 1..1) relationship. A retail company’s management structure may require that each of its stores be managed by a single employee. In turn, each store manager, who is an employee, manages only a single store. Therefore, the rela- tionship “EMPLOYEE manages STORE” is labeled 1:1.
The preceding discussion identified each relationship in both directions; that is, rela- tionships are bidirectional:
• •
One CUSTOMER can generate many INVOICEs.
Each of the many INVOICEs is generated by only one CUSTOMER.
A constraint is a restriction placed on the data. Constraints are important because they help to ensure data integrity. Constraints are normally expressed in the form of rules:
• An employee’s salary must have values that are between 6,000 and 350,000. • A student’s GPA must be between 0.00 and 4.00.
• Each class must have one and only one teacher.
How do you properly identify entities, attributes, relationships, and constraints? The first step is to clearly identify the business rules for the problem domain you are modeling.
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2-4 Business Rules
When database designers go about selecting or determining the entities, attributes, and relationships that will be used to build a data model, they might start by gaining a thorough understanding of what types of data exist in an organization, how the data is used, and in what time frames it is used. But such data and information do not, by themselves, yield the required understanding of the total business. From a database point of view, the collection of data becomes meaningful only when it reflects properly defined business rules. A business rule is a brief, precise, and unambiguous descrip- tion of a policy, procedure, or principle within a specific organization. In a sense, busi- ness rules are misnamed: they apply to any organization, large or small—a business, a government unit, a religious group, or a research laboratory—that stores and uses data to generate information.
Business rules derived from a detailed description of an organization’s operations help to create and enforce actions within that organization’s environment. Business rules must be rendered in writing and updated to reflect any change in the organiza- tion’s operational environment.
Properly written business rules are used to define entities, attributes, relationships, and constraints. Any time you see relationship statements such as “an agent can serve many customers, and each customer can be served by only one agent,” business rules are at work. You will see the application of business rules throughout this book, especially in the chapters devoted to data modeling and database design.
To be effective, business rules must be easy to understand and widely disseminated to ensure that every person in the organization shares a common interpretation of the rules. Business rules describe, in simple language, the main and distinguishing charac- teristics of the data as viewed by the company. Examples of business rules are as follows:
• A customer may generate many invoices.
• An invoice is generated by only one customer.
• A training session cannot be scheduled for fewer than 10 employees or for more than 30 employees.
Note that those business rules establish entities, relationships, and constraints. For example, the first two business rules establish two entities (CUSTOMER and INVOICE) and a 1:M relationship between those two entities. The third business rule estab- lishes a constraint (no fewer than 10 people and no more than 30 people), two entities (EMPLOYEE and TRAINING), and also implies a relationship between EMPLOYEE and TRAINING.
2-4a Discovering Business Rules
The main sources of business rules are company managers, policy makers, department managers, and written documentation such as a company’s procedures, standards, and operations manuals. A faster and more direct source of business rules is direct interviews with end users. Unfortunately, because perceptions differ, end users are sometimes a less reliable source when it comes to specifying business rules. For example, a maintenance department mechanic might believe that any mechanic can initiate a maintenance pro- cedure, when actually only mechanics with inspection authorization can perform such a task. Such a distinction might seem trivial, but it can have major legal consequences. Although end users are crucial contributors to the development of business rules, it pays to verify end-user perceptions. Too often, interviews with several people who perform the
business rule
A description of a policy, procedure, or principle within an organization. For example, a pilot cannot be on duty for more than 10 hours during a 24-hour period, or a professor may teach up to four classes during a semester.
Chapter 2
Data Models 39
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40 Part 1
Database Concepts
same job yield very different perceptions of what the job components are. While such a discovery may point to “management problems,” that general diagnosis does not help the database designer. The database designer’s job is to reconcile such differences and verify the results of the reconciliation to ensure that the business rules are appropriate and accurate.
The process of identifying and documenting business rules is essential to database design for several reasons:
• It helps to standardize the company’s view of data.
• It can be a communication tool between users and designers.
• It allows the designer to understand the nature, role, and scope of the data.
• It allows the designer to understand business processes.
• It allows the designer to develop appropriate relationship participation rules and constraints and to create an accurate data model.
Of course, not all business rules can be modeled. For example, a business rule that specifies “no pilot can fly more than 10 hours within any 24-hour period” cannot be modeled in the database model directly. However, such a business rule can be repre- sented and enforced by application software.
2-4b Translating Business Rules into Data Model Components
Business rules set the stage for the proper identification of entities, attributes, rela- tionships, and constraints. In the real world, names are used to identify objects. If the business environment wants to keep track of the objects, there will be specific business rules for the objects. As a general rule, a noun in a business rule will translate into an entity in the model, and a verb (active or passive) that associates the nouns will trans- late into a relationship among the entities. For example, the business rule “a customer may generate many invoices” contains two nouns (customer and invoices) and a verb (generate) that associates the nouns. From this business rule, you could deduce the following:
• Customer and invoice are objects of interest for the environment and should be repre- sented by their respective entities.
• There is a generate relationship between customer and invoice.
To properly identify the type of relationship, you should consider that relationships are bidirectional; that is, they go both ways. For example, the business rule “a cus- tomer may generate many invoices” is complemented by the business rule “an invoice is generated by only one customer.” In that case, the relationship is one-to-many (1:M). Customer is the “1” side, and invoice is the “many” side.
As a general rule, to properly identify the relationship type, you should ask two questions:
• How many instances of B are related to one instance of A?
• How many instances of A are related to one instance of B?
For example, you can assess the relationship between student and class by asking two questions:
• In how many classes can one student enroll? Answer: many classes.
• How many students can enroll in one class? Answer: many students.
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Therefore, the relationship between student and class is many-to-many (M:N). You will have many opportunities to determine the relationships between entities as you proceed through this book, and soon the process will become second nature.
2-4c Naming Conventions
During the translation of business rules to data model components, you identify entities, attributes, relationships, and constraints. This identification process includes naming the object in a way that makes it unique and distinguishable from other objects in the prob- lem domain. Therefore, it is important to pay special attention to how you name the objects you are discovering.
Entity names should be descriptive of the objects in the business environment and use terminology that is familiar to the users. An attribute name should also be descrip- tive of the data represented by that attribute. It is also a good practice to prefix the name of an attribute with the name or abbreviation of the entity in which it occurs. For example, in the CUSTOMER entity, the customer’s credit limit may be called CUS_CREDIT_LIMIT. The CUS indicates that the attribute is descriptive of the CUSTOMER entity, while CREDIT_LIMIT makes it easy to recognize the data that will be contained in the attribute. This will become increasingly important in later chapters when you learn about the need to use common attributes to specify relation- ships between entities. The use of a proper naming convention will improve the data model’s ability to facilitate communication among the designer, application program- mer, and the end user. In fact, a proper naming convention can go a long way toward making your model self-documenting.
2-5 The Evolution of Data Models
The quest for better data management has led to several models that attempt to resolve the previous model’s critical shortcomings and to provide solutions to ever-evolving data management needs. These models represent schools of thought as to what a database is, what it should do, the types of structures that it should employ, and the technology that would be used to implement these structures. Perhaps confusingly, these models are called data models, as are the graphical data models discussed earlier in this chapter. This section gives an overview of the major data models in roughly chronological order. You will discover that many of the “new” database concepts and structures bear a remarkable resemblance to some of the “old” data model concepts and structures. Table 2.1 traces the evolution of the major data models.
2-5a Hierarchical and Network Models
The hierarchical model was developed in the 1960s to manage large amounts of data for complex manufacturing projects, such as the Apollo rocket that landed on the moon in 1969. The model’s basic logical structure is represented by an upside-down tree. The hierarchical structure contains levels, or segments. A segment is the equivalent of a file system’s record type. Within the hierarchy, a higher layer is perceived as the parent of the segment directly beneath it, which is called the child. The hierarchical model depicts a set of one-to-many (1:M) relationships between a parent and its children segments. (Each parent can have many children, but each child has only one parent.)
The network model was created to represent complex data relationships more effec- tively than the hierarchical model, to improve database performance, and to impose a database standard. In the network model, the user perceives the network database as a
Chapter 2 Data Models 41 Online
Content
The hierarchical and network models are largely of historical interest, yet they do contain some ele- ments and features that interest current database profession- als. The technical details of those two models are discussed in Appendixes K and L, respectively, which are available at www. cengagebrain.com. Appendix G is devoted to the object-oriented (OO) model. However, given the dominant market presence of the relational model, most of the book focuses on the relational model.
hierarchical model
An early database model whose basic concepts and characteristics formed the basis for subsequent database development. This model is based on
an upside-down tree structure in which
each record is called a segment. The top record is the root segment. Each segment has a
1:M relationship to the segment directly below it.
segment
In the hierarchical data model, the equivalent of a file system’s record type.
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42 Part 1
Database Concepts
TABLE 2.1
EVOLUTION OF MAJOR DATA MODELS
GENERATION
TIME
DATA MODEL
EXAMPLES
COMMENTS
First
1960s–1970s
File system
VMS/VSAM
Used mainly on IBM mainframe systems Managed records, not relationships
Second
1970s
Hierarchical and network
IMS, ADABAS, IDS-II
Early database systems Navigational access
Third
Mid-1970s
Relational
DB2 Oracle
MS
SQL Server MySQL
Conceptual simplicity
Entity relationship (ER) modeling and support for relational data modeling
Fourth
Mid-1980s
Object-oriented Object/relational (O/R)
Versant Objectivity/DB DB2 UDB Oracle 12c
Object/relational supports object data types Star Schema support for data warehousing Web databases become common
Fifth
Mid-1990s
XML Hybrid DBMS
dbXML Tamino
DB2 UDB Oracle 12c
MS SQL Server
Unstructured data support
O/R model supports XML
documents
Hybrid DBMS adds object front end to relational databases
Support large databases (terabyte size)
Emerging Models: NoSQL
Early 2000s to present
Key-value store Column store
SimpleDB (Amazon) BigTable (Google) Cassandra (Apache) MongoDB
Riak
Distributed, highly scalable
High performance, fault tolerant
Very large storage (petabytes)
Suited for sparse data
Proprietary application programming interface (API)
collection of records in 1:M relationships. However, unlike the hierarchical model, the network model allows a record to have more than one parent. While the network data- base model is generally not used today, the definitions of standard database concepts that emerged with the network model are still used by modern data models:
• The schema is the conceptual organization of the entire database as viewed by the database administrator.
• The subschema defines the portion of the database “seen” by the application programs that actually produce the desired information from the data within the database.
• A data manipulation language (DML) defines the environment in which data can be managed and is used to work with the data in the database.
• A schema data definition language (DDL) enables the database administrator to define the schema components.
As information needs grew and more sophisticated databases and applications were required, the network model became too cumbersome. The lack of ad hoc query capa- bility put heavy pressure on programmers to generate the code required to produce even the simplest reports. Although the existing databases provided limited data indepen- dence, any structural change in the database could still produce havoc in all application programs that drew data from the database. Because of the disadvantages of the hierar- chical and network models, they were largely replaced by the relational data model in the 1980s.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
network model
An early data model that represented data as a collection of record types in 1:M relationships.
schema
A logical grouping of database objects, such as tables, indexes, views, and queries, that are related to each other.
subschema
The portion of the database that interacts with application programs.
data manipulation language (DML)
The set of commands that allows an end user to manipulate the data in the database, such as SELECT, INSERT, UPDATE, DELETE, COMMIT, and ROLLBACK.
Copyright 2017 Cengage Lea Editorial review has deemed that

2-5b The Relational Model
The relational model was introduced in 1970 by E. F. Codd of IBM in his landmark paper “A Relational Model of Data for Large Shared Databanks” (Communications of the ACM, June 1970, pp. 377–387). The relational model represented a major breakthrough for both users and designers. To use an analogy, the relational model produced an “auto- matic transmission” database to replace the “standard transmission” databases that pre- ceded it. Its conceptual simplicity set the stage for a genuine database revolution.
Note
The relational model’s foundation is a mathematical concept known as a relation. To avoid the complexity of abstract mathematical theory, you can think of a relation (sometimes called a table) as a two-dimensional structure composed of intersecting rows and columns. Each row in a relation is called a tuple. Each column represents an attribute. The relational model also describes a precise set of data manipulation con- structs based on advanced mathematical concepts.
In 1970, Codd’s work was considered ingenious but impractical. The relational model’s conceptual simplicity was bought at the expense of computer overhead; com- puters at that time lacked the power to implement the relational model. Fortunately, computer power grew exponentially, as did operating system efficiency. Better yet, the cost of computers diminished rapidly as their power grew. Today, even PCs, which cost a fraction of what their mainframe ancestors cost, can run sophisticated relational database software such as Oracle, DB2, Microsoft SQL Server, MySQL, and other mainframe relational software.
The relational data model is implemented through a very sophisticated relational database management system (RDBMS). The RDBMS performs the same basic func- tions provided by the hierarchical and network DBMS systems, in addition to a host of other functions that make the relational data model easier to understand and implement (as outlined in Chapter 1, in the DBMS Functions section).
Arguably the most important advantage of the RDBMS is its ability to hide the com- plexities of the relational model from the user. The RDBMS manages all of the physical details, while the user sees the relational database as a collection of tables in which data is stored. The user can manipulate and query the data in a way that seems intuitive and logical.
Tables are related to each other through the sharing of a common attribute (a value in a column). For example, the CUSTOMER table in Figure 2.1 might contain a sales agent’s number that is also contained in the AGENT table.
The common link between the CUSTOMER and AGENT tables enables you to match the customer to his or her sales agent, even though the customer data is stored in one table and the sales representative data is stored in another table. For example, you can easily determine that customer Dunne’s agent is Alex Alby because for customer Dunne, the CUSTOMER table’s AGENT_CODE is 501, which matches the AGENT table’s
data definition language (DDL)
The language that allows a database administrator to define the database structure, schema, and subschema.
relational model
Developed by E. F. Codd of IBM in 1970, the relational model is based on mathematical set theory and represents data as independent relations. Each relation (table) is conceptually represented as a two- dimensional structure
of intersecting rows and columns. The relations are related to each other through the sharing
of common entity characteristics (values in columns).
table (relation)
A logical construct perceived to be a two- dimensional structure composed of intersecting rows (entities) and columns (attributes) that represents an entity set in the relational model.
tuple
In the relational model, a table row.
relational database management system (RDBMS)
A collection of programs that manages a relational database. The RDBMS software translates a user’s logical requests (queries) into commands that physically locate and retrieve the requested data.
Chapter 2
Data Models 43
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The relational database model presented in this chapter is an introduction and an over- view. A more detailed discussion is in Chapter 3, The Relational Database Model. In fact, the relational model is so important that it will serve as the basis for discussions in most of the remaining chapters.

44
Part 1
Database Concepts
FIGURE2.1 LINKINGRELATIONALTABLES
Table name: AGENT (first six attributes)
Table name: CUSTOMER
Database name: Ch02_InsureCo
Link through AGENT_CODE
This
bases are available at www.cengagebrain .com. For example, the contents of the AGENT and CUSTOMER tables shown in Figure 2.1 are in the database named Ch02_InsureCo.
Online
Content
AGENT_CODE for Alex Alby. Although the tables are independent of one another, you can easily associate the data between tables. The relational model provides a minimum level of controlled redundancy to eliminate most of the redundancies commonly found in file systems.
The relationship type (1:1, 1:M, or M:N) is often shown in a relational schema, an example of which is shown in Figure 2.2. A relational diagram is a representation of the relational database’s entities, the attributes within those entities, and the relationships between those entities.
In Figure 2.2, the relational diagram shows the connecting fields (in this case, AGENT_CODE) and the relationship type (1:M). Microsoft Access, the database soft- ware application used to generate Figure 2.2, employs the infinity symbol (∞) to indicate the “many” side. In this example, the CUSTOMER represents the “many” side because an AGENT can have many CUSTOMERs. The AGENT represents the “1” side because each CUSTOMER has only one AGENT.
A relational table stores a collection of related entities. In this respect, the relational database table resembles a file, but there is a crucial difference between a table and a file:
chapter’s data-
FIGURE2.2 ARELATIONALDIAGRAM
relational diagram
A graphical representation of a relational database’s entities, the attributes within those entities, and the relationships among the entities.
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a table yields complete data and structural independence because it is a purely logical structure. How the data is physically stored in the database is of no concern to the user or the designer; the perception is what counts. This property of the relational data model, which is explored in depth in the next chapter, became the source of a real database revolution.
Another reason for the relational data model’s rise to dominance is its powerful and flexible query language. Most relational database software uses Structured Query Lan- guage (SQL), which allows the user to specify what must be done without specifying how. The RDBMS uses SQL to translate user queries into instructions for retrieving the requested data. SQL makes it possible to retrieve data with far less effort than any other database or file environment.
From an end-user perspective, any SQL-based relational database application involves three parts: a user interface, a set of tables stored in the database, and the SQL “engine.” Each of these parts is explained as follows:
• The end-user interface. Basically, the interface allows the end user to interact with the data (by automatically generating SQL code). Each interface is a product of the software vendor’s idea of meaningful interaction with the data. You can also design your own customized interface with the help of application generators that are now standard fare in the database software arena.
• A collection of tables stored in the database. In a relational database, all data is per- ceived to be stored in tables. The tables simply “present” the data to the end user in a way that is easy to understand. Each table is independent. Rows in different tables are related by common values in common attributes.
• SQL engine. Largely hidden from the end user, the SQL engine executes all que- ries, or data requests. Keep in mind that the SQL engine is part of the DBMS software. The end user uses SQL to create table structures and to perform data access and table maintenance. The SQL engine processes all user requests—largely behind the scenes and without the end user’s knowledge. Hence, SQL is said to be a declarative language that tells what must be done but not how. (You will learn more about the SQL engine in Chapter 11, Database Performance Tuning and Query Optimization.)
Because the RDBMS performs some tasks behind the scenes, it is not necessary to focus on the physical aspects of the database. Instead, the following chapters concentrate on the logical portion of the relational database and its design. Furthermore, SQL is cov- ered in detail in Chapter 7, Introduction to Structured Query Language (SQL), and in Chapter 8, Advanced SQL.
2-5c The Entity Relationship Model
The conceptual simplicity of relational database technology triggered the demand for RDBMSs. In turn, the rapidly increasing requirements for transaction and information created the need for more complex database implementation structures, thus creating the need for more effective database design tools. (Building a skyscraper requires more detailed design activities than building a doghouse, for example.)
Complex design activities require conceptual simplicity to yield successful results. Although the relational model was a vast improvement over the hierarchical and net- work models, it still lacked the features that would make it an effective database design tool. Because it is easier to examine structures graphically than to describe them in text,
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Chapter 2
Data Models 45

46 Part 1
Database Concepts
database designers prefer to use a graphical tool in which entities and their relationships are pictured. Thus, the entity relationship (ER) model, or ERM, has become a widely accepted standard for data modeling.
Peter Chen first introduced the ER data model in 1976; the graphical representa- tion of entities and their relationships in a database structure quickly became popu- lar because it complemented the relational data model concepts. The relational data model and ERM combined to provide the foundation for tightly structured database design. ER models are normally represented in an entity relationship diagram (ERD), which uses graphical representations to model database components. You will learn how to use ERDs to design databases in Chapter 4, Entity Relationship (ER) Modeling.
The ER model is based on the following components:
• Entity. Earlier in this chapter, an entity was defined as anything about which data will be collected and stored. An entity is represented in the ERD by a rectangle, also known as an entity box. The name of the entity, a noun, is written in the center of the rectangle. The entity name is generally written in capital letters and in singular form: PAINTER rather than PAINTERS, and EMPLOYEE rather than EMPLOYEES. Usually, when applying the ERD to the relational model, an entity is mapped to a rela- tional table. Each row in the relational table is known as an entity instance or entity occurrence in the ER model. A collection of like entities is known as an entity set. For example, you can think of the AGENT file in Figure 2.1 as a collection of three agents (entities) in the AGENT entity set. Technically speaking, the ERD depicts entity sets. Unfortunately, ERD designers use the word entity as a substitute for entity set, and this book will conform to that established practice when discussing any ERD and its components.
• Each entity consists of a set of attributes that describes particular characteristics of the entity. For example, the entity EMPLOYEE will have attributes such as a Social Security number, a last name, and a first name. (Chapter 4 explains how attributes are included in the ERD.)
• Relationships. Relationships describe associations among data. Most relationships describe associations between two entities. When the basic data model compo- nents were introduced, three types of data relationships were illustrated: one- to-many (1:M), many-to-many (M:N), and one-to-one (1:1). The ER model uses the term connectivity to label the relationship types. The name of the relation- ship is usually an active or passive verb. For example, a PAINTER paints many PAINTINGs, an EMPLOYEE learns many SKILLs, and an EMPLOYEE manages a STORE.
Figure 2.3 shows the different types of relationships using three ER notations: the original Chen notation, the Crow’s Foot notation, and the newer class diagram notation, which is part of the Unified Modeling Language (UML).
The left side of the ER diagram shows the Chen notation, based on Peter Chen’s landmark paper. In this notation, the connectivities are written next to each entity box. Relationships are represented by a diamond connected to the related entities through a relationship line. The relationship name is written inside the diamond.
The middle of Figure 2.3 illustrates the Crow’s Foot notation. The name Crow’s Foot is derived from the three-pronged symbol used to represent the “many” side of the relationship. As you examine the basic Crow’s Foot ERD in Figure 2.3, note that the connectivities are represented by symbols. For example, the “1” is represented by a short line segment, and the “M” is represented by the three-pronged “crow’s foot.” In this example, the relationship name is written above the relationship line.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
entity relationship (ER) model (ERM)
A data model that describes relationships (1:1, 1:M, and M:N) among entities at the conceptual level with the help of ER diagrams. The model was developed by
Peter Chen.
entity relationship diagram (ERD)
A diagram that depicts an entity relationship model’s entities, attributes, and relations.
entity instance (entity occurrence) A row in a relational table.
entity set
A collection of like entities.
connectivity
The type of relationship between entities. Classifications include 1:1, 1:M, and M:N.
Chen notation
See entity relationship (ER) model.
Crow’s Foot notation
A representation of
the entity relationship diagram that uses a three-pronged symbol to represent the “many” sides of the relationship.
class diagram notation
The set of symbols used in the creation of class diagrams.
Copyright 2017 Cengage Lea Editorial review has deemed that

The right side of Figure 2.3 shows the UML notation (also known as the UML class notation). Note that the connectivities are represented by lines with symbols (1..1, 1..*). Also, the UML notation uses names in both sides of the relationship. For example, to read the relationship between PAINTER and PAINTING, note the following:
• A PAINTER “paints” one to many PAINTINGs, as indicated by the 1..* symbol.
• A PAINTING is “painted by” one and only one PAINTER, as indicated by the 1..1 symbol.
Note
Many-to-many (M:N) relationships exist at a conceptual level, and you should know how to recognize them. However, you will learn in Chapter 3 that M:N relationships are not appro- priate in a relational model. For that reason, Microsoft Visio does not support the M:N rela- tionship directly. Therefore, to illustrate the existence of an M:N relationship using Visio, you have to change the line style of the connector (see Appendix A, Designing Databases with Visio Professional: A Tutorial, at www.cengagebrain.com).
In Figure 2.3, entities and relationships are shown in a horizontal format, but they may also be oriented vertically. The entity location and the order in which the entities are presented are immaterial; just remember to read a 1:M relationship from the “1” side to the “M” side.
The Crow’s Foot notation is used as the design standard in this book. However, the Chen notation is used to illustrate some of the ER modeling concepts whenever necessary. Most data modeling tools let you select the Crow’s Foot or UML class diagram notation.
Online
Content
Aside from the Chen, Crow’s Foot, and UML notations, there are other ER model nota- tions. For a summary of ER model notation sym- bols, see Appendix E, Comparison of ER Model Notations, at www. cengagebrain.com.
Chapter 2
Data Models 47
FIGURE 2.3 THE ER MODEL NOTATIONS
Chen Notation Crow’s Foot Notation UML Class Diagram Notation
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48 Part 1
Database Concepts
Online
Content
This chapter introduces only basic OO con- cepts. You can exam- ine object-orientation concepts and princi- ples in detail in Appen- dix G, Object-Oriented Databases, at www. cengagebrain.com.
object-oriented data model (OODM)
A data model whose basic modeling structure is an object.
object
An abstract representation of a real- world entity that has a unique identity, embed- ded properties, and the ability to interact with other objects and itself.
object-oriented database management system (OODBMS)
Data management software used to manage data in an object-oriented database model.
semantic data model
The first of a series of data models that more closely represented the real world, modeling both data and their relationships in a single structure known
as an object. The SDM, published in 1981, was developed by M. Hammer and D. McLeod.
class
A collection of similar objects with shared structure (attributes) and behavior (methods).
A class encapsulates
an object’s data representation and a method’s implementation. Classes are organized in a class hierarchy.
Microsoft Visio Professional software was used to generate the Crow’s Foot designs you will see in subsequent chapters.
The ER model’s exceptional visual simplicity makes it the dominant database model- ing and design tool. Nevertheless, the search for better data-modeling tools continues as the data environment continues to evolve.
2-5d The Object-Oriented (OO) Model
Increasingly complex real-world problems demonstrated a need for a data model that more closely represented the real world. In the object-oriented data model (OODM), both data and its relationships are contained in a single structure known as an object. In turn, the OODM is the basis for the object-oriented database management system (OODBMS).
An OODM reflects a very different way to define and use entities. Like the relational model’s entity, an object is described by its factual content. But, quite unlike an entity, an object includes information about relationships between the facts within the object, as well as information about its relationships with other objects. Therefore, the facts within the object are given greater meaning. The OODM is said to be a semantic data model because semantic indicates meaning.
Subsequent OODM development has allowed an object also to contain all operations that can be performed on it, such as changing its data values, finding a specific data value, and printing data values. Because objects include data, various types of relationships, and operational procedures, the object becomes self-contained, thus making it—at least potentially—a basic building block for autonomous structures.
The OO data model is based on the following components:
• An object is an abstraction of a real-world entity. In general terms, an object may be considered equivalent to an ER model’s entity. More precisely, an object represents only one occurrence of an entity. (The object’s semantic content is defined through several of the items in this list.)
• Attributes describe the properties of an object. For example, a PERSON object includes the attributes Name, Social Security Number, and Date of Birth.
• Objects that share similar characteristics are grouped in classes. A class is a collec- tion of similar objects with shared structure (attributes) and behavior (methods). In a general sense, a class resembles the ER model’s entity set. However, a class is different from an entity set in that it contains a set of procedures known as methods. A class’s method represents a real-world action such as finding a selected PERSON’s name, changing a PERSON’s name, or printing a PERSON’s address. In other words, meth- ods are the equivalent of procedures in traditional programming languages. In OO terms, methods define an object’s behavior.
• Classes are organized in a class hierarchy. The class hierarchy resembles an upside-down tree in which each class has only one parent. For example, the CUSTOMER class and the EMPLOYEE class share a parent PERSON class. (Note the similarity to the hierarchical data model in this respect.)
• Inheritance is the ability of an object within the class hierarchy to inherit the attri- butes and methods of the classes above it. For example, two classes, CUSTOMER and EMPLOYEE, can be created as subclasses from the class PERSON. In this case, CUSTOMER and EMPLOYEE will inherit all attributes and methods from PERSON.
• Object-oriented data models are typically depicted using Unified Modeling Language (UML) class diagrams. UML is a language based on OO concepts that describes a set of diagrams and symbols you can use to graphically model a system.
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UML class diagrams are used to represent data and its relationships within the larger UML object-oriented system’s modeling language. For a more complete description of UML, see Appendix H, Unified Modeling Language (UML).
To illustrate the main concepts of the object-oriented data model, consider a simple invoicing problem. In this case, invoices are generated by customers, each invoice ref- erences one or more lines, and each line represents an item purchased by a customer. Figure 2.4 illustrates the object representation for this simple invoicing problem, as well as the equivalent UML class diagram and ER model. The object representation is a simple way to visualize a single object occurrence.
Chapter 2
Data Models 49
method
In the object-oriented data model, a named set of instructions to perform an action. Methods represent real-world actions, and are invoked through messages.
FIGURE2.4 ACOMPARISONOFOO,UMLANDERMODELS
Object Representation UML Class Diagram ER Model
As you examine Figure 2.4, note the following:
• The object representation of the INVOICE includes all related objects within the same object box. Note that the connectivities (1 and M) indicate the relationship of the related objects to the INVOICE. For example, the “1” next to the CUSTOMER object indicates that each INVOICE is related to only one CUSTOMER. The “M” next to the LINE object indicates that each INVOICE contains many LINEs.
• The UML class diagram uses three separate object classes (CUSTOMER, INVOICE, and LINE) and two relationships to represent this simple invoicing problem. Note that the relationship connectivities are represented by the 1..1, 0..*, and 1..* symbols, and that the relationships are named in both ends to represent the different “roles” that the objects play in the relationship.
• The ER model also uses three separate entities and two relationships to represent this simple invoice problem.
The OODM advances influenced many areas, from system modeling to program- ming. (Most contemporary programming languages have adopted OO concepts, includ- ing Java, Ruby, Perl, C#, and Visual Studio .NET.) The added semantics of the OODM allowed for a richer representation of complex objects. This in turn enabled applications to support increasingly complex objects in innovative ways. As you will see in the next section, such evolutionary advances also affected the relational model.
2-5e Object/Relational and XML
Facing the demand to support more complex data representations, the relational
model’s main vendors evolved the model further and created the extended
class hierarchy
The organization of classes in a hierarchical tree in which each parent class is a superclass and each child class is a subclass. See also inheritance.
inheritance
In the object-oriented data model, the ability of an object to inherit the data structure and methods of the classes above it in the class hierarchy. See also class hierarchy.
Unified Modeling Language (UML)
A language based on object-oriented concepts that provides tools such as diagrams and symbols to graphically model a system.
class diagram
A diagram used to represent data and their relationships in UML object notation.
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50 Part 1
Database Concepts
extended relational data model (ERDM) A model that includes the object-oriented model’s best features in an inherently simpler relational database structural environment. See extended entity relationship model (EERM).
object/relational database management system (O/R DBMS)
A DBMS based on the extended relational model (ERDM). The ERDM, championed
by many relational database researchers, constitutes the relational model’s response to
the OODM. This model includes many of the object-oriented model’s best features within
an inherently simpler relational database structure.
Extensible Markup Language (XML)
A metalanguage
used to represent
and manipulate data elements. Unlike other markup languages, XML permits the manipulation of a document’s data elements. XML facilitates the exchange of structured documents such as orders and invoices over the Internet.
relational data model (ERDM). The ERDM adds many of the OO model’s features within the inherently simpler relational database structure. The ERDM gave birth to a new generation of relational databases that support OO features such as objects (encapsulated data and methods), extensible data types based on classes, and inher- itance. That’s why a DBMS based on the ERDM is often described as an object/ relational database management system (O/R DBMS).
Today, most relational database products can be classified as object/relational, and they represent the dominant market share of OLTP and OLAP database applications. The success of the O/R DBMSs can be attributed to the model’s conceptual simplicity, data integrity, easy-to-use query language, high transaction performance, high availabil- ity, security, scalability, and expandability. In contrast, the OO DBMS is popular in niche markets such as computer-aided drawing/computer-aided manufacturing (CAD/CAM), geographic information systems (GIS), telecommunications, and multimedia, which require support for more complex objects.
From the start, the OO and relational data models were developed in response to different problems. The OO data model was created to address very specific engineer- ing needs, not the wide-ranging needs of general data management tasks. The relational model was created with a focus on better data management based on a sound mathemat- ical foundation. Given its focus on a smaller set of problem areas, it is not surprising that the OO market has not grown as rapidly as the relational data model market.
The use of complex objects received a boost with the Internet revolution. When orga- nizations integrated their business models with the Internet, they realized its potential to access, distribute, and exchange critical business information. This resulted in the widespread adoption of the Internet as a business communication tool. Within this environment, Extensible Markup Language (XML) emerged as the de facto standard for the efficient and effective exchange of structured, semistructured, and unstructured data. Organizations that used XML data soon realized that they needed to manage large amounts of unstructured data such as word-processing documents, webpages, emails, and diagrams. To address this need, XML databases emerged to manage unstructured data within a native XML format. (See Chapter 15, Database Connectivity and Web Tech- nologies, for more information about XML.) At the same time, O/R DBMSs added sup- port for XML-based documents within their relational data structure. Due to its robust foundation in broadly applicable principles, the relational model is easily extended to include new classes of capabilities, such as objects and XML.
Although relational and object/relational databases address most current data pro- cessing needs, a new generation of databases has emerged to address some very specific challenges found in some Internet-era organizations.
2-5f Emerging Data Models: Big Data and NoSQL
Deriving usable business information from the mountains of web data that organizations have accumulated over the years has become an imperative need. Web data in the form of browsing patterns, purchasing histories, customer preferences, behavior patterns, and social media data from sources such as Facebook, Twitter, and LinkedIn have inundated organizations with combinations of structured and unstructured data. In addition, mobile technologies such as smartphones and tablets, plus sensors of all types—GPS, RFID sys- tems, weather sensors, biomedical devices, space research probes, car and aviation black boxes—as well as other Internet and cellular-connected devices, have created new ways to automatically collect massive amounts data in multiple formats (text, pictures, sound, video, etc.). The amount of data being collected grows exponentially every day. According to IBM, “Every day we create 2.5 quintillion bytes of data—so much that 90 percent of the
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data in the world today has been created in the last two years alone.”1 According to some studies, the rapid pace of data growth is the top challenge for organizations,2 with system performance and scalability as the next biggest challenges. Today’s information technology (IT) managers are constantly balancing the need to manage this rapidly growing data with shrinking budgets. The need to manage and leverage all these converging trends (rapid data growth, performance, scalability, and lower costs) has triggered a phenomenon called “Big Data.” Big Data refers to a movement to find new and better ways to manage large amounts of web and sensor-generated data and derive business insight from it, while simultaneously providing high performance and scalability at a reasonable cost.
The term Big Data has been used in many different frameworks, from law to statis- tics to economics to computing. The term seems to have been first used in a computing framework by John Mashey, a Silicon Graphics scientist in the 1990s.3 However, it seems to be Douglas Laney, a data analyst from the Gartner Group, who first described the basic characteristics of Big Data databases:4 volume, velocity, and variety, or the 3 Vs.
• Volume refers to the amounts of data being stored. With the adoption and growth of the Internet and social media, companies have multiplied the ways to reach custom- ers. Over the years, and with the benefit of technological advances, data for millions of e-transactions were being stored daily on company databases. Furthermore, orga- nizations are using multiple technologies to interact with end users and those tech- nologies are generating mountains of data. This ever-growing volume of data quickly reached petabytes in size, and it’s still growing.
• Velocity refers not only to the speed with which data grows but also to the need to process this data quickly in order to generate information and insight. With the advent of the Internet and social media, business response times have shrunk considerably. Organiza- tions need not only to store large volumes of quickly accumulating data, but also need to process such data quickly. The velocity of data growth is also due to the increase in the number of different data streams from which data is being piped to the organization (via the web, e-commerce, Tweets, Facebook posts, emails, sensors, GPS, and so on).
• Variety refers to the fact that the data being collected comes in multiple different data formats. A great portion of these data comes in formats not suitable to be handled by the typical operational databases based on the relational model.
The 3 Vs framework illustrates what companies now know, that the amount of data being collected in their databases has been growing exponentially in size and complexity. Traditional relational databases are good at managing structured data but are not well suited to managing and processing the amounts and types of data being collected in today’s business environment.
The problem is that the relational approach does not always match the needs of orga- nizations with Big Data challenges.
• It is not always possible to fit unstructured, social media and sensor-generated data into the conventional relational structure of rows and columns.
• Adding millions of rows of multiformat (structured and nonstructured) data on a daily basis will inevitably lead to the need for more storage, processing power, and
1 IBM, “What is big data? Bringing big data to the enterprise,” http://www-01.ibm.com/software/data/ bigdata/, accessed April 2013.
2 “Gartner survey shows data growth as the largest data center infrastructure challenge,” www.gartner.com/ it/page.jsp?id=1460213, accessed March 2015.
3 Steve Lohr, “The origins of ‘Big Data’: An etymological detective story,” New York Times, February 1, 2013.
4 Douglas Laney, “3D data management controlling data volume, velocity and variety,” META Group,
February 6, 2011.
Big Data
A movement to find new and better ways to manage large amounts of web-generated data and derive business insight from it, while simultaneously providing high performance
and scalability at a reasonable cost.
3Vs
Three basic characteristics of Big Data databases: volume, velocity, and variety.
Chapter 2
Data Models 51
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52 Part 1
Database Concepts
Hadoop
A Java based, open source, high speed, fault-tolerant distributed storage and com- putational framework. Hadoop uses low-cost hardware to create clusters of thousands of computer nodes to store and process data.
Hadoop Distributed File System (HDFS)
A highly distributed, fault-tolerant file storage system designed to manage large amounts of data at high speeds.
name node
One of three types
of nodes used in the Hadoop Distributed
File System (HDFS). The name node stores all the metadata about the file system. See also client node and data node.
data node
One of three types
of nodes used in the Hadoop Distributed File System (HDFS). The data node stores fixed-size data blocks (that could be replicated to other da-ta nodes). See also client node and name node.
sophisticated data analysis tools that may not be available in the relational environ- ment. Generally speaking, the type of high-volume implementations required in the RDBMS environment for the Big Data problem comes with a hefty price tag for expanding hardware, storage, and software licenses.
• Data analysis based on OLAP tools has proven to be very successful in relational environments with highly structured data. However, mining for usable data in the vast amounts of unstructured data collected from web sources requires a different approach.
There is no “one-size-fits-all” cure to data management needs (although many estab- lished database vendors will probably try to sell you on the idea). For some organizations, creating a highly scalable, fault-tolerant infrastructure for Big Data analysis could prove to be a matter of business survival. The business world has many examples of companies that leverage technology to gain a competitive advantage, and others that miss it. Just ask yourself how the business landscape would be different if:
• Blackberry had responded quickly to the emerging Apple smartphone technology.
• MySpace had responded to Facebook’s challenge in time.
• Blockbuster had reacted to the Netflix business model sooner.
• Barnes & Noble had developed a viable Internet strategy before Amazon.
Will broadcast television networks be able to adapt to streaming services such as Hulu, AppleTV, and Roku? Will traditional news outlets be able to adapt to the changing news consumption patterns of the millennial generation?
Big Data analytics are being used to create new types of services by all types of com- panies. For example: TXU Energy,5 a Texas electricity provider, and OPower,6 a service company that provides managed solutions for utility providers, are using Big Data and emerging technologies to reduce consumption and provide energy savings to their cus- tomers. Their data comes from multiple sources (intelligent sensors, weather feeds, demographics data banks, public sector data, and geographical data), and it is being used to create value for both companies and customers.
In order to create value from their previously unused Big Data stores, companies are using new Big Data technologies. These emerging technologies allow organizations to process massive data stores of multiple formats in cost-effective ways. Some of the most frequently used Big Data technologies are Hadoop, MapReduce, and NoSQL databases.
• Hadoop is a Java based, open source, high speed, fault-tolerant distributed storage and computational framework. Hadoop uses low-cost hardware to create clusters of thousands of computer nodes to store and process data. Hadoop originated from Google’s work on distributed file systems and parallel processing and is currently sup- ported by the Apache Software Foundation.7 Hadoop has several modules, but the two main components are Hadoop Distributed File System (HDFS) and MapReduce.
• Hadoop Distributed File System (HDFS) is a highly distributed, fault-tolerant file storage system designed to manage large amounts of data at high speeds. In order to achieve high throughput, HDFS uses the write-once, read many model. This means that once the data is written, it cannot be modified. HDFS uses three types of nodes: a name node that stores all the metadata about the file system, a data node that
5 Harish Kotadia, “4 excellent big data case studies,” http://hkotadia.com/archives/5021, July 22, 2012.
6 Katie Fehrenbacher, “How big data can curb the world’s energy consumption,” http://gigaom.
com/2012/03/11/10-ways-big-data-is-changing-everything/3/ 7 For more information about Hadoop visit hadoop.apache.org.
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stores fixed-size data blocks (that could be replicated to other data nodes), and a client node that acts as the interface between the user application and the HDFS.
• MapReduce is an open source application programming interface (API) that pro- vides fast data analytics services. MapReduce distributes the processing of the data among thousands of nodes in parallel. MapReduce works with structured and non- structured data. The MapReduce framework provides two main functions, Map and Reduce. In general terms, the Map function takes a job and divides it into smaller units of work; the Reduce function collects all the output results generated from the nodes and integrates them into a single result set.
• NoSQL is a large-scale distributed database system that stores structured and unstruc- tured data in efficient ways. NoSQL databases are discussed in more detail later in this section.
Hadoop technologies provide a framework for Big Data analytics in which data (structured or unstructured) is distributed, replicated, and processed in parallel using a network of low-cost commodity hardware. Hadoop introduced new ways to store and manage data and Hadoop-related technologies gave rise to a new generation of database systems. NoSQL databases provide distributed, fault-tolerant databases for processing nonstructured data.
With the potential of big gains derived from Big Data analytics, it is not surprising that some organizations are turning to emerging Big Data technologies, such as NoSQL databases, to mine the wealth of information hidden in mountains of web data and gain a competitive advantage.
Note
Chapter 2
Data Models 53
NoSQL Databases Every time you search for a product on Amazon, send messages to friends in Facebook, watch a video on YouTube, or search for directions in Google Maps, you are using a NoSQL database. As with any new technology, the term NoSQL can be loosely applied to many different types of technologies. However, this chapter uses NoSQL to refer to a new generation of databases that address the specific challenges of the Big Data era and have the following general characteristics:
• They are not based on the relational model and SQL, hence the name NoSQL.
• They support distributed database architectures.
• They provide high scalability, high availability, and fault tolerance.
• They support very large amounts of sparse data.
• They are geared toward performance rather than transaction consistency.
Let’s examine these characteristics in more detail.
NoSQL databases are not based on the relational model. In fact, there is no standard NoSQL data model. To the contrary, many different data models are grouped under the
client node
One of three types
of nodes used in the Hadoop Distributed
File System (HDFS). The client node acts as the interface between the user application and the HDFS. See also name node and data node.
MapReduce
An open-source application programming interface (API) that provides fast data analytics services; one of the main Big Data technologies that allows organizations to process massive data stores.
NoSQL
A new generation of database management systems that is not based on the traditional relational database model.
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Does this mean that relational databases don’t have a place in organizations with Big Data challenges? No, relational databases remain the preferred and dominant databases to sup- port most day-to-day transactions and structured data analytics needs. Each DBMS tech- nology has its areas of application, and the best approach is to use the best tool for the job. In perspective, object/relational databases serve 98 percent of operational market needs. For Big Data needs, Hadoop, MapReduce, and NoSQL databases are the options.
Chapter 14, Big Data Analytics and NoSQL, discusses these options in greater detail.

54 Part 1
Database Concepts
NoSQL umbrella, from document databases to graph stores, column stores, and key-value stores. It is still too early to know which, if any, of these data models will survive and grow to become a dominant force in the database arena. However, the early success of prod- ucts such as Amazon’s SimpleDB, Google’s BigTable, and Apache’s Cassandra points to the key-value stores and column stores as the early leaders. The word stores indicates that these data models permanently store data in secondary storage, just like any other database. This added emphasis comes from the fact that these data models originated from programming languages (such as LISP), in which in-memory arrays of values are used to hold data.
The key-value data model is based on a structure composed of two data elements: a key and a value, in which every key has a corresponding value or set of values. The key- value data model is also referred to as the attribute-value or associative data model. To better understand the key-value model, look at the simple example in Figure 2.5.
Figure 2.5 shows the example of a small truck-driving company called Trucks-R-Us. Each of the three drivers has one or more certifications and other general information. Using this example, we can draw the following important points:
FIGURE2.5 ASIMPLEKEY-VALUEREPRESENTATION
Data stored using traditional relational model
Data stored using key-value model
Trucks-R-Us
• In the relational model:
• Each row represents one entity instance.
• Each column represents one attribute of the entity. • The values in a column are of the same data type.
• In the key-value model:
• Each row represents one attribute/value of one entity
instance.
• The “key” column could represent any entity’s attribute.
• The values in the “value” column could be of any data
type and therefore it is generally assigned a long string data type.
Driver 2732
key-value
A data model based on a structure composed of two data elements:
a key and a value, in which every key has a corresponding value or set of values. The key- value data model is also called the associative
or attribute-value data model.
• •
• •
•
In the relational model, every row represents a single entity occurrence and every column represents an attribute of the entity occurrence. Each column has a defined data type.
In the key-value data model, each row represents one attribute of one entity instance. The “key” column points to an attribute, and the “value” column contains the actual value for the attribute.
The data type of the “value” column is generally a long string to accommodate the variety of actual data types of the values placed in the column.
To add a new entity attribute in the relational model, you need to modify the table definition. To add a new attribute in the key-value store, you add a row to the key-value store, which is why it is said to be “schema-less.”
NoSQL databases do not store or enforce relationships among entities. The program- mer is required to manage the relationships in the program code. Furthermore, all data and integrity validations must be done in the program code (although some implementations have been expanded to support metadata).
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• NoSQL databases use their own native application programming interface (API) with simple data access commands, such as put, read, and delete. Because there is no declarative SQL-like syntax to retrieve data, the program code must take care of retrieving related data in the correct way.
• Indexing and searches can be difficult. Because the “value” column in the key-value data model could contain many different data types, it is often difficult to create indexes on the data. At the same time, searches can become very complex.
As a matter of fact, you could use the key-value structure as a general data modeling technique when attributes are numerous but actual data values are scarce. The key-value data model is not exclusive to NoSQL databases; actually, key-value data structures could reside inside a relational database. However, because of the problems with maintaining relationships and integrity within the data, and the increased complexity of even simple queries, key-value structures would be a poor design for most structured business data.
Several NoSQL database implementations, such as Google’s BigTable and Apache’s Cassandra, have extended the key-value data model to group multiple key-value sets into column families or column stores. In addition, such implementations support features such as versioning using a date/time stamp. For example, BigTable stores data in the syntax of [row, column, time, value], where row, column, and value are string data types, and time is a date/time data type. The key used to access the data is composed of (row, column, time), where time can be left blank to indicate the most recent stored value.
NoSQL supports distributed database architecture. One of the big advantages of NoSQL databases is that they generally use a distributed architecture. In fact, several of them (Cassandra and BigTable, for example) are designed to use low-cost commodity serv- ers to form a complex network of distributed database nodes. Remember that several NoSQL databases originated in the research labs of some of the most successful web companies, and most started on very small budgets!
NoSQL supports very large amounts of sparse data. NoSQL databases can handle very high volumes of data. In particular, they are suited for sparse data—that is, for cases in which the number of attributes is very large but the number of actual data instances is low. Using the preceding example, drivers can take any certification exam, but they are not required to take all. In this case, if there are three drivers and three possible certifi- cates for each driver, there will be nine possible data points. In practice, however, there are only four data instances. Now extrapolate this example for the case of a clinic with 15,000 patients and more than 500 possible tests, remembering that each patient can take a few tests but is not required to take all.
NoSQL provides high scalability, high availability, and fault tolerance. True to its web origins, NoSQL databases are designed to support web operations, such as the ability to add capacity in the form of nodes to the distributed database when the demand is high, and to do it transparently and without downtime. Fault tolerance means that if one of the nodes in the distributed database fails, it will keep operating as normal.
Most NoSQL databases are geared toward performance rather than transaction consis- tency. One of the biggest problems of very large distributed databases is enforcing data consistency. Distributed databases automatically make copies of data elements at multi- ple nodes to ensure high availability and fault tolerance. If the node with the requested data goes down, the request can be served from any other node with a copy of the data. However, what happens if the network goes down during a data update? In a relational database, transaction updates are guaranteed to be consistent or the transaction is rolled back. NoSQL databases sacrifice consistency to attain high levels of performance. (See Chapter 14, Big Data Analytics and NoSQL, to learn more about this topic.) Some NoSQL databases provide a feature called eventual consistency, which means that updates to the database will propagate through the system and eventually all data copies will be
sparse data
A case in which the number of table attributes is very large but the number of actual data instances is low.
eventual consistency
A model for database consistency in which updates to the database will propagate through the system so that all data copies will be consistent eventually.
Chapter 2
Data Models 55
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56 Part 1
Database Concepts
consistent. With eventual consistency, data is not guaranteed to be consistent across all copies of the data immediately after an update.
NoSQL is one of the hottest items in database technologies today. But it is only one of many emerging trends in data management. Whatever database technology you use, you need to be able to select the best tool for the job by understanding the pros and cons of each technology. The following section briefly summarizes the evolution of data models and provides some advantages and disadvantages of each.
2-5g Data Models: A Summary
The evolution of DBMSs has always been driven by the search for new ways of modeling and managing increasingly complex real-world data. A summary of the most commonly recognized data models is shown in Figure 2.6.
FIGURE 2.6 THE EVOLUTION OF DATA MODELS
Semantics in Data Model
least
1960
1969
1970
1976
1978 1985
Hierarchical Network Relational
Entity Relationship Semantic
1990
Comments
• Difficult to represent M:N relationships (hierarchical only)
• Structural level dependency
• No ad hoc queries (record-at-a-time access) • Access path predefined (navigational access)
• Conceptual simplicity (structural independence) • Provides ad hoc queries (SQL)
• Set-oriented access
• Easy to understand (more semantics) • Limited to conceptual modeling
(no implementation component)
1983 Internet is born
most
Object-Oriented
2009
Big Data
Extended Relational (O/R DBMS)
• More semantics in data model • Support for complex objects • Inheritance (class hierarchy) • Behavior
• Unstructured data (XML) • XML data exchanges
NoSQL
• Addresses Big Data problem
• Less semantics in data model
• Based on schema-less key-value data model • Best suited for large sparse data stores
In the evolution of data models, some common characteristics have made them widely accepted:
• A data model must show some degree of conceptual simplicity without compro- mising the semantic completeness of the database. It does not make sense to have a data model that is more difficult to conceptualize than the real world. At the same time, the model should show clarity and relevance; that is, the data model should be unambiguous and applicable to the problem domain. A data model must repre- sent the real world as closely as possible. This goal is more easily realized by adding
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more semantics to the model’s data representation. (Semantics concern dynamic data behavior, while data representation constitutes the static aspect of the real- world scenario.) In other words, the model should be accurate and complete—all the needed data is included and properly described.
• Representation of the real-world transformations (behavior) must be in compliance with the consistency and integrity characteristics required by the intended use of the data model.
Each new data model addresses the shortcomings of previous models. The network model replaced the hierarchical model because the former made it much easier to repre- sent complex (many-to-many) relationships. In turn, the relational model offers several advantages over the hierarchical and network models through its simpler data repre- sentation, superior data independence, and easy-to-use query language; these features made it the preferred data model for business applications. The OO data model intro- duced support for complex data within a rich semantic framework. The ERDM added many OO features to the relational model and allowed it to maintain strong market share within the business environment. In recent years, the Big Data phenomenon has stim- ulated the development of alternative ways to model, store, and manage data that rep- resents a break with traditional data management.
It is important to note that not all data models are created equal; some data models are better suited than others for some tasks. For example, conceptual models are better suited for high-level data modeling, while implementation models are better for manag- ing stored data for implementation purposes. The entity relationship model is an exam- ple of a conceptual model, while the hierarchical and network models are examples of implementation models. At the same time, some models, such as the relational model and the OODM, could be used as both conceptual and implementation models. Table 2.2 summarizes the advantages and disadvantages of the various database models.
Note
Thus far, you have been introduced to the basic constructs of the more prominent data models. Each model uses such constructs to capture the meaning of the real-world data environment. Table 2.3 shows the basic terminology used by the various data models.
2-6 Degrees of Data Abstraction
If you ask 10 database designers what a data model is, you will end up with 10 different answers—depending on the degree of data abstraction. To illustrate the meaning of data abstraction, consider the example of automotive design. A car designer begins by draw- ing the concept of the car to be produced. Next, engineers design the details that help transfer the basic concept into a structure that can be produced. Finally, the engineering drawings are translated into production specifications to be used on the factory floor. As you can see, the process of producing the car begins at a high level of abstraction and proceeds to an ever-increasing level of detail. The factory floor process cannot proceed unless the engineering details are properly specified, and the engineering details cannot
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Chapter 2
Data Models 57
All databases assume the use of a common data pool within the database. Therefore, all database models promote data sharing, thus reducing the potential problem of islands of information.

58 Part 1 Database Concepts
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TABLE 2.2
ADVANTAGES AND DISADVANTAGES OF VARIOUS DATABASE MODELS
DATA MODEL
DATA STRUCTURAL INDEPENDENCE INDEPENDENCE
ADVANTAGES
DISADVANTAGES
Hierarchical
Yes No
1. It promotes data sharing.
2. Parent/child relationship promotes conceptual simplicity. 3. Database security is provided and enforced by DBMS.
4. Parent/child relationship promotes data integrity.
5. It is efficient with 1:M relationships.
1. Complex implementation requires knowledge of physical data storage characteristics.
Network
Yes No
1. Conceptual simplicity is at least equal to that of the hierarchical model.
1. System complexity limits efficiency—still a navigational system. 2. Navigational system yields complex implementation, application
Relational
Yes Yes
1. Structural independence is promoted by the use of independent tables. Changes in a table’s structure do not affect data access or application programs.
1. The RDBMS requires substantial hardware and system software overhead.
2. Conceptual simplicity gives relatively untrained people the tools to use a good
Entity relationship
Yes Yes
1. Visual modeling yields exceptional conceptual simplicity. 2. Visual representation makes it an effective communication
1. There is limited constraint representation.
2. There is limited relationship representation.
3. There is no data manipulation language.
4. Loss of information content occurs when attributes are removed from entities
Object- oriented
Yes Yes
1. Semantic content is added.
2. Visual representation includes semantic content. 3. Inheritance promotes data integrity.
1. Slow development of standards caused vendors to supply their own enhancements, thus eliminating a widely accepted standard.
NoSQL
Yes Yes
1. High scalability, availability, and fault tolerance are provided. 2. It uses low-cost commodity hardware.
3. It supports Big Data.
4. Key-value model improves storage efficiency.
1. Complex programming is required.
2. There is no relationship support—only by application code.
3. There is no transaction integrity support.
4. In terms of data consistency, it provides an eventually consistent model.
2. It handles more relationship types, such as M:N and multiparent.
development, and management.
3. Structural changes require changes in all application programs.
3. Data access is more flexible than in hierarchical and file system models.
4. Data owner/member relationship promotes data integrity. 5. There is conformance to standards.
6. It includes data definition language (DDL) and data
manipulation language (DML) in DBMS.
2. Tabular view substantially improves conceptual simplicity, thereby promoting easier database design, implementation, management, and use.
found in file systems.
3. It may promote islands of information problems as individuals and
3. Ad hoc query capability is based on SQL.
4. Powerful RDBMS isolates the end user from physical-level
departments can easily develop their own applications.
details and improves implementation and management simplicity.
tool.
3. It is integrated with the dominant relational model.
to avoid crowded displays. (This limitation has been addressed in subsequent graphical versions.)
2. Navigational system yields complex application development, management, and use; requires knowledge of hierarchical path.
3. Changes in structure require changes in all application programs.
4. There are implementation limitations (no multiparent or M:N relationships). 5. There is no data definition or data manipulation language in the DBMS.
6. There is a lack of standards.
system poorly, and if unchecked, it may produce the same data anomalies
2. It is a complex navigational system.
3. There is a steep learning curve.
4. High system overhead slows transactions.

Chapter 2
Data Models 59
TABLE 2.3
DATA MODEL BASIC TERMINOLOGY COMPARISON
REAL WORLD
EXAMPLE
FILE PROCESSING
HIERARCHICAL MODEL
NETWORK MODEL
RELATIONAL MODEL
ER MODEL
OO MODEL
A group of vendors
Vendor file cabinet
File
Segment type
Record type
Table
Entity set
Class
A single vendor
Global supplies
Record
Segment occurrence
Current record
Row (tuple)
Entity occurrence
Object instance
The contact name
Johnny Ventura
Field
Segment field
Record field
Table attribute
Entity attribute
Object attribute
The vendor identifier
G12987
Index
Sequence field
Record key
Key
Entity identifier
Object identifier
Note: For additional information about the terms used in this table, consult the corresponding chapters and online appendixes that accompany this book. For example, if you want to know more about the OO model, refer to Appendix G, Object-Oriented Databases.
exist without the basic conceptual framework created by the designer. Designing a usable database follows the same basic process. That is, a database designer starts with an abstract view of the overall data environment and adds details as the design comes closer to implementation. Using levels of abstraction can also be very helpful in integrating multiple (and sometimes conflicting) views of data at different levels of an organization.
In the early 1970s, the American National Standards Institute (ANSI) Standards Planning and Requirements Committee (SPARC) defined a framework for data mod- eling based on degrees of data abstraction. The resulting ANSI/SPARC architecture defines three levels of data abstraction: external, conceptual, and internal. You can use this framework to better understand database models, as shown in Figure 2.7. In the figure, the ANSI/SPARC framework has been expanded with the addition of a physical model to explicitly address physical-level implementation details of the internal model.
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American National Standards Institute (ANSI)
The group that accepted the DBTG recommendations and augmented database standards in 1975
through its SPARC committee.
FIGURE2.7 DATAABSTRACTIONLEVELS
End-User View
External Model
Logical independence
Physical independence
End-User View
External Model
Conceptual
Model View
Internal DBMS Model View
Physical Model
Degree of Abstraction
Characteristics
Hardware-independent Software-independent
Hardware-independent Software-dependent
Hardware-dependent Software-dependent
Designer’s
High
Medium
Low
ER Object-Oriented
Relational
Network Hierarchical

60 Part 1
Database Concepts
2-6a The External Model
The external model is the end users’ view of the data environment. The term end users refers to people who use the application programs to manipulate the data and generate information. End users usually operate in an environment in which an application has a specific business unit focus. Companies are generally divided into several business units, such as sales, finance, and marketing. Each business unit is subject to specific constraints and requirements, and each one uses a subset of the overall data in the organization. Therefore, end users within those business units view their data subsets as separate from or external to other units within the organization.
Because data is being modeled, ER diagrams will be used to represent the external views. A specific representation of an external view is known as an external schema. To illustrate the external model’s view, examine the data environment of Tiny College.
Figure 2.8 presents the external schemas for two Tiny College business units: student registration and class scheduling. Each external schema includes the appropriate entities, relationships, processes, and constraints imposed by the business unit. Also note that although the application views are isolated from each other, each view shares a common entity with the other view. For example, the registration and scheduling external schemas share the entities CLASS and COURSE.
external model
The application programmer’s view of the data environment. Given its business focus, an external model works with a data subset of the global database schema.
external schema
The specific representation of an external view; the end user’s view of the data environment.
FIGURE2.8 EXTERNALMODELSFORTINYCOLLEGE
Note the entity relationships represented in Figure 2.8:
• A PROFESSOR may teach many CLASSes, and each CLASS is taught by only one PROFESSOR; there is a 1:M relationship between PROFESSOR and CLASS.
• A CLASS may ENROLL many students, and each STUDENT may ENROLL in many CLASSes, thus creating an M:N relationship between STUDENT and CLASS. (You will learn about the precise nature of the ENROLL entity in Chapter 4.)
• Each COURSE may generate many CLASSes, but each CLASS references a single COURSE. For example, there may be several classes (sections) of a database course that have a course code of CIS-420. One of those classes might be offered on MWF
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from 8:00 a.m. to 8:50 a.m., another might be offered on MWF from 1:00 p.m. to 1:50 p.m., while a third might be offered on Thursdays from 6:00 p.m. to 8:40 p.m. Yet, all three classes have the course code CIS-420.
• Finally, a CLASS requires one ROOM, but a ROOM may be scheduled for many CLASSes. That is, each classroom may be used for several classes: one at 9:00 a.m., one at 11:00 a.m., and one at 1:00 p.m., for example. In other words, there is a 1:M relationship between ROOM and CLASS.
The use of external views that represent subsets of the database has some important advantages:
• It is easy to identify specific data required to support each business unit’s operations.
• It makes the designer’s job easy by providing feedback about the model’s adequacy. Specifically, the model can be checked to ensure that it supports all processes as defined by their external models, as well as all operational requirements and constraints.
• It helps to ensure security constraints in the database design. Damaging an entire database is more difficult when each business unit works with only a subset of data.
• It makes application program development much simpler. 2-6b The Conceptual Model
The conceptual model represents a global view of the entire database by the entire orga- nization. That is, the conceptual model integrates all external views (entities, relationships, constraints, and processes) into a single global view of the data in the enterprise, as shown in Figure 2.9. Also known as a conceptual schema, it is the basis for the identification and high-level description of the main data objects (avoiding any database model-specific details).
The most widely used conceptual model is the ER model. Remember that the ER model is illustrated with the help of the ERD, which is effectively the basic database blue- print. The ERD is used to graphically represent the conceptual schema.
The conceptual model yields some important advantages. First, it provides a bird’s- eye (macro level) view of the data environment that is relatively easy to understand. For example, you can get a summary of Tiny College’s data environment by examining the conceptual model in Figure 2.9.
Chapter 2
Data Models 61
FIGURE2.9 CONCEPTUALMODELFORTINYCOLLEGE
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conceptual model
The output of the conceptual design process. The conceptual model provides a global view of an entire database and describes the main data objects, avoiding details.
conceptual schema
A representation of the conceptual model, usually expressed graphically. See also conceptual model.

62 Part 1
Database Concepts
Second, the conceptual model is independent of both software and hardware. Soft- ware independence means that the model does not depend on the DBMS software used to implement the model. Hardware independence means that the model does not depend on the hardware used in the implementation of the model. Therefore, changes in either the hardware or the DBMS software will have no effect on the database design at the conceptual level. Generally, the term logical design refers to the task of creating a conceptual data model that could be implemented in any DBMS.
2-6c The Internal Model
Once a specific DBMS has been selected, the internal model maps the conceptual model to the DBMS. The internal model is the representation of the database as “seen” by the DBMS. In other words, the internal model requires the designer to match the conceptual model’s characteristics and constraints to those of the selected implementation model. An internal schema depicts a specific representation of an internal model, using the database constructs supported by the chosen database.
Because this book focuses on the relational model, a relational database was chosen to implement the internal model. Therefore, the internal schema should map the concep- tual model to the relational model constructs. In particular, the entities in the concep- tual model are mapped to tables in the relational model. Likewise, because a relational database has been selected, the internal schema is expressed using SQL, the standard language for relational databases. In the case of the conceptual model for Tiny College depicted in Figure 2.9, the internal model was implemented by creating the tables PRO- FESSOR, COURSE, CLASS, STUDENT, ENROLL, and ROOM. A simplified version of the internal model for Tiny College is shown in Figure 2.10.
The development of a detailed internal model is especially important to database designers who work with hierarchical or network models because those models require
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
FIGURE2.10 INTERNALMODELFORTINYCOLLEGE
software independence
A property of any model or application that does not depend on the software used to implement it.
hardware independence
A condition in which
a model does not
depend on the hardware used in the model’s implementation. Therefore, changes in the hardware will have no effect on the database design at the conceptual level.
logical design
A stage in the design phase that matches
the conceptual design
to the requirements of the selected DBMS and
is therefore software- dependent. Logical design is used to translate the conceptual design into the internal model for a selected database management system, such as DB2, SQL Server, Oracle, IMS, Informix, Access, or Ingress.
internal model
In database modeling, a level of data abstraction that adapts the conceptual model to a specific DBMS model for implementation. The internal model is
the representation of
a database as“seen”
by the DBMS. In other words, the internal model requires a designer to match the conceptual model’s characteristics and constraints to those of the selected implementation model.
internal schema
A representation of an internal model using the database constructs sup- ported by the chosen database.
Copyright 2017 Cengage Lea Editorial review has deemed that

precise specification of data storage location and data access paths. In contrast, the rela- tional model requires less detail in its internal model because most RDBMSs handle data access path definition transparently; that is, the designer need not be aware of the data access path details. Nevertheless, even relational database software usually requires spec- ifications of data storage locations, especially in a mainframe environment. For example, DB2 requires that you specify the data storage group, the location of the database within the storage group, and the location of the tables within the database.
Because the internal model depends on specific database software, it is said to be software dependent. Therefore, a change in the DBMS software requires that the inter- nal model be changed to fit the characteristics and requirements of the implementation database model. When you can change the internal model without affecting the concep- tual model, you have logical independence. However, the internal model is still hard- ware independent because it is unaffected by the type of computer on which the software is installed. Therefore, a change in storage devices or even a change in operating systems will not affect the internal model.
2-6d The Physical Model
The physical model operates at the lowest level of abstraction, describing the way data is saved on storage media such as magnetic, solid state, or optical media. The physical model requires the definition of both the physical storage devices and the (physical) access meth- ods required to reach the data within those storage devices, making it both software and hardware dependent. The storage structures used are dependent on the software (the DBMS and the operating system) and on the type of storage devices the computer can handle. The precision required in the physical model’s definition demands that database designers have a detailed knowledge of the hardware and software used to implement the database design.
Early data models forced the database designer to take the details of the physical model’s data storage requirements into account. However, the now dominant relational model is aimed largely at the logical level rather than the physical level; therefore, it does not require the physical-level details common to its predecessors.
Although the relational model does not require the designer to be concerned about the data’s physical storage characteristics, the implementation of a relational model may require physical-level fine-tuning for increased performance. Fine-tuning is especially important when very large databases are installed in a mainframe environment, yet even such perfor- mance fine-tuning at the physical level does not require knowledge of physical data storage characteristics.
As noted earlier, the physical model is dependent on the DBMS, methods of accessing files, and types of hardware storage devices supported by the operating system. When you can change the physical model without affecting the internal model, you have physi- cal independence. Therefore, a change in storage devices or methods and even a change in operating system will not affect the internal model.
The levels of data abstraction are summarized in Table 2.4.
Chapter 2
Data Models 63
logical independence
A condition in which the internal model can be changed without af- fecting the conceptual model. (The internal model is hardware- independent because it is unaffected by the computer on which the software is installed. Therefore, a change
in storage devices or operating systems will not affect the internal model.)
physical model
A model in which physical characteristics such as location, path, and format are described for the data. The
physical model is both hardware- and software- dependent. See also physical design.
physical independence
A condition in which the physical model can be changed without affecting the internal model.
TABLE 2.4
LEVELS OF DATA ABSTRACTION
MODEL
DEGREE OF ABSTRACTION
FOCUS
INDEPENDENT OF
External
High
Low
End-user views
Hardware and software
Conceptual
Global view of data (database model independent)
Hardware and software
Internal
Specific database model
Hardware
Physical
Storage and access methods
Neither hardware nor software
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64 Part 1 Database Concepts
Summary
• A data model is an abstraction of a complex real-world data environment. Database designers use data models to communicate with programmers and end users. The basic data-modeling components are entities, attributes, relationships, and con- straints. Business rules are used to identify and define the basic modeling components within a specific real-world environment.
• The hierarchical and network data models were early models that are no longer used, but some of the concepts are found in current data models.
• The relational model is the current database implementation standard. In the rela- tional model, the end user perceives the data as being stored in tables. Tables are related to each other by means of common values in common attributes. The entity relationship (ER) model is a popular graphical tool for data modeling that comple- ments the relational model. The ER model allows database designers to visually pres- ent different views of the data—as seen by database designers, programmers, and end users—and to integrate the data into a common framework.
• The object-oriented data model (OODM) uses objects as the basic modeling struc- ture. Like the relational model’s entity, an object is described by its factual content. Unlike an entity, however, the object also includes information about relationships between the facts, as well as relationships with other objects, thus giving its data more meaning.
• The relational model has adopted many object-oriented (OO) extensions to become the extended relational data model (ERDM). Object/relational database management systems (O/R DBMS) were developed to implement the ERDM. At this point, the OODM is largely used in specialized engineering and scientific applications, while the ERDM is primarily geared to business applications.
• Emerging Big Data technologies such as Hadoop, MapReduce, and NoSQL provide distributed, fault-tolerant, and cost-efficient support for Big Data analytics. NoSQL databases are a new generation of databases that do not use the relational model and are geared to support the very specific needs of Big Data organizations. NoSQL data- bases offer distributed data stores that provide high scalability, availability, and fault tolerance by sacrificing data consistency and shifting the burden of maintaining rela- tionships and data integrity to the program code.
• Data-modeling requirements are a function of different data views (global versus local) and the level of data abstraction. The American National Standards Institute Standards Planning and Requirements Committee (ANSI/SPARC) describes three levels of data abstraction: external, conceptual, and internal. The fourth and lowest level of data abstraction, called the physical level, is concerned exclusively with phys- ical storage methods.
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Chapter 2
Data Models 65
1. Discuss the importance of data models.
2. What is a business rule, and what is its purpose in data modeling?
3. How do you translate business rules into data model components?
4. Describe the basic features of the relational data model and discuss their importance to the end user and the designer.
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Key Terms
3 Vs
American National Standards Institute (ANSI)
attribute
Big Data
business rule
Chen notation
class
class diagram
class diagram notation
class hierarchy
client node
conceptual model
conceptual schema
connectivity
constraint
Crow’s Foot notation
data definition language (DDL)
entity relationship diagram (ERD)
entity set
eventual consistency
extended relational data model (ERDM)
Extensible Markup Language (XML)
external model
external schema
Hadoop
Hadoop Distributed File System (HDFS)
hardware independence hierarchical model inheritance
internal model
internal schema
key-value
logical design
logical independence
MapReduce
many-to-many (M:N or *..*) relationship
object/relational database management system (O/R DBMS)
object-oriented data model (OODM)
object-oriented database management system (OODBMS)
one-to-many (1:M or 1..*) relationship
one-to-one (1:1 or 1..1) relationship
physical independence physical model relation
relational database management system (RDBMS)
relational diagram relational model relationship schema
segment
semantic data model
software independence
sparse data
subschema
table
tuple
Unified Modeling Language (UML)
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
data manipulation language (DML)
data model
data modeling data node
entity
entity instance entity occurrence
entity relationship (ER) model (ERM)
method
name node network model NoSQL
object
Review Questions

66 Part 1
Database Concepts
5. Explain how the entity relationship (ER) model helped produce a more structured relational database design environment.
6. Consider the scenario described by the statement “A customer can make many pay- ments, but each payment is made by only one customer.” Use this scenario as the basis for an entity relationship diagram (ERD) representation.
7. Why is an object said to have greater semantic content than an entity?
8. What is the difference between an object and a class in the object-oriented data model (OODM)?
9. How would you model Question 6 with an OODM? (Use Figure 2.4 as your guide.)
10. WhatisanERDM,andwhatroledoesitplayinthemodern(production)database environment?
11. What is a relationship, and what three types of relationships exist?
12. Give an example of each of the three types of relationships.
13. What is a table, and what role does it play in the relational model?
14. What is a relational diagram? Give an example.
15. What is connectivity? (Use a Crow’s Foot ERD to illustrate connectivity.)
16. Describe the Big Data phenomenon.
17. What does the term 3 Vs refer to?
18. What is Hadoop and what are its basic components?
19. What is sparse data? Give an example.
20. Define and describe the basic characteristics of a NoSQL database.
21. Using the example of a medical clinic with patients and tests, provide a simple representation of how to model this example using the relational model and how it would be represented using the key-value data modeling technique.
22. What is logical independence?
23. What is physical independence?
Problems
Use the contents of Figure 2.1 to work Problems 1–3.
1. Write the business rule(s) that govern the relationship between AGENT and CUSTOMER.
2. Given the business rule(s) you wrote in Problem 1, create the basic Crow’s Foot ERD.
3. Using the ERD you drew in Problem 2, create the equivalent object representation and UML class diagram. (Use Figure 2.4 as your guide.)
Using Figure P2.4 as your guide, work Problems 4–5. The DealCo relational diagram shows the initial entities and attributes for the DealCo stores, which are located in two regions of the country.
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4. Identify each relationship type and write all of the business rules.
5. Create the basic Crow’s Foot ERD for DealCo.
Using Figure P2.6 as your guide, work Problems 6–8. The Tiny College relational dia- gram shows the initial entities and attributes for the college.
6. Identify each relationship type and write all of the business rules.
7. Create the basic Crow’s Foot ERD for Tiny College.
8. Create the UML class diagram that reflects the entities and relationships you identi- fied in the relational diagram.
9. Typically, a hospital patient receives medications that have been ordered by a particular doctor. Because the patient often receives several medications per day, there is a 1:M relationship between PATIENT and ORDER. Similarly, each order can include several medications, creating a 1:M relationship between ORDER and MEDICATION.
a. Identify the business rules for PATIENT, ORDER, and MEDICATION.
b. Create a Crow’s Foot ERD that depicts a relational database model to capture these business rules.
10. United Broke Artists (UBA) is a broker for not-so-famous artists. UBA maintains a small database to track painters, paintings, and galleries. A painting is created by a particular artist and then exhibited in a particular gallery. A gallery can exhibit many paintings, but each painting can be exhibited in only one gallery. Similarly, a painting is created by a single painter, but each painter can create many paintings. Using PAINTER, PAINTING, and GALLERY, in terms of a relational database:
a. What tables would you create, and what would the table components be?
b. How might the (independent) tables be related to one another?
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Chapter 2
Data Models 67
FIGURE P2.4 THE DEALCO RELATIONAL DIAGRAM
FIGURE P2.6 THE TINY COLLEGE RELATIONAL DIAGRAM

68 Part 1
Database Concepts
11. Using the ERD from Problem 10, create the relational schema. (Create an appropri- ate collection of attributes for each of the entities. Make sure you use the appropriate naming conventions to name the attributes.)
12. Convert the ERD from Problem 10 into a corresponding UML class diagram.
13. Describe the relationships (identify the business rules) depicted in the Crow’s Foot ERD shown in Figure P2.13.
FIGURE P2.13 THE CROW’S FOOT ERD FOR PROBLEM 13
14. Create a Crow’s Foot ERD to include the following business rules for the ProdCo company:
a. Each sales representative writes many invoices.
b. Each invoice is written by one sales representative.
c. Each sales representative is assigned to one department.
d. Each department has many sales representatives.
e. Each customer can generate many invoices.
f. Each invoice is generated by one customer.
15. WritethebusinessrulesthatarereflectedintheERDshowninFigureP2.15.(Note that the ERD reflects some simplifying assumptions. For example, each book is writ- ten by only one author. Also, remember that the ERD is always read from the “1” to the “M” side, regardless of the orientation of the ERD components.)
FIGURE P2.15 THE CROW’S FOOT ERD FOR PROBLEM 15
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16. CreateaCrow’sFootERDforeachofthefollowingdescriptions.(Notethattheword many merely means more than one in the database modeling environment.)
a. Each of the MegaCo Corporation’s divisions is composed of many departments. Each department has many employees assigned to it, but each employee works for only one department. Each department is managed by one employee, and each of those managers can manage only one department at a time.
b. During some period of time, a customer can download many ebooks from BooksOnline. Each of the ebooks can be downloaded by many customers during that period of time.
c. An airliner can be assigned to fly many flights, but each flight is flown by only one airliner.
d. The KwikTite Corporation operates many factories. Each factory is located in a region, and each region can be “home” to many of KwikTite’s factories. Each fac- tory has many employees, but each employee is employed by only one factory.
e. An employee may have earned many degrees, and each degree may have been earned by many employees.
17. Write the business rules that are reflected in the ERD shown in Figure P2.17.
Chapter 2
Data Models 69
FIGURE P2.17 THE CROW’S FOOT ERD FOR PROBLEM 17
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PART 2
Design Concepts
3 The Relational Database Model 4 Entity Relationship (ER) Modeling 5 Advanced Data Modeling
6 Normalization of Database Tables
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Chapter 3
The Relational Database Model
In this chapter, you will learn:
• That the relational database model offers a logical view of data
• About the relational model’s basic component: relations
• That relations are logical constructs composed of rows (tuples) and columns (attributes) • That relations are implemented as tables in a relational DBMS
• About relational database operators, the data dictionary, and the system catalog
• How data redundancy is handled in the relational database model
• Why indexing is important
Preview
In this chapter, you will learn about the relational model’s logical structure and more about how ERDs (entity relationship diagrams) can be used to design a relational data- base. You will also learn how the relational database’s basic data components fit into a logical construct known as a table, and how tables within a database can be related to one another.
After learning about tables, their components, and their relationships, you will be intro- duced to basic table design concepts and the characteristics of well-designed and poorly designed tables. These concepts will become your gateway to the next few chapters.
Data Files and Available Formats
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
CH03_CollegeTry P CH03_CollegeTry2 P CH03_InsureCo P CH03_Museum P CH03_SaleCo P CH03_TinyCollege P CH03_Relational_DB P
P P P P P P P P P P P P P P P P P P P P P
CH03_AviaCo P CH03_BeneCo P CH03_CollegeQue P CH03_NoComp P CH03_StoreCo P CH03_Theater P CH03_TransCo P CH03_VendingCo P
P P P P P P P P P P P P P P P P P P P P P P P P
Data Files Available on cengagebrain.com
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Note
3-1 A Logical View of Data
In Chapter 1, Database Systems, you learned that a database stores and manages both data and metadata. You also learned that the DBMS manages and controls access to the data and the database structure. Such an arrangement—placing the DBMS between the application and the database—eliminates most of the file system’s inherent limitations. The result of such flexibility, however, is a far more complex physical structure. In fact, the database structures required by both the hierarchical and network database models often become complicated enough to diminish efficient database design. The relational data model changed all of that by allowing the designer to focus on the logical represen- tation of the data and its relationships, rather than on the physical storage details. To use an automotive analogy, the relational database uses an automatic transmission to relieve you of the need to manipulate clutch pedals and gearshifts. In short, the relational model enables you to view data logically rather than physically.
The practical significance of taking the logical view is that it serves as a reminder of the simple file concept of data storage. Although the use of a table, quite unlike that of a file, has the advantages of structural and data independence, a table does resemble a file from a conceptual point of view. Because you can think of related records as being stored in independent tables, the relational database model is much easier to understand than the hierarchical and network models. Logical simplicity tends to yield simple and effective database design methodologies.
Because the table plays such a prominent role in the relational model, it deserves a closer look. Therefore, our discussion begins by exploring the details of table structure and contents.
3-1a Tables and Their Characteristics
The logical view of the relational database is facilitated by the creation of data relation- ships based on a logical construct known as a relation. Because a relation is a mathemat- ical construct, end users find it much easier to think of a relation as a table. A table is perceived as a two-dimensional structure composed of rows and columns. A table is also
predicate logic
Used extensively in mathematics to provide a framework in which an assertion (statement of fact) can be verified as either true or false.
set theory
A part of mathematical science that deals with sets, or groups of things, and is used as the basis for data manipulation in the relational model.
Chapter 3 The Relational Database Model 73
The relational model, introduced by E. F. Codd in 1970, is based on predicate logic and set theory. Predicate logic, used extensively in mathematics, provides a framework in which an assertion (statement of fact) can be verified as either true or false. For example, suppose that a student with a student ID of 12345678 is named Melissa Sanduski. This assertion can easily be demonstrated to be true or false. Set theory is a mathematical science that deals with sets, or groups of things, and is used as the basis for data manipulation in the relational model. For example, assume that set A contains three numbers: 16, 24, and 77. This set is represented as A(16, 24, 77). Furthermore, set B contains four numbers: 44, 77, 90, and 11, and so is represented as B(44, 77, 90, 11). Given this information, you can conclude that the intersection of A and B yields a result set with a single number, 77. This result can be expressed as A ∩ B = 77. In other words, A and B share a common value, 77.
Based on these concepts, the relational model has three well-defined components:
1. A logical data structure represented by relations (see Sections 3-1, 3-2, and 3-5)
2. A set of integrity rules to enforce that the data is consistent and remains consistent over time (see Sections 3-3, 3-6, 3-7, and 3-8)
3. A set of operations that defines how data is manipulated (see Section 3-4)
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74 Part 2
Design Concepts
Note
called a relation because the relational model’s creator, E. F. Codd, used the two terms as synonyms. You can think of a table as a persistent representation of a logical relation— that is, a relation whose contents can be permanently saved for future use. As far as the table’s user is concerned, a table contains a group of related entity occurrences—that is, an entity set. For example, a STUDENT table contains a collection of entity occurrences, each representing a student. For that reason, the terms entity set and table are often used interchangeably.
The word relation, also known as a dataset in Microsoft Access, is based on the mathe- matical set theory from which Codd derived his model. Because the relational model uses attribute values to establish relationships among tables, many database users incorrectly assume that the term relation refers to such relationships. Many then incorrectly conclude that only the relational model permits the use of relationships.
You will discover that the table view of data makes it easy to spot and define entity rela- tionships, thereby greatly simplifying the task of database design. The characteristics of a relational table are summarized in Table 3.1.
TABLE 3.1
CHARACTERISTICS OF A RELATIONAL TABLE
1
A table is perceived as a two-dimensional structure composed of rows and columns.
2
Each table row (tuple) represents a single entity occurrence within the entity set.
3
Each table column represents an attribute, and each column has a distinct name.
4
Each intersection of a row and column represents a single data value.
5
All values in a column must conform to the same data format.
6
Each column has a specific range of values known as the attribute domain.
7
The order of the rows and columns is immaterial to the DBMS.
8
Each table must have an attribute or combination of attributes that uniquely identifies each row.
Note
The database table shown in Figure 3.1 illustrates the characteristics listed in Table 3.1.
Relational database terminology is very precise. Unfortunately, file system terminology sometimes creeps into the database environment. Thus, rows are sometimes referred to as records, and columns are sometimes labeled as fields. Occasionally, tables are labeled files. Technically speaking, this substitution of terms is not always appropriate. The database table is a logical concept rather than a physical concept, and the terms file, record, and field describe physical concepts. Nevertheless, as long as you recognize that the table is actually a logical concept rather than a physical construct, you may think of table rows as records and of table columns as fields. In fact, many database software vendors still use this familiar file system terminology.
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Chapter 3 The Relational Database Model 75
FIGURE3.1 STUDENTTABLEATTRIBUTEVALUES
Table name: STUDENT Database name: Ch03_TinyCollege
STU_NUM STU_LNAME STU_FNAME STU_INIT STU_DOB STU_HRS STU_CLASS STU_GPA STU_TRANSFER DEPT_CODE STU_PHONE PROF_NUM
= Student number
= Student last name
= Student first name
= Student middle initial
= Student date of birth
= Credit hours earned
= Student classification
= Grade point average
= Student transferred from another institution
= Department code
= 4-digit campus phone extension
= Number of the professor who is the student’s advisor
Using the STUDENT table shown in Figure 3.1, you can draw the following conclu- sions corresponding to the points in Table 3.1:
1. The STUDENT table is perceived to be a two-dimensional structure composed of 8 rows (tuples) and 12 columns (attributes).
2. Each row in the STUDENT table describes a single entity occurrence within the entity set. (The entity set is represented by the STUDENT table.) For example, row 4 in Figure 3.1 describes a student named Walter H. Oblonski. Given the table con- tents, the STUDENT entity set includes eight distinct entities (rows), or students.
3. Each column represents an attribute, and each column has a distinct name.
4. All of the values in a column match the attribute’s characteristics. For example, the grade point average (STU_GPA) column contains only STU_GPA entries for each of the table rows. Data must be classified according to its format and func- tion. Although various DBMSs can support different data types, most support at least the following:
a. Numeric.Youcanusenumericdatatoperformmeaningfularithmeticprocedures. For example, in Figure 3.1, STU_HRS and STU_GPA are numeric attributes.
b. Character. Character data, also known as text data or string data, can contain any character or symbol not intended for mathematical manipulation. In Figure 3.1, STU_CLASS and STU_PHONE are examples of character attributes.
c. Date.Dateattributescontaincalendardatesstoredinaspecialformatknownas the Julian date format. In Figure 3.1, STU_DOB is a date attribute.
d. Logical. Logical data can only have true or false (yes or no) values. In Figure 3.1, the STU_TRANSFER attribute uses a logical data format.
5. The column’s range of permissible values is known as its domain. Because the STU_GPA values are limited to the range 0–4, inclusive, the domain is [0,4].
6. The order of rows and columns is immaterial to the user.
Online Content
All of the databases used to illustrate the material in this chapter (see the Data Files list at the beginning of the chapter) are available at www.cengagebrain. com. The database names match the data- base names shown in the figures.
tuple
In the relational model, a table row.
domain
In data modeling,
the construct used to organize and describe an attribute’s set of possible values.
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76 Part 2
Design Concepts
7. Each table must have a primary key. In general terms, the primary key (PK) is an attribute or combination of attributes that uniquely identifies any given row. In this case, STU_NUM (the student number) is the primary key. Using the data in Figure 3.1, observe that a student’s last name (STU_LNAME) would not be a good primary key because several students have the last name of Smith. Even the combination of the last name and first name (STU_FNAME) would not be an appropriate primary key because more than one student is named John Smith.
3-2 Keys
In the relational model, keys are important because they are used to ensure that each row in a table is uniquely identifiable. They are also used to establish relationships among tables and to ensure the integrity of the data. A key consists of one or more attributes that determine other attributes. For example, an invoice number identifies all of the invoice attributes, such as the invoice date and the customer name.
One type of key, the primary key, has already been introduced. Given the structure of the STUDENT table shown in Figure 3.1, defining and describing the primary key seem simple enough. However, because the primary key plays such an important role in the relational environment, you will examine the primary key’s properties more carefully. In this section, you also will become acquainted with superkeys, candidate keys, and secondary keys.
3-2a Dependencies
The role of a key is based on the concept of determination. Determination is the state in which knowing the value of one attribute makes it possible to determine the value of another. The idea of determination is not unique to the database environment. You are familiar with the formula revenue − cost = profit. This is a form of determination, because if you are given the revenue and the cost, you can determine the profit. Given profit and revenue, you can determine the cost. Given any two values, you can determine the third. Determination in a database environment, however, is not normally based on a formula but on the relationships among the attributes.
If you consider what the attributes of the STUDENT table in Figure 3.1 actually represent, you will see a relationship among the attributes. If you are given a value for STU_NUM, then you can determine the value for STU_LNAME because one and only one value of STU_LNAME is associated with any given value of STU_NUM. A specific terminology and notation is used to describe relationships based on determination. The relationship is called functional dependence, which means that the value of one or more attributes determines the value of one or more other attributes. The standard notation for representing the relationship between STU_NUM and STU_LNAME is as follows:
STU_NUM → STU_LNAME
In this functional dependency, the attribute whose value determines another is called the determinant or the key. The attribute whose value is determined by the other attribute is called the dependent. Using this terminology, it would be correct to say that STU_ NUM is the determinant and STU_LNAME is the dependent. STU_NUM functionally determines STU_LNAME, and STU_LNAME is functionally dependent on STU_NUM. As stated earlier, functional dependence can involve a determinant that comprises more than one attribute and multiple dependent attributes. Refer to the STUDENT table for the following example:
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
primary key (PK)
In the relational model, an identifier composed of one or more attributes that uniquely identifies a row. Also, a candidate key selected as a unique entity identifier. See also key.
key
One or more attributes that determine other attributes. See also superkey, candidate key, primary key (PK), secondary key, and foreign key.
determination
The role of a key. In the context of a database table, the statement “A determines B” indicates that knowing the value of attribute A means that the value of attribute B can be looked up.
functional dependence
Within a relation R, an attribute B is functionally dependent on an attribute A if and only if a given value of attribute A determines exactly one value of attribute
B. The relationship
“B is dependent on
A” is equivalent to “A determines B,” and is written as A → B.
determinant
Any attribute in a specific row whose value directly determines other values in that row. See also Boyce-Codd normal form (BCNF).
dependent
An attribute whose value is determined by another attribute.
Copyright 2017 Cengage Lea Editorial review has deemed that

STU_NUM → (STU_LNAME, STU_FNAME, STU_GPA) and
(STU_FNAME, STU_LNAME, STU_INIT, STU_PHONE) → (STU_DOB, STU_HRS, STU_GPA)
Determinants made of more than one attribute require special consideration. It is possible to have a functional dependency in which the determinant contains attri- butes that are not necessary for the relationship. Consider the following two functional dependencies:
STU_NUM → STU_GPA
(STU_NUM, STU_LNAME) → STU_GPA
In the second functional dependency, the determinant includes STU_LNAME, but this attribute is not necessary for the relationship. The functional dependency is valid because given a pair of values for STU_NUM and STU_LNAME, only one value would occur for STU_GPA. A more specific term, full functional dependence, is used to refer to func- tional dependencies in which the entire collection of attributes in the determinant is nec- essary for the relationship. Therefore, the dependency shown in the preceding example is a functional dependency, but not a full functional dependency.
3-2b Types of Keys
Recall that a key is an attribute or group of attributes that can determine the values of other attributes. Therefore, keys are determinants in functional dependencies. Several different types of keys are used in the relational model, and you need to be familiar with them.
A composite key is a key that is composed of more than one attribute. An attribute that is a part of a key is called a key attribute. For example,
STU_NUM → STU_GPA
(STU_LNAME, STU_FNAME, STU_INIT, STU_PHONE) → STU_HRS
In the first functional dependency, STU_NUM is an example of a key composed of only one key attribute. In the second functional dependency, (STU_LNAME, STU_FNAME, STU_INIT, STU_PHONE) is a composite key composed of four key attributes.
A superkey is a key that can uniquely identify any row in the table. In other words, a superkey functionally determines every attribute in the row. In the STUDENT table, STU_NUM is a superkey, as are the composite keys (STU_NUM, STU_LNAME), (STU_ NUM, STU_LNAME, STU_INIT), and (STU_LNAME, STU_FNAME, STU_INIT, STU_PHONE). In fact, because STU_NUM alone is a superkey, any composite key that has STU_NUM as a key attribute will also be a superkey. Be careful, however, because not all keys are superkeys. For example, Gigantic State University determines its student classification based on hours completed, as shown in Table 3.2.
Therefore, you can write STU_HRS → STU_CLASS.
However, the specific number of hours is not dependent on the classification. It is quite possible to find a junior with 62 completed hours or one with 84 completed hours. In other words, the classification (STU_CLASS) does not determine one and only one value for completed hours (STU_HRS).
full functional dependence
A condition in which an attribute is functionally dependent on a composite key but not on any subset of the key.
composite key
A multiple-attribute key.
key attributes
The attributes that form a primary key. See also prime attribute.
superkey
An attribute or attributes that uniquely identify each entity in a table. See key.
Chapter 3 The Relational Database Model 77
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78 Part 2
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TABLE 3.2
STUDENT CLASSIFICATION
HOURS COMPLETED
CLASSIFICATION
Less than 30
Fr
30–59
So
60–89
Jr
90 or more
Sr
Note
One specific type of superkey is called a candidate key. A candidate key is a minimal superkey—that is, a superkey without any unnecessary attributes. A candidate key is based on a full functional dependency. For example, STU_NUM would be a candidate key, as would (STU_LNAME, STU_FNAME, STU_INIT, STU_PHONE). On the other hand, (STU_NUM, STU_LNAME) is a superkey, but it is not a candidate key because STU_LNAME could be removed and the key would still be a superkey. A table can have many different candidate keys. If the STUDENT table also included the students’ Social Security numbers as STU_SSN, then it would appear to be a candidate key. Candidate keys are called candidates because they are the eligible options from which the designer will choose when selecting the primary key. The primary key is the candidate key chosen to be the primary means by which the rows of the table are uniquely identified.
Entity integrity is the condition in which each row (entity instance) in the table has its own unique identity. To ensure entity integrity, the primary key has two requirements: (1) all of the values in the primary key must be unique, and (2) no key attribute in the primary key can contain a null.
Null values are problematic in the relational model. A null is the absence of any data value, and it is never allowed in any part of the primary key. From a theoretical perspective, it can be argued that a table that contains a null is not properly a relational table at all. From a practical perspective, however, some nulls cannot be reasonably avoided. For example, not all students have a middle initial. As a general rule, nulls should be avoided as much as reasonably possible. In fact, an abundance of nulls is often a sign of a poor design. Also, nulls should be avoided in the database because their meaning is not always identifiable. For example, a null could represent any of the following:
• An unknown attribute value
• A known, but missing, attribute value
• A “not applicable” condition
Depending on the sophistication of the application development software, nulls can create problems when functions such as COUNT, AVERAGE, and SUM are used. In addition, nulls can create logical problems when relational tables are linked.
In addition to its role in providing a unique identity to each row in the table, the primary key may play an additional role in the controlled redundancy that allows the
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A null is no value at all. It does not mean a zero or a space. A null is created when you press the Enter key or the Tab key to move to the next entry without making an entry of any kind. Pressing the Spacebar creates a blank (or a space).
candidate key
A minimal superkey; that is, a key that does not contain a subset of attributes that is itself a superkey. See key.
entity integrity
The property of a relational table that guarantees each entity has a unique value in a primary key and that the key has no null values.
null
The absence of an attribute value. Note that a null is not a blank.
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relational model to work. Recall from Chapter 2 that a hallmark of the relational model is that relationships between tables are implemented through common attributes as a form of controlled redundancy. For example, Figure 3.2 shows PRODUCT and VEN- DOR tables that are linked through a common attribute, VEND_CODE. VEND_CODE is referred to as a foreign key in the PRODUCT table. A foreign key (FK) is the primary key of one table that has been placed into another table to create a common attribute. In Figure 3.2, the primary key of VENDOR, VEND_CODE, was placed in the PRODUCT table; therefore, VEND_CODE is a foreign key in PRODUCT. One advantage of using a proper naming convention for table attributes is that you can identify foreign keys more easily. For example, because the STUDENT table in Figure 3.1 used a proper naming convention, you can identify two foreign keys in the table (DEPT_CODE and PROF_ NUM) that imply the existence of two other tables in the database (DEPARTMENT and PROFESSOR) related to STUDENT.
Just as the primary key has a role in ensuring the integrity of the database, so does the foreign key. Foreign keys are used to ensure referential integrity, the condition in which every reference to an entity instance by another entity instance is valid. In other words, every foreign key entry must either be null or a valid value in the primary key of the related table. Note that the PRODUCT table has referential integrity because every entry in VEND_CODE in the PRODUCT table is either null or a valid value in VEND_ CODE in the VENDOR table. Every vendor referred to by a row in the PRODUCT table is a valid vendor.
Finally, a secondary key is defined as a key that is used strictly for data retrieval purposes. Suppose that customer data is stored in a CUSTOMER table in which the customer number is the primary key. Do you think that most customers will remember their numbers? Data retrieval for a customer is easier when the cus- tomer’s last name and phone number are used. In that case, the primary key is the customer number; the secondary key is the combination of the customer’s last name and phone number. Keep in mind that a secondary key does not necessarily yield a unique outcome. For example, a customer’s last name and home telephone number could easily yield several matches in which one family lives together and shares a phone line. A less efficient secondary key would be the combination of the last name and zip code; this could yield dozens of matches, which could then be combed for a specific match.
foreign key (FK)
An attribute or attributes in one table whose values must match the primary key in another table or whose values must be null. See key.
referential integrity
A condition by which a dependent table’s foreign key must have either a null entry or a matching entry in the related table.
secondary key
A key used strictly for data retrieval purposes. For example, customers are not likely to know their customer number (primary key), but the combination of last name, first name, middle initial, and telephone number will probably match the appropriate table row. See also key.
Chapter 3 The Relational Database Model 79
FIGURE3.2 ANEXAMPLEOFASIMPLERELATIONALDATABASE
Table name: PRODUCT Primary key: PROD_CODE Foreign key: VEND_CODE
Table name: VENDOR Primary key: VEND_CODE Foreign key: none
Database name: Ch03_SaleCo
link
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80 Part 2
Design Concepts
A secondary key’s effectiveness in narrowing down a search depends on how restric- tive the key is. For instance, although the secondary key CUS_CITY is legitimate from a database point of view, the attribute values New York or Sydney are not likely to produce a usable return unless you want to examine millions of possible matches. (Of course, CUS_CITY is a better secondary key than CUS_COUNTRY.)
Table 3.3 summarizes the various relational database table keys.
TABLE 3.3
RELATIONAL DATABASE KEYS
KEY TYPE
DEFINITION
Superkey
An attribute or combination of attributes that uniquely identifies each row in a table
Candidate key
A minimal (irreducible) superkey; a superkey that does not contain a subset of attributes that is itself a superkey
Primary key
A candidate key selected to uniquely identify all other attribute values in any given row; cannot contain null entries
Foreign key
An attribute or combination of attributes in one table whose values must either match the primary key in another table or be null
Secondary key
An attribute or combination of attributes used strictly for data retrieval purposes
3-3 Integrity Rules
Relational database integrity rules are very important to good database design. RDBMSs enforce integrity rules automatically, but it is much safer to make sure your application design conforms to the entity and referential integrity rules mentioned in this chapter. Those rules are summarized in Table 3.4.
TABLE 3.4
INTEGRITY RULES
ENTITY INTEGRITY
DESCRIPTION
Requirement
All primary key entries are unique, and no part of a primary key may be null.
Purpose
Each row will have a unique identity, and foreign key values can properly reference primary key values.
Example
No invoice can have a duplicate number, nor can it be null; in short, all invoices are uniquely identified by their invoice number.
REFERENTIAL INTEGRITY
DESCRIPTION
Requirement
A foreign key may have either a null entry, as long as it is not a part of its table’s primary key, or an entry that matches the primary key value in a table to which it is related; (every non-null foreign key value must reference an existing primary key value).
Purpose
It is possible for an attribute not to have a corresponding value, but it will be impossible to have an invalid entry; the enforcement of the referential integrity rule makes it impossible to delete a row in one table whose primary key has mandatory matching foreign key values in another table.
Example
A customer might not yet have an assigned sales representative (number), but it will be impossible to have an invalid sales representative (number).
The integrity rules summarized in Table 3.4 are illustrated in Figure 3.3.
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Note the following features of Figure 3.3.
• Entity integrity. The CUSTOMER table’s primary key is CUS_CODE. The CUS- TOMER primary key column has no null entries, and all entries are unique. Similarly, the AGENT table’s primary key is AGENT_CODE, and this primary key column is also free of null entries.
• Referential integrity. The CUSTOMER table contains a foreign key, AGENT_CODE, that links entries in the CUSTOMER table to the AGENT table. The CUS_CODE row identified by the (primary key) number 10013 contains a null entry in its AGENT_ CODE foreign key because Paul F. Olowski does not yet have a sales representative assigned to him. The remaining AGENT_CODE entries in the CUSTOMER table all match the AGENT_CODE entries in the AGENT table.
To avoid nulls, some designers use special codes, known as flags, to indicate the absence of some value. Using Figure 3.3 as an example, the code –99 could be used as the AGENT_CODE entry in the fourth row of the CUSTOMER table to indicate that cus- tomer Paul Olowski does not yet have an agent assigned to him. If such a flag is used, the AGENT table must contain a dummy row with an AGENT_CODE value of −99. Thus, the AGENT table’s first record might contain the values shown in Table 3.5.
Chapter 4, Entity Relationship (ER) Modeling, discusses several ways to handle nulls.
Other integrity rules that can be enforced in the relational model are the NOT NULL and UNIQUE constraints. The NOT NULL constraint can be placed on a col- umn to ensure that every row in the table has a value for that column. The UNIQUE constraint is a restriction placed on a column to ensure that no duplicate values exist for that column.
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Chapter 3 The Relational Database Model 81
FIGURE3.3 ANILLUSTRATIONOFINTEGRITYRULES
Table name: CUSTOMER Database name: Ch03_InsureCo Primary key: CUS_CODE
Foreign key: AGENT_CODE
Table name: AGENT (only five selected fields are shown) Primary key: AGENT_CODE
Foreign key: none
flags
Special codes implemented by designers to trigger a required response, alert end users to specified conditions, or encode values. Flags may be used to prevent nulls by bringing attention to the absence of a value in a table.
TABLE 3.5
A DUMMY VARIABLE VALUE USED AS A FLAG
AGENT_CODE
AGENT_AREACODE
AGENT_PHONE
AGENT_LNAME
AGENT_YTD_SLS
−99
000
000–0000
None
$0.00

82 Part 2
Design Concepts
3-4 Relational Algebra
The data in relational tables is of limited value unless the data can be manipulated to gen- erate useful information. This section describes the basic data manipulation capabilities of the relational model. Relational algebra defines the theoretical way of manipulating table contents using relational operators. In Chapter 7, Introduction to Structured Query Language (SQL), and Chapter 8, Advanced SQL, you will learn how SQL commands can be used to accomplish relational algebra operations.
Note
The degree of relational completeness can be defined by the extent to which relational algebra is supported. To be considered minimally relational, the DBMS must support the key relational operators SELECT, PROJECT, and JOIN.
3-4a Formal Definitions and Terminology
Recall that the relational model is actually based on mathematical principles, and manip- ulating the data in the database can be described in mathematical terms. The good news is that, as database professionals, we do not have to write mathematical formulas to work with our data. Data is manipulated by database developers and programmers using powerful languages like SQL that hide the underlying math. However, understanding the underlying principles can give you a good feeling for the types of operations that can be performed, and it can help you to understand how to write your queries more efficiently and effectively.
One advantage of using formal mathematical representations of operations is that mathematical statements are unambiguous. These statements are very specific, and they require that database designers be specific in the language used to explain them. As previously explained, it is common to use the terms relation and table interchangeably. However, since the mathematical terms need to be precise, we will use the more specific term relation when discussing the formal definitions of the various relational algebra operators.
Before considering the specific relational algebra operators, it is necessary to formal- ize our understanding of a table.
One important aspect of using the specific term relation is that it acknowledges the distinction between the relation and the relation variable, or relvar, for short. A relation is the data that we see in our tables. A relvar is a variable that holds a relation. For exam- ple, imagine you were writing a program and created a variable named qty for holding integer data. The variable qty is not an integer itself; it is a container for holding integers. Similarly, when you create a table, the table structure holds the table data. The structure is properly called a relvar, and the data in the structure would be a relation. The relvar is a container (variable) for holding relation data, not the relation itself. The data in the table is a relation.
A relvar has two parts: the heading and the body. The relvar heading contains the names of the attributes, while the relvar body contains the relation. To conveniently maintain this distinction in formulas, an unspecified relation is often assigned a lower- case letter (e.g., “r”), while the relvar is assigned an uppercase letter (e.g., “R”). We could then say that r is a relation of type R, or r(R).
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relational algebra
A set of mathematical principles that form the basis for manipulating relational table contents; the eight main functions are SELECT, PROJECT, JOIN, INTERSECT, UNION, DIFFERENCE, PRODUCT, and DIVIDE.
relvar
Short for relation variable, a variable that holds a relation. A relvar is a container (variable) for holding relation data, not the relation itself.
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3-4b Relational Set Operators
The relational operators have the property of closure; that is, the use of relational alge- bra operators on existing relations (tables) produces new relations. Numerous operators have been defined. Some operators are fundamental, while others are convenient but can be derived using the fundamental operators. In this section, the focus will be on the SELECT (or RESTRICT), PROJECT, UNION, INTERSECT, DIFFERENCE, PRODUCT, JOIN, and DIVIDE operators.
Select (Restrict) SELECT, also known as RESTRICT, is referred to as a unary oper- ator because it only uses one table as input. It yields values for all rows found in the table that satisfy a given condition. SELECT can be used to list all of the rows, or it can yield only rows that match a specified criterion. In other words, SELECT yields a horizontal subset of a table. SELECT will not limit the attributes returned so all attributes of the table will be included in the result. The effect of a SELECT operation is shown in Figure 3.4.
Note
Project PROJECT yields all values for selected attributes. It is also a unary operator, accepting only one table as input. PROJECT will return only the attributes requested, in the order in which they are requested. In other words, PROJECT yields a vertical subset of a table. PROJECT will not limit the rows returned so all rows of the specified attributes will be included in the result. The effect of a PROJECT operation is shown in Figure 3.5.
closure
A property of relational operators that permits the use of relational algebra operators on existing tables (relations) to produce new relations.
SELECT
In relational algebra, an operator used to select a subset of rows. Also known as RESTRICT.
RESTRICT
See SELECT.
PROJECT
In relational algebra, an operator used to select a subset of columns.
Chapter 3 The Relational Database Model 83
FIGURE3.4 SELECT
Original table
New table
SELECT ALL yields
SELECT only PRICE less than $2.00 yields SELECT only P_CODE = 311452 yields
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Formally, SELECT is denoted by the lowercase Greek letter sigma (σ). Sigma is followed by the condition to be evaluated (called a predicate) as a subscript, and then the relation is listed in parentheses. For example, to SELECT all of the rows in the CUSTOMER table that have the value ‘10010’ in the CUS_CODE attribute, you would write the following:
σcus_code = 10010 (customer)

84 Part 2
Design Concepts
FIGURE3.5 PROJECT
Original table
New table
PROJECT PRICE yields
PROJECT P_DESCRIPT and PRICE yields
PROJECT P_CODE and PRICE yields
Note
Formally, PROJECT is denoted by the Greek letter pi (π). Some sources use the uppercase letter, and other sources use the lowercase letter. Codd used the lowercase π in his origi- nal article on the relational model, and that is what we use here. Pi is followed by the list of attributes to be returned as subscripts, and then the relation listed in parentheses. For example, to PROJECT the CUS_FNAME and CUS_LNAME attributes in the CUSTOMER table, you would write the following:
πcus_fname, cus_lname (customer)
Since relational operators have the property of closure, that is, they accept relations as input and produce relations as output, it is possible to combine operators. For example, you can combine the two previous operators to find the customer first and last name of the customer with customer code 10010:
πcus_fname, cus_lname (σcus_code = 10010 (customer))
UNION
In relational algebra, an operator used to merge (append) two tables into a new table, dropping the duplicate rows. The tables must be union- compatible.
union-compatible
Two or more tables that have the same number
of columns and the corresponding columns have compatible domains.
Union UNIONcombinesallrowsfromtwotables,excludingduplicaterows.Tobeusedinthe UNION, the tables must have the same attribute characteristics; in other words, the columns and domains must be compatible. When two or more tables share the same number of col- umns, and when their corresponding columns share the same or compatible domains, they are said to be union-compatible. The effect of a UNION operation is shown in Figure 3.6.
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FIGURE3.6 UNION
UNION yields

Note
Chapter 3 The Relational Database Model 85
UNION is denoted by the symbol ∪. If the relations SUPPLIER and VENDOR are union- compatible, then a UNION between them would be denoted as follows:
supplier ∪ vendor
It is rather unusual to find two relations that are union-compatible in a database. Typically, PROJECT operators are applied to relations to produce results that are union-compatible. For example, assume the SUPPLIER and VENDOR tables are not union-compatible. If you wish to produce a listing of all vendor and supplier names, then you can PROJECT the names from each table and then perform a UNION with them.
πsupplier_name (supplier) ∪ πvendor_name (vendor)
Intersect INTERSECTyieldsonlytherowsthatappearinbothtables.AswithUNION, the tables must be union-compatible to yield valid results. For example, you cannot use INTERSECT if one of the attributes is numeric and one is character-based. For the rows to be considered the same in both tables and appear in the result of the INTERSECT, the entire rows must be exact duplicates. The effect of an INTERSECT operation is shown in Figure 3.7.
Note
FIGURE3.7 INTERSECT
INTERSECT yields
Difference DIFFERENCE yields all rows in one table that are not found in the other table; that is, it subtracts one table from the other. As with UNION, the tables must be union-compatible to yield valid results. The effect of a DIFFER- ENCE operation is shown in Figure 3.8. However, note that subtracting the first table from the second table is not the same as subtracting the second table from the first table.
INTERSECT
In relational algebra, an operator used to yield only the rows that are common to two union- compatible tables.
DIFFERENCE
In relational algebra, an operator used to yield all rows from one table that are not found in another union-compatible table.
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INTERSECT is denoted by the symbol ∩. If the relations SUPPLIER and VENDOR are union-compatible, then an INTERSECT between them would be denoted as follows:
supplier ∩ vendor
Just as with the UNION operator, it is unusual to find two relations that are union- compatible in a database, so PROJECT operators are applied to relations to produce results that can be manipulated with an INTERSECT operator. For example, again assume the SUPPLIER and VENDOR tables are not union-compatible. If you wish to produce a listing of any vendor and supplier names that are the same in both tables, then you can PROJECT the names from each table and then perform an INTERSECT with them.
πsupplier_name (supplier) ∩ πvendor_name (vendor)

86 Part 2
Design Concepts
FIGURE3.8 DIFFERENCE
DIFFERENCE yields
Note
DIFFERENCE is denoted by the minus symbol −. If the relations SUPPLIER and VENDOR are union-compatible, then an DIFFERENCE of SUPPLIER minus VENDOR would be written as follows:
supplier − vendor
Assuming the SUPPLIER and VENDOR tables are not union-compatible, producing a list of any supplier names that do not appear as vendor names, then you can use a DIFFERENCE operator.
πsupplier_name (supplier) − πvendor_name (vendor)
Product PRODUCT yields all possible pairs of rows from two tables—also known as the Cartesian product. Therefore, if one table has 6 rows and the other table has 3 rows, the PRODUCT yields a list composed of 6 × 3 = 18rows. The effect of a PRODUCT operation is shown in Figure 3.9.
Note
FIGURE3.9 PRODUCT
PRODUCT yields
PRODUCT is denoted by the multiplication symbol ×. The PRODUCT of the CUSTOMER and AGENT relations would be written as follows:
customer × agent
A Cartesian product produces a set of sequences in which every member of one set is paired with every member of another set. In terms of relations, this means that every tuple in one relation is paired with every tuple in the second relation.
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PRODUCT
In relational algebra, an operator used to yield all possible pairs of rows from two tables. Also known as the Cartesian product.
Copyright 2017 Cengage Lea Editorial review has deemed that

Join JOIN allows information to be intelligently combined from two or more tables. JOIN is the real power behind the relational database, allowing the use of independent tables linked by common attributes. The CUSTOMER and AGENT tables shown in Figure 3.10 will be used to illustrate several types of joins.
A natural join links tables by selecting only the rows with common values in their com- mon attribute(s). A natural join is the result of a three-stage process:
1. First, a PRODUCT of the tables is created, yielding the results shown in Figure 3.11.
Chapter 3 The Relational Database Model 87
FIGURE 3.10 TWO TABLES THAT WILL BE USED IN JOIN ILLUSTRATIONS
Table name: CUSTOMER Table name: AGENT
FIGURE3.11 NATURALJOIN,STEP1:PRODUCT
2. Second, a SELECT is performed on the output of Step 1 to yield only the rows for which the AGENT_CODE values are equal. The common col- umns are referred to as the join columns. Step 2 yields the results shown in Figure 3.12.
JOIN
In relational algebra, a type of operator used to yield rows from two tables based on criteria. There are many types of joins, such as natural join, theta join, equijoin, and outer join.
natural join
A relational operation that yields a new table composed of only the rows with common values in their common attribute(s).
join columns
Columns that are used in the criteria of join operations. The join columns generally share similar values.
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88 Part 2
Design Concepts
FIGURE3.12 NATURALJOIN,STEP2:SELECT
3. A PROJECT is performed on the results of Step 2 to yield a single copy of each attribute, thereby eliminating duplicate columns. Step 3 yields the output shown in Figure 3.13.
The final outcome of a natural join yields a table that does not include unmatched pairs and provides only the copies of the matches.
Note a few crucial features of the natural join operation:
• If no match is made between the table rows, the new table does not include the unmatched row. In that case, neither AGENT_CODE 421 nor the customer whose last name is Smithson is included. Smithson’s AGENT_CODE 421 does not match any entry in the AGENT table.
• The column on which the join was made—that is, AGENT_CODE—occurs only once in the new table.
• If the same AGENT_CODE were to occur several times in the AGENT table, a customer would be listed for each match. For example, if the AGENT_CODE 167 occurred three times in the AGENT table, the customer named Rakowski would also occur three times in the resulting table because Rakowski is associated with AGENT_ CODE 167. (Of course, a good AGENT table cannot yield such a result because it would contain unique primary key values.)
FIGURE3.13 NATURALJOIN,STEP3:PROJECT
equijoin
A join operator that links tables based on
an equality condition that compares specified columns of the tables.
Note
Natural join is normally just referred to as JOIN in formal treatments. JOIN is denoted by the symbol ⨝. The JOIN of the CUSTOMER and AGENT relations would be written as follows:
customer ⨝ agent
Notice that the JOIN of two relations returns all of the attributes of both relations, except only one copy of the common attribute is returned. Formally, this is described as a UNION of the relvar headings. Therefore, the JOIN of the relations (c ⨝ a) includes the UNION of the relvars (C ∪ A). Also note that, as described above, JOIN is not a fundamental relational algebra operator. It can be derived from other operators as follows:
πcus_code, cus_lname, cus_fname, cus_initial, cus_renew_date, agent_code, agent_areacode, agent_phone, agent_lname, agent_ytd_sls (σcustomer.agent_code = agent.agent_code (customer × agent))
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Another form of join, known as an equijoin, links tables on the basis of an equality con- dition that compares specified columns of each table. The outcome of the equijoin does not eliminate duplicate columns, and the condition or criterion used to join the tables must be explicitly defined. In fact, the result of an equijoin looks just like the outcome shown in Figure 3.12 for Step 2 of a natural join. The equijoin takes its name from the equality comparison operator (=) used in the condition. If any other comparison opera- tor is used, the join is called a theta join.
Note
Each of the preceding joins is often classified as an inner join. An inner join only returns matched records from the tables that are being joined. In an outer join, the matched pairs would be retained, and any unmatched values in the other table would be left null. It is an easy mistake to think that an outer join is the opposite of an inner join. However, it is more accurate to think of an outer join as an “inner join plus.” The outer join still returns all of the matched records that the inner join returns, plus it returns the unmatched records from one of the tables. More specifically, if an outer join is produced for tables CUSTOMER and AGENT, two scenarios are possible:
theta join
A join operator that
links tables using an inequality comparison operator (<, >, <=, >=) in the join condition.
inner join
A join operation in which only rows that meet a given criterion are selected. The join criterion can be an equality condition (natural join or equijoin) or an inequality condition (theta join). The inner join is the most commonly used type of join. Contrast with outer join.
outer join
A relational algebra join operation that produces a table in which all unmatched pairs are retained; unmatched values in the related table are left null. Contrast with inner join. See also left outer join and right outer join.
left outer join
In a pair of tables to be joined, a join that yields all the rows in the left table, including those that have no matching values in the other table. For example, a left outer join of CUSTOMER with AGENT will yield all of
the CUSTOMER rows, including the ones that do not have a matching AGENT row. See also outer join and right outer join.
right outer join
In a pair of tables to be joined, a join that yields all of the rows in the right table, including the ones with no matching values in the other table. For example, a right outer join of CUSTOMER with AGENT will yield all of the AGENT rows, including the ones that do not have a matching CUSTOMER row. See also left outer join and outer join.
Chapter 3 The Relational Database Model 89
•
A left outer join yields all of the rows in the CUSTOMER table, including those that do not have a matching value in the AGENT table. An example of such a join is shown in Figure 3.14.
A right outer join yields all of the rows in the AGENT table, including those that do not have matching values in the CUSTOMER table. An example of such a join is shown in Figure 3.15.
FIGURE3.14 LEFTOUTERJOIN
•
FIGURE3.15 RIGHTOUTERJOIN
Outer joins are especially useful when you are trying to determine what values in related tables cause referential integrity problems. Such problems are created when foreign key values do not match the primary key values in the related table(s). In fact, if you are asked to convert large spreadsheets or other “nondatabase” data into relational database
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In formal terms, theta join is considered an extension of natural join. Theta join is denoted by adding a theta subscript after the JOIN symbol: ⨝θ. Equijoin is then a special type of theta join.

90 Part 2
Design Concepts
Note
tables, you will discover that the outer joins save you vast amounts of time and uncounted headaches when you encounter referential integrity errors after the conversions.
You may wonder why the outer joins are labeled “left” and “right.” The labels refer to the order in which the tables are listed in the SQL command. Chapter 8 explores such joins in more detail.
Outer join is also an extension of JOIN. Outer joins are the application of JOIN, DIFFERENCE, UNION, and PRODUCT. A JOIN returns the matched tuples, DIFFERENCE finds the tuples in one table that have values in the common attribute that do not appear in the com- mon attribute of the other relation, these unmatched tuples are combined with NULL val- ues through a PRODUCT, and then a UNION combines these results into a single relation. Clearly, a defined outer join is a great simplification! Left and right outer joins are denoted by the symbols ⟕ and ⟖, respectively.
Divide The DIVIDE operator is used to answer questions about one set of data being associated with all values of data in another set of data. The DIVIDE operation uses one 2-column table (Table 1) as the dividend and one single-column table (Table 2) as the divisor. For example, Figure 3.16 shows a list of customers and the products purchased in Table 1 on the left. Table 2 in the center contains a set of products that are of interest to the users. A DIVIDE operation can be used to determine which customers, if any, pur- chased every product shown in Table 2. In the figure, the dividend contains the P_CODE and CUS_CODE columns. The divisor contains the P_CODE column. The tables must have a common column—in this case, the P_CODE column. The output of the DIVIDE operation on the right is a single column that contains all values from the second column of the dividend (CUS_CODE) that are associated with every row in the divisor.
Using the example shown in Figure 3.16, note the following:
• Table 1 is “divided” by Table 2 to produce Table 3. Tables 1 and 2 both contain the P_CODE column but do not share the CUS_CODE column.
• To be included in the resulting Table 3, a value in the unshared column (CUS_CODE) must be associated with every value in Table 2.
• The only customers associated with all of products 123456, 234567, and 567890 are customers 10030 and 12550.
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FIGURE3.16 DIVIDE
DIVIDE yields
DIVIDE
In relational algebra, an operator that answers queries about one set of data being associated with all values of data in another set of data.
Copyright 2017 Cengage Le Editorial review has deemed that

Note
3-5 The Data Dictionary and the System Catalog
The data dictionary provides a detailed description of all tables in the database created by the user and designer. Thus, the data dictionary contains at least all of the attribute names and characteristics for each table in the system. In short, the data dictionary con- tains metadata—data about data. Using the small database presented in Figure 3.3, you might picture its data dictionary as shown in Table 3.6.
Note
Chapter 3 The Relational Database Model 91
The DIVIDE operator is denoted by the division symbol ÷. Given two relations, R and S, the DIVISION of them would be written: r ÷ s.
The data dictionary is sometimes described as “the database designer’s database” because it records the design decisions about tables and their structures.
Like the data dictionary, the system catalog contains metadata. The system catalog can be described as a detailed system data dictionary that describes all objects within the database, including data about table names, table’s creator and creation date, num- ber of columns in each table, data type corresponding to each column, index filenames, index creators, authorized users, and access privileges. Because the system catalog con- tains all required data dictionary information, the terms system catalog and data dic- tionary are often used interchangeably. In fact, current relational database software generally provides only a system catalog, from which the designer’s data dictionary information may be derived. The system catalog is actually a system-created database whose tables store the user/designer-created database characteristics and contents. Therefore, the system catalog tables can be queried just like any user/designer-created table.
In effect, the system catalog automatically produces database documenta- tion. As new tables are added to the database, that documentation also allows the RDBMS to check for and eliminate homonyms and synonyms. In general terms, homonyms are similar-sounding words with different meanings, such as boar and bore, or a word with different meanings, such as fair (which means “just” in some contexts and “festival” in others). In a database context, the word homonym indi- cates the use of the same name to label different attributes. For example, you might use C_NAME to label a customer name attribute in a CUSTOMER table and use C_NAME to label a consultant name attribute in a CONSULTANT table. To lessen confusion, you should avoid database homonyms; the data dictionary is very use- ful in this regard.
data dictionary
A DBMS component that stores metadata— data about data. Thus, the data dictionary contains the data definition as well as their characteristics and relationships. A data dictionary may also include data that are external to the DBMS. Also known as an information resource dictionary. See also active data dictionary, metadata, and passive data dictionary.
system catalog
A detailed system data dictionary that describes all objects in a database.
homonym
The use of the same name to label different attributes. Homonyms generally should be avoided. Some relational software automatically checks for homonyms and either alerts the
user to their existence or automatically makes the appropriate adjustments. See also synonym.
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The data dictionary in Table 3.6 is an example of the human view of the entities, attributes, and relationships. The purpose of this data dictionary is to ensure that all members of database design and implementation teams use the same table and attribute names and characteristics. The DBMS’s internally stored data dictionary contains additional informa- tion about relationship types, entity and referential integrity checks and enforcement, and index types and components. This additional information is generated during the data- base implementation stage.

92 Part 2 Design Concepts
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TABLE 3.6
A SAMPLE DATA DICTIONARY
TABLE NAME
ATTRIBUTE NAME
CONTENTS
TYPE FORMAT
RANGE
REQUIRED PK OR FK FK REFERENCED
CUSTOMER
CUS_CODE CUS_LNAME CUS_FNAME CUS_INITIAL CUS_RENEW_DATE
Customer account code
CHAR(5) 99999 VARCHAR(20) Xxxxxxxx VARCHAR(20) Xxxxxxxx CHAR(1) X
10000–99999
Y PK Y
Y
AGENT
Agent code
FK
PK
CHAR VARCHAR NUMBER
= Foreign key
Note
AGENT_CODE AGENT_CODE AGENT_AREACODE AGENT_PHONE
Agent code
CHAR(3) 999 CHAR(3) 999 CHAR(3) 999 CHAR(8) 999–9999
FK AGENT Y PK
AGENT_LNAME AGENT_YTD_SLS
Agent last name
Agent year-to-date sales
VARCHAR(20) Xxxxxxxx NUMBER(9,2) 9,999,999.99
Y
= Primary key
Customer last name
Customer first name
Customer initial
Customer insurance renewal date
DATE dd-mmm-yyyy
Agent area code
Y Y
Agent telephone number
= Fixed character length data (1 – 255 characters)
= Variable character length data (1 – 2,000 characters)
= Numeric data. NUMBER (9,2) is used to specify numbers with up to nine digits, including two digits to the right of the decimal place. Some RDBMS permit the use of a MONEY or CURRENCY data type.
Telephone area codes are always composed of digits 0−9, but because area codes are not used arithmetically, they are most efficiently stored as character data. Also, the area codes are always composed of three digits. Therefore, the area code data type is defined as CHAR(3). On the other hand, names do not conform to a standard length. Therefore, the customer first names are defined as VARCHAR(20), indicating that up to 20 characters may be used to store the names. Character data are shown as left-aligned.
TABLE

In a database context, a synonym is the opposite of a homonym, and indicates the use of different names to describe the same attribute. For example, car and auto refer to the same object. Synonyms must be avoided whenever possible.
3-6 Relationships within the Relational Database
You already know that relationships are classified as one-to-one (1:1), one-to-many (1:M), and many-to-many (M:N or M:M). This section explores those relationships fur- ther to help you apply them properly when you start developing database designs. This section focuses on the following points:
• The 1:M relationship is the relational modeling ideal. Therefore, this relationship type should be the norm in any relational database design.
• The 1:1 relationship should be rare in any relational database design.
• M:N relationships cannot be implemented as such in the relational model. Later in this section, you will see how any M:N relationship can be changed into two 1:M relationships.
3-6a The 1:M Relationship
The 1:M relationship is the norm for relational databases. To see how such a relationship is modeled and implemented, consider the PAINTER and PAINTING example shown in Figure 3.17.
Compare the data model in Figure 3.17 with its implementation in Figure 3.18.
As you examine the PAINTER and PAINTING table contents in Figure 3.18, note the following features:
• •
Each painting was created by one and only one painter, but each painter could have created many paintings. Note that painter 123 (Georgette P. Ross) has three works stored in the PAINTING table.
There is only one row in the PAINTER table for any given row in the PAINTING table, but there may be many rows in the PAINTING table for any given row in the PAINTER table.
Note
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Chapter 3 The Relational Database Model 93
FIGURE 3.17 THE 1:M RELATIONSHIP BETWEEN PAINTER AND PAINTING
synonym
The use of different names to identify the same object, such as an
The one-to-many (1:M) relationship is easily implemented in the relational model by put- ting the primary key of the “1” side in the table of the “many” side as a foreign key.
entity, an attribute, or a relationship; synonyms should generally be avoided. See also homonym.

94 Part 2
Design Concepts
FIGURE 3.18 THE IMPLEMENTED 1:M RELATIONSHIP BETWEEN PAINTER AND PAINTING
Table name: PAINTER Primary key: PAINTER_NUM Foreign key: none
Table name: PAINTING Primary key: PAINTING_NUM Foreign key: PAINTER_NUM
Database name: Ch03_Museum
The 1:M relationship is found in any database environment. Students in a typical college or university will discover that each COURSE can generate many CLASSes but that each CLASS refers to only one COURSE. For example, an Accounting II course might yield two classes: one offered on Monday, Wednesday, and Friday (MWF) from 10:00 a.m. to 10:50 a.m., and one offered on Thursday (Th) from 6:00 p.m. to 8:40 p.m. Therefore, the 1:M relationship between COURSE and CLASS might be described this way:
• Each COURSE can have many CLASSes, but each CLASS references only one COURSE.
• There will be only one row in the COURSE table for any given row in the CLASS table, but there can be many rows in the CLASS table for any given row in the COURSE table.
Figure 3.19 maps the ERM (entity relationship model) for the 1:M relationship between COURSE and CLASS.
The 1:M relationship between COURSE and CLASS is further illustrated in Figure 3.20.
Using Figure 3.20, take a minute to review some important terminology. Note that CLASS_CODE in the CLASS table uniquely identifies each row. Therefore, CLASS_ CODE has been chosen to be the primary key. However, the combination CRS_CODE and CLASS_SECTION will also uniquely identify each row in the class table. In other
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FIGURE 3.19 THE 1:M RELATIONSHIP BETWEEN COURSE AND CLASS

words, the composite key composed of CRS_CODE and CLASS_SECTION is a candidate key. Any candidate key must have the not-null and unique constraints enforced. (You will see how this is done when you learn SQL in Chapter 7.)
For example, note in Figure 3.18 that the PAINTER table’s primary key, PAINTER_ NUM, is included in the PAINTING table as a foreign key. Similarly, in Figure 3.20, the COURSE table’s primary key, CRS_CODE, is included in the CLASS table as a foreign key.
3-6b The 1:1 Relationship
As the 1:1 label implies, one entity in a 1:1 relationship can be related to only one other entity, and vice versa. For example, one department chair—a professor—can chair only one department, and one department can have only one department chair. The entities PROFESSOR and DEPARTMENT thus exhibit a 1:1 relationship. (You might argue that not all professors chair a department and professors cannot be required to chair a depart- ment. That is, the relationship between the two entities is optional. However, at this stage of the discussion, you should focus your attention on the basic 1:1 relationship. Optional relationships will be addressed in Chapter 4.) The basic 1:1 relationship is modeled in Figure 3.21, and its implementation is shown in Figure 3.22.
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Chapter 3 The Relational Database Model 95
FIGURE 3.20 THE IMPLEMENTED 1:M RELATIONSHIP BETWEEN COURSE AND CLASS
Table name: COURSE Primary key: CRS_CODE Foreign key: none
Table name: CLASS Primary key: CLASS_CODE Foreign key: CRS_CODE
Database name: Ch03_TinyCollege
FIGURE 3.21 THE 1:1 RELATIONSHIP BETWEEN PROFESSOR AND DEPARTMENT

96 Part 2
Design Concepts
As you examine the tables in Figure 3.22, note several important features:
• Each professor is a Tiny College employee. Therefore, the professor identification is through the EMP_NUM. (However, note that not all employees are professors— there’s another optional relationship.)
• The 1:1 “PROFESSOR chairs DEPARTMENT” relationship is implemented by having the EMP_NUM foreign key in the DEPARTMENT table. Note that the 1:1 relationship is treated as a special case of the 1:M relationship in which the “many” side is restricted to a single occurrence. In this case, DEPARTMENT con- tains the EMP_NUM as a foreign key to indicate that it is the department that has a chair.
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FIGURE 3.22 THE IMPLEMENTED 1:1 RELATIONSHIP BETWEEN PROFESSOR AND DEPARTMENT
Table name: PROFESSOR Database name: Ch03_TinyCollege Primary key: EMP_NUM
Foreign key: DEPT_CODE
The 1:M DEPARTMENT employs PROFESSOR relationship is implemented through the placement of the DEPT_CODE foreign key in the PROFESSOR table.
Table name: DEPARTMENT Primary key: DEPT_CODE Foreign key: EMP_NUM
The 1:1 PROFESSOR chairs DEPARTMENT relationship is implemented through the placement of the EMP_NUM foreign key in the DEPARTMENT table.

• Also note that the PROFESSOR table contains the DEPT_CODE foreign key to implement the 1:M “DEPARTMENT employs PROFESSOR” relationship. This is a good example of how two entities can participate in two (or even more) relationships simultaneously.
The preceding “PROFESSOR chairs DEPARTMENT” example illustrates a proper 1:1 relationship. In fact, the use of a 1:1 relationship ensures that two entity sets are not placed in the same table when they should not be. However, the existence of a 1:1 relationship sometimes means that the entity components were not defined properly. It could indicate that the two entities actually belong in the same table!
Although 1:1 relationships should be rare, certain conditions absolutely require their use. In Chapter 5, Advanced Data Modeling, you will explore a concept called a general- ization hierarchy, which is a powerful tool for improving database designs under specific conditions to avoid a proliferation of nulls. One characteristic of generalization hierar- chies is that they are implemented as 1:1 relationships.
3-6c The M:N Relationship
A many-to-many (M:N) relationship is not supported directly in the relational environ- ment. However, M:N relationships can be implemented by creating a new entity in 1:M relationships with the original entities.
To explore the many-to-many relationship, consider a typical college environment. The ER model in Figure 3.23 shows this M:N relationship.
Online
Content
If you open the Ch03_ TinyCollege database at www.cengagebrain. com, you will see that the STUDENT and CLASS entities still use PROF_NUM as their foreign key. PROF_ NUM and EMP_NUM are labels for the same attribute, which is an example of the use of synonyms—that is, different names for the same attribute. These synonyms will be elim- inated in future chap- ters as the Tiny College database continues to be improved.
Online
Content
If you look at the Ch03_AviaCo database at www.cengagebrain. com, you will see the implementation of the 1:1 PILOT to EMPLOYEE relationship. This rela- tionship is based on a generalization hierar- chy, which you will learn about in Chapter 5.
Chapter 3 The Relational Database Model 97
FIGURE 3.23 THE ERM’S M:N RELATIONSHIP BETWEEN STUDENT AND CLASS
Note the features of the ERM in Figure 3.23.
• Each CLASS can have many STUDENTs, and each STUDENT can take many CLASSes.
• There can be many rows in the CLASS table for any given row in the STUDENT table, and there can be many rows in the STUDENT table for any given row in the CLASS table.
To examine the M:N relationship more closely, imagine a small college with two students, each of whom takes three classes. Table 3.7 shows the enrollment data for the two students.
Given such a data relationship and the sample data in Table 3.7, you could wrongly assume that you could implement this M:N relationship simply by adding a foreign key in the “many” side of the relationship that points to the primary key of the related table, as shown in Figure 3.24.
However, the M:N relationship should not be implemented as shown in Figure 3.24 for two good reasons:
• The tables create many redundancies. For example, note that the STU_NUM values occur many times in the STUDENT table. In a real-world situation, additional stu- dent attributes such as address, classification, major, and home phone would also be contained in the STUDENT table, and each of those attribute values would be repeated in each of the records shown here. Similarly, the CLASS table contains much
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98 Part 2
Design Concepts
TABLE 3.7
SAMPLE STUDENT ENROLLMENT DATA
STUDENT’S LAST NAME
SELECTED CLASSES
Bowser
Accounting 1, ACCT-211, code 10014 Intro to Microcomputing, CIS-220, code 10018 Intro to Statistics, QM-261, code 10021
Smithson
Accounting 1, ACCT-211, code 10014 Intro to Microcomputing, CIS-220, code 10018 Intro to Statistics, QM-261, code 10021
FIGURE 3.24 THE WRONG IMPLEMENTATION OF THE M:N RELATIONSHIP BETWEEN STUDENT AND CLASS
Table name: STUDENT Primary key: STU_NUM Foreign key: none
Table name: CLASS Primary key: CLASS_CODE Foreign key: STU_NUM
Database name: Ch03_CollegeTry
duplication: each student taking the class generates a CLASS record. The problem would be even worse if the CLASS table included such attributes as credit hours and course description. Those redundancies lead to the anomalies discussed in Chapter 1.
• Given the structure and contents of the two tables, the relational operations become very complex and are likely to lead to system efficiency errors and output errors.
Fortunately, the problems inherent in the many-to-many relationship can easily be avoided by creating a composite entity (also referred to as a bridge entity or an associative entity). Because such a table is used to link the tables that were origi- nally related in an M:N relationship, the composite entity structure includes—as for- eign keys—at least the primary keys of the tables that are to be linked. The database designer has two main options when defining a composite table’s primary key: use the combination of those foreign keys or create a new primary key.
Remember that each entity in the ERM is represented by a table. Therefore, you can create the composite ENROLL table shown in Figure 3.25 to link the tables CLASS and STUDENT. In this example, the ENROLL table’s primary key is the combination of its foreign keys CLASS_CODE and STU_NUM. However, the designer could have decided to create a single-attribute new primary key such as ENROLL_LINE, using a different line value to identify each ENROLL table row uniquely. (Microsoft Access users might use the Autonumber data type to generate such line values automatically.)
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
composite entity
An entity designed
to transform an M:N relationship into two 1:M relationships. The composite entity’s primary key comprises at least the primary keys of the entities that it connects. Also known as a bridge entity or associative entity. See also linking table.
bridge entity
See composite entity. associative entity
See composite entity.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 3 The Relational Database Model 99
FIGURE3.25 CONVERTINGTHEM:NRELATIONSHIPINTOTWO1:MRELATIONSHIPS
Table name: STUDENT Primary key: STU_NUM Foreign key: none
Table name: ENROLL
Primary key: CLASS_CODE + STU_NUM Foreign key: CLASS_CODE, STU_NUM
Table name: CLASS Primary key: CLASS_CODE Foreign key: CRS_CODE
Database name: Ch03_CollegeTry2
Because the ENROLL table in Figure 3.25 links two tables, STUDENT and CLASS, it is also called a linking table. In other words, a linking table is the implementation of a composite entity.
Note
The ENROLL table shown in Figure 3.25 yields the required M:N to 1:M con- version. Observe that the composite entity represented by the ENROLL table must contain at least the primary keys of the CLASS and STUDENT tables (CLASS_ CODE and STU_NUM, respectively) for which it serves as a connector. Also note that the STUDENT and CLASS tables now contain only one row per entity. The ENROLL table contains multiple occurrences of the foreign key values, but those controlled redundancies are incapable of producing anomalies as long as referen- tial integrity is enforced. Additional attributes may be assigned as needed. In this case, ENROLL_GRADE is selected to satisfy a reporting requirement. Also note that ENROLL_GRADE is fully dependent on the composite primary key. Naturally, the conversion is reflected in the ERM, too. The revised relationship is shown in Figure 3.26.
As you examine Figure 3.26, note that the composite entity named ENROLL rep- resents the linking table between STUDENT and CLASS.
linking table
In the relational model, a table that implements an M:M relationship. See also composite entity.
In addition to the linking attributes, the composite ENROLL table can also contain such relevant attributes as the grade earned in the course. In fact, a composite table can contain any number of attributes that the designer wants to track. Keep in mind that the compos- ite entity, although implemented as an actual table, is conceptually a logical entity that was created as a means to an end: to eliminate the potential for multiple redundancies in the original M:N relationship.
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100 Part 2
Design Concepts
FIGURE3.26 CHANGINGTHEM:NRELATIONSHIPSTOTWO 1:M RELATIONSHIPS
The 1:M relationship between COURSE and CLASS was first illustrated in Figure 3.19 and Figure 3.20. You can increase the amount of available information even as you control the database’s redundancies. Thus, Figure 3.27 shows the expanded ERM, including the 1:M relationship between COURSE and CLASS shown in Figure 3.19. Note that the model can handle multiple sections of a CLASS while controlling redundancies by making sure that all of the COURSE data common to each CLASS are kept in the COURSE table.
The relational diagram that corresponds to the ERM in Figure 3.27 is shown in Figure 3.28.
The ERM will be examined in greater detail in Chapter 4 to show you how it is used to design more complex databases. The ERM will also be used as the basis for developing and implementing a realistic database design of a university computer lab in Appendixes B and C. These appendixes are available at www.cengagebrain.com.
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FIGURE 3.27 THE EXPANDED ER MODEL
FIGURE 3.28 THE RELATIONAL DIAGRAM FOR THE CH03_TINYCOLLEGE DATABASE

3-7 Data Redundancy Revisited
In Chapter 1, you learned that data redundancy leads to data anomalies, which can destroy the effectiveness of the database. You also learned that the relational database makes it possible to control data redundancies by using common attributes that are shared by tables, called foreign keys.
The proper use of foreign keys is crucial to controlling data redundancy, although they do not totally eliminate the problem because the foreign key values can be repeated many times. However, the proper use of foreign keys minimizes data redundancies and the chances that destructive data anomalies will develop.
Note
Chapter 3 The Relational Database Model 101
The real test of redundancy is not how many copies of a given attribute are stored, but whether the elimination of an attribute will eliminate information. Therefore, if you delete an attribute and the original information can still be generated through relational alge- bra, the inclusion of that attribute would be redundant. Given that view of redundancy, proper foreign keys are clearly not redundant in spite of their multiple occurrences in a table. However, even when you use this less restrictive view of redundancy, keep in mind that controlled redundancies are often designed as part of the system to ensure transaction speed and/or information requirements.
You will learn in Chapter 4 that database designers must reconcile three often contra- dictory requirements: design elegance, processing speed, and information requirements. Also, you will learn in Chapter 13, Business Intelligence and Data Warehouses, that proper data warehousing design requires carefully defined and controlled data redun- dancies to function properly. Regardless of how you describe data redundancies, the potential for damage is limited by proper implementation and careful control.
As important as it is to control data redundancy, sometimes the level of data redun- dancy must actually be increased to make the database serve crucial information pur- poses. You will learn about such redundancies in Chapter 13. Also, data redundancies sometimes seem to exist to preserve the historical accuracy of the data. For example, consider a small invoicing system. The system includes the CUSTOMER, who may buy one or more PRODUCTs, thus generating an INVOICE. Because a customer may buy more than one product at a time, an invoice may contain several invoice LINEs, each providing details about the purchased product. The PRODUCT table should contain the product price to provide a consistent pricing input for each product that appears on the invoice. The tables that are part of such a system are shown in Figure 3.29. The system’s relational diagram is shown in Figure 3.30.
As you examine the tables and relationships in the two figures, note that you can keep track of typical sales information. For example, by tracing the relationships among the four tables, you discover that customer 10014 (Myron Orlando) bought two items on March 8, 2016, that were written to invoice number 1001: one Houselite chain saw with a 16-inch bar and three rat-tail files. In other words, trace the CUS_ CODE number 10014 in the CUSTOMER table to the matching CUS_CODE value in the INVOICE table. Next, trace the INV_NUMBER 1001 to the first two rows in the LINE table. Finally, match the two PROD_CODE values in LINE with the PROD_CODE values in PRODUCT. Application software will be used to write the correct bill by multiplying each invoice line item’s LINE_UNITS by its LINE_PRICE, adding the results, and applying appropriate taxes. Later, other application software
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102 Part 2
Design Concepts
FIGURE3.29 ASMALLINVOICINGSYSTEM
Table name: CUSTOMER Primary key: CUS_CODE Foreign key: none
Table name: INVOICE Primary key: INV_NUMBER Foreign key: CUS_CODE
Table name: PRODUCT Primary key: PROD_CODE Foreign key: none
Database name: Ch03_SaleCo
Table name: LINE
Primary key: INV_NUMBER + LINE_NUMBER Foreign key: INV_NUMBER, PROD_CODE
FIGURE 3.30 THE RELATIONAL DIAGRAM FOR THE INVOICING SYSTEM
might use the same technique to write sales reports that track and compare sales by week, month, or year.
As you examine the sales transactions in Figure 3.29, you might reasonably suppose that the product price billed to the customer is derived from the PRODUCT table because the product data is stored there. But why does that same product price occur again in the LINE table? Is that not a data redundancy? It certainly appears to be, but this time, the apparent redundancy is crucial to the system’s success. Copying the prod- uct price from the PRODUCT table to the LINE table maintains the historical accuracy of the transactions. Suppose, for instance, that you fail to write the LINE_PRICE in the LINE table and that you use the PROD_PRICE from the PRODUCT table to calculate the sales revenue. Now suppose that the PRODUCT table’s PROD_PRICE changes, as prices frequently do. This price change will be properly reflected in all subsequent sales revenue calculations. However, the calculations of past sales revenues will also reflect the new product price, which was not in effect when the transaction took place! As a
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result, the revenue calculations for all past transactions will be incorrect, thus elim- inating the possibility of making proper sales comparisons over time. On the other hand, if the price data is copied from the PRODUCT table and stored with the transac- tion in the LINE table, that price will always accurately reflect the transaction that took place at that time. You will discover that such planned “redundancies” are common in good database design.
Finally, you might wonder why the LINE_NUMBER attribute was used in the LINE table in Figure 3.29. Wouldn’t the combination of INV_NUMBER and PROD_CODE be a sufficient composite primary key—and, therefore, isn’t the LINE_NUMBER redundant? Yes, it is, but this redundancy is common practice on invoicing software that typically generates such line numbers automatically. In this case, the redundancy is not necessary, but given its automatic generation, the redundancy is not a source of anomalies. The inclusion of LINE_NUMBER also adds another benefit: the order of the retrieved invoic- ing data will always match the order in which the data was entered. If product codes are used as part of the primary key, indexing will arrange those product codes as soon as the invoice is completed and the data is stored. You can imagine the potential confusion when a customer calls and says, “The second item on my invoice has an incorrect price,” and you are looking at an invoice whose lines show a different order from those on the customer’s copy!
3-8 Indexes
Suppose you want to locate a book in a library. Does it make sense to look through every book until you find the one you want? Of course not; you use the library’s cat- alog, which is indexed by title, topic, and author. The index (in either a manual or computer library catalog) points you to the book’s location, making retrieval a quick and simple matter. An index is an orderly arrangement used to logically access rows in a table.
Or, suppose you want to find a topic in this book, such as ER model. Does it make sense to read through every page until you stumble across the topic? Of course not; it is much simpler to go to the book’s index, look up the phrase ER model, and read the ref- erences that point you to the appropriate page(s). In each case, an index is used to locate a needed item quickly.
Indexes in the relational database environment work like the indexes described in the preceding paragraphs. From a conceptual point of view, an index is composed of an index key and a set of pointers. The index key is, in effect, the index’s reference point. More formally, an index is an ordered arrangement of keys and pointers. Each key points to the location of the data identified by the key.
For example, suppose you want to look up all of the paintings created by a given painter in the Ch03_Museum database in Figure 3.18. Without an index, you must read each row in the PAINTING table and see if the PAINTER_NUM matches the requested painter. However, if you index the PAINTER table and use the index key PAINTER_ NUM, you merely need to look up the appropriate PAINTER_NUM in the index and find the matching pointers. Conceptually speaking, the index would resemble the pre- sentation in Figure 3.31.
As you examine Figure 3.31, note that the first PAINTER_NUM index key value (123) is found in records 1, 2, and 4 of the PAINTING table. The second PAINTER_NUM index key value (126) is found in records 3 and 5 of the PAINTING table.
DBMSs use indexes for many different purposes. You just learned that an index can be used to retrieve data more efficiently, but indexes can also be used by a DBMS
index
An ordered array of index key values and row ID values (pointers). Indexes are generally used to speed up and facilitate data retrieval. Also known as an index key.
index key
See index.
Chapter 3 The Relational Database Model 103
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104 Part 2
Design Concepts
FIGURE3.31 COMPONENTSOFANINDEX
PAINTING table index
PAINTING table
123 126
PAINTER_NUM (index key)
1, 2, 4 3, 5
Pointers to the PAINTING table rows
to retrieve data ordered by a specific attribute or attributes. For example, creating an index on a customer’s last name will allow you to retrieve the customer data alphabet- ically by the customer’s last name. Also, an index key can be composed of one or more attributes. For example, in Figure 3.29, you can create an index on VEND_CODE and PROD_CODE to retrieve all rows in the PRODUCT table ordered by vendor, and within vendor, ordered by product.
Indexes play an important role in DBMSs for the implementation of primary keys. When you define a table’s primary key, the DBMS automatically creates a unique index on the primary key column(s) you declared. For example, in Figure 3.29, when you declare CUS_CODE to be the primary key of the CUSTOMER table, the DBMS auto- matically creates a unique index on that attribute. In a unique index, as its name implies, the index key can have only one pointer value (row) associated with it. (The index in Fig- ure 3.31 is not a unique index because the PAINTER_NUM has multiple pointer values associated with it. For example, painter number 123 points to three rows—1, 2, and 4—in the PAINTING table.)
A table can have many indexes, but each index is associated with only one table. The index key can have multiple attributes (a composite index). Creating an index is easy. You will learn in Chapter 7 that a simple SQL command produces any required index.
3-9 Codd’s Relational Database Rules
In 1985, Dr. E. F. Codd published a list of 12 rules to define a relational database system.1 He published the list out of concern that many vendors were marketing products as “relational” even though those products did not meet minimum relational standards. Dr. Codd’s list, shown in Table 3.8, is a frame of reference for what a truly relational database should be. Bear in mind that even the dominant database vendors do not fully support all 12 rules.
1Codd, E., “Is Your DBMS Really Relational?” and “Does Your DBMS Run by the Rules?” Computerworld, October 14 and October 21, 1985.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
unique index
An index in which the index key can have only one associated pointer value (row).
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 3 The Relational Database Model 105
TABLE 3.8
DR. CODD’S 12 RELATIONAL DATABASE RULES
RULE
RULE NAME
DESCRIPTION
1
Information
All information in a relational database must be logically represented as column values in rows within tables.
2
Guaranteed access
Every value in a table is guaranteed to be accessible through a combination of table name, primary key value, and column name.
3
Systematic treatment of nulls
Nulls must be represented and treated in a systematic way, independent of data type.
4
Dynamic online catalog based on the relational model
The metadata must be stored and managed as ordinary data—that is, in tables within the database; such data must be available to authorized users using the standard database relational language.
5
Comprehensive data sublanguage
The relational database may support many languages; however, it must support one well-defined, declarative language as well as data definition, view definition, data manipulation (interactive and by program), integrity constraints, authorization, and transaction management (begin, commit, and rollback).
6
View updating
Any view that is theoretically updatable must be updatable through the system.
7
High-level insert, update, and delete
The database must support set-level inserts, updates, and deletes.
8
Physical data independence
Application programs and ad hoc facilities are logically unaffected when physical access methods or storage structures are changed.
9
Logical data independence
Application programs and ad hoc facilities are logically unaffected when changes are made to the table structures that preserve the original table values (changing order of columns or inserting columns).
10
Integrity independence
All relational integrity constraints must be definable in the relational language and stored in the system catalog, not at the application level.
11
Distribution independence
The end users and application programs are unaware of and unaffected by the data location (distributed vs. local databases).
12
Nonsubversion
If the system supports low-level access to the data, users must not be allowed to bypass the integrity rules of the database.
13
Rule zero
All preceding rules are based on the notion that to be considered relational, a database must use its relational facilities exclusively for management.
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106 Part 2 Design Concepts
Summary
• Tables are the basic building blocks of a relational database. A grouping of related entities, known as an entity set, is stored in a table. Conceptually speaking, the rela- tional table is composed of intersecting rows (tuples) and columns. Each row rep- resents a single entity, and each column represents the characteristics (attributes) of the entities.
• Keys are central to the use of relational tables. Keys define functional dependencies; that is, other attributes are dependent on the key and can therefore be found if the key value is known. A key can be classified as a superkey, a candidate key, a primary key, a secondary key, or a foreign key.
• Each table row must have a primary key. The primary key is an attribute or combina- tion of attributes that uniquely identifies all remaining attributes found in any given row. Because a primary key must be unique, no null values are allowed if entity integ- rity is to be maintained.
• Although tables are independent, they can be linked by common attributes. Thus, the primary key of one table can appear as the foreign key in another table to which it is linked. Referential integrity dictates that the foreign key must contain values that match the primary key in the related table, or must contain nulls.
• The relational model supports several relational algebra functions, including SELECT, PROJECT, JOIN, INTERSECT, UNION, DIFFERENCE, PRODUCT, and DIVIDE. Understanding the basic mathematical forms of these functions gives a broader understanding of the data manipulation options.
• A relational database performs much of the data manipulation work behind the scenes. For example, when you create a database, the RDBMS automatically produces a structure to house a data dictionary for your database. Each time you create a new table within the database, the RDBMS updates the data dictionary, thereby providing the database documentation.
• Once you know the basics of relational databases, you can concentrate on design. Good design begins by identifying appropriate entities and their attributes and then the relationships among the entities. Those relationships (1:1, 1:M, and M:N) can be represented using ERDs. The use of ERDs allows you to create and evaluate simple logical design. The 1:M relationship is most easily incorpo- rated in a good design; just make sure that the primary key of the “1” is included in the table of the “many.”
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1. What is the difference between a database and a table?
Online Content
All of the databases used in the questions and prob- lems are available at www. cengagebrain.com. The data- base names match the database names shown in the figures.
Chapter 3 The Relational Database Model 107
2. What does it mean to say that a database displays both entity integrity and referential integrity?
3. Why are entity integrity and referential integrity important in a database?
4. What are the requirements that two relations must satisfy to be considered union-compatible?
5. Which relational algebra operators can be applied to a pair of tables that are not union-compatible?
6. Explain why the data dictionary is sometimes called “the database designer’s database.”
7. A database user manually notes that “The file contains two hundred records, each record containing nine fields.” Use appropriate relational database terminology to “translate” that statement.
Use Figure Q3.8 to answer Questions 8–12.
8. Using the STUDENT and PROFESSOR tables, illustrate the difference between a natural join, an equijoin, and an outer join.
9. Create the table that would result from πstu_code (student).
10. Create the table that would result from πstu_code, dept_code (student ⨝ professor).
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Key Terms
associative entity attribute domain bridge entity candidate key closure composite entity composite key data dictionary dependent determinant determination DIFFERENCE DIVIDE
domain
entity integrity equijoin
flags
foreign key (FK)
full functional dependence functional dependence homonym
index
index key inner join INTERSECT JOIN
join column(s) key
key attribute left outer join linking table natural join null
outer join predicate logic primary key (PK)
PRODUCT PROJECT referential integrity relational algebra relvar
RESTRICT
right outer join secondary key SELECT
set theory superkey synonym
system catalog theta join
tuple
UNION union-compatible unique index
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
Review Questions

108 Part 2
Design Concepts
11. Create the basic ERD for the database shown in Figure Q3.8.
12. Create the relational diagram for the database shown in Figure Q3.8.
FIGURE Q3.8 THE CH03_COLLEGEQUE DATABASE TABLES
Database name: Ch03_CollegeQue Table name: STUDENT
Table name: PROFESSOR
Use Figure Q3.13 to answer Questions 13–17.
FIGURE Q3.13 THE CH03_VENDINGCO DATABASE TABLES
Database name: Ch03_VendingCo Table name: BOOTH Table name: MACHINE
13. Write the relational algebra formula to apply a UNION relational operator to the tables shown in Figure Q3.13.
14. CreatethetablethatresultsfromapplyingaUNIONrelationaloperatortothetables shown in Figure Q3.13.
15. WritetherelationalalgebraformulatoapplyanINTERSECTrelationaloperatorto the tables shown in Figure Q3.13.
16. CreatethetablethatresultsfromapplyinganINTERSECTrelationaloperatortothe tables shown in Figure Q3.13.
17. Using the tables in Figure Q3.13, create the table that results from MACHINE DIFFERENCE BOOTH.
Use Figure Q3.18 to answer Question 18.
FIGURE Q3.18 THE CROW’S FOOT ERD FOR QUESTION 14
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18. Suppose you have the ERD shown in Figure Q3.18. How would you convert this model into an ERM that displays only 1:M relationships? (Make sure you create the revised ERD.)
19. What are homonyms and synonyms, and why should they be avoided in database design?
20. Howwouldyouimplemental:Mrelationshipinadatabasecomposedoftwotables? Give an example.
Use Figure Q3.21 to answer Question 21.
21. IdentifyanddescribethecomponentsofthetableshowninFigureQ3.21,usingcor- rect terminology. Use your knowledge of naming conventions to identify the table’s probable foreign key(s).
Use the database shown in Figure Q3.22 to answer Questions 22–27.
Chapter 3 The Relational Database Model 109
FIGURE Q3.21 THE CH03_NOCOMP DATABASE EMPLOYEE TABLE
Table name: EMPLOYEE Database name: Ch03_NoComp
FIGURE Q3.22 THE CH03_THEATER DATABASE TABLES
Database name: Ch03_Theater Table name: DIRECTOR
Table name: PLAY
22. Identify the primary keys.
23. Identify the foreign keys.
24. Create the ERM.
25. Create the relational diagram to show the relationship between DIRECTOR and PLAY.
26. Supposeyouwantedquicklookupcapabilitytogetalistingofallplaysdirectedbya given director. Which table would be the basis for the INDEX table, and what would be the index key?
27. What would be the conceptual view of the INDEX table described in Question 26? Depict the contents of the conceptual INDEX table.
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110 Part 2 Design Concepts
Problems
FIGURE P3.1 THE CH03_STORECO DATABASE TABLES
Table name: EMPLOYEE Database name: Ch03_StoreCo
Table name: STORE
Table name: REGION
Use the database shown in Figure P3.1 to answer Problems 1–9.
1. For each table, identify the primary key and the foreign key(s). If a table does not
have a foreign key, write None.
2. Do the tables exhibit entity integrity? Answer yes or no, and then explain your
answer.
3. Do the tables exhibit referential integrity? Answer yes or no, and then explain
your answer. Write NA (Not Applicable) if the table does not have a foreign key.
4. Describe the type(s) of relationship(s) between STORE and REGION.
5. Create the ERD to show the relationship between STORE and REGION.
6. Create the relational diagram to show the relationship between STORE and REGION.
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Chapter 3 The Relational Database Model 111
7. Describe the type(s) of relationship(s) between EMPLOYEE and STORE.
(Hint: Each store employs many employees, one of whom manages the store.)
8. Create the ERD to show the relationships among EMPLOYEE, STORE, and
REGION.
9. Create the relational diagram to show the relationships among EMPLOYEE, STORE, and REGION.
Use the database shown in Figure P3.10 to work Problems 10–16. Note that the database is composed of four tables that reflect these relationships:
• An EMPLOYEE has only one JOB_CODE, but a JOB_CODE can be held by many EMPLOYEEs.
• An EMPLOYEE can participate in many PLANs, and any PLAN can be assigned to many EMPLOYEEs.
Note also that the M:N relationship has been broken down into two 1:M relationships for which the BENEFIT table serves as the composite or bridge entity.
10. For each table in the database, identify the primary key and the foreign key(s). If a
table does not have a foreign key, write None.
11. Create the ERD to show the relationship between EMPLOYEE and JOB.
12. Create the relational diagram to show the relationship between EMPLOYEE and JOB.
13. Do the tables exhibit entity integrity? Answer yes or no, and then explain your answer.
14. Do the tables exhibit referential integrity? Answer yes or no, and then explain your answer. Write NA (Not Applicable) if the table does not have a foreign key.
15. CreatetheERDtoshowtherelationshipsamongEMPLOYEE,BENEFIT,JOB,and PLAN.
16. Create the relational diagram to show the relationships among EMPLOYEE, BENEFIT, JOB, and PLAN.
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FIGURE P3.10 THE CH03_BENECO DATABASE TABLES
Table name: EMPLOYEE Table name: BENEFIT
Table name: JOB Table name: PLAN
Database name: Ch03_BeneCo

112 Part 2
Design Concepts
FIGURE P3.17 THE CH03_TRANSCO DATABASE TABLES
Table name: TRUCK
Primary key: TRUCK_NUM
Foreign key: BASE_CODE, TYPE_CODE
Table name: BASE Primary key: BASE_CODE Foreign key: none
Table name: TYPE Primary key: TYPE_CODE Foreign key: none
Database name: Ch03_TransCo
Use the database shown in Figure P3.17 to answer Problems 17–23.
17. For each table, identify the primary key and the foreign key(s). If a table does not
have a foreign key, write None.
18. Do the tables exhibit entity integrity? Answer yes or no, and then explain your
answer.
19. Do the tables exhibit referential integrity? Answer yes or no, and then explain your
answer. Write NA (Not Applicable) if the table does not have a foreign key.
20. Identify the TRUCK table’s candidate key(s).
21. For each table, identify a superkey and a secondary key.
22. Create the ERD for this database.
23. Create the relational diagram for this database.
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Use the database shown in Figure P3.24 to answer Problems 24–31. AviaCo is an air- craft charter company that supplies on-demand charter flight services using a fleet of four aircraft. Aircraft are identified by a unique registration number. Therefore, the aircraft registration number is an appropriate primary key for the AIRCRAFT table.
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Chapter 3 The Relational Database Model 113
FIGURE P3.24 THE CH03_AVIACO DATABASE TABLES
Table name: CHARTER Database name: Ch03_AviaCo
The destinations are indicated by standard three-letter airport codes. For example,
STL = St. Louis, MO Table name: AIRCRAFT
Table name: MODEL
ATL = Atlanta, GA
BNA = Nashville, TN
AC-TTAF = Aircraft total time, airframe (hours) AC-TTEL = Total time, left engine (hours) AC_TTER = Total time, right engine (hours)
In a fully developed system, such attribute values would be updated by application software when the CHARTER table entries were posted.
Customers are charged per round-trip mile, using the MOD_CHG_MILE rate. The MOD_SEATS column lists the total number of seats in the airplane, including the pilot and copilot seats. Therefore, a PA31-350 trip that is flown by a pilot and a copilot has eight passenger seats available.

114 Part 2
Design Concepts
FIGURE P3.24 THE CH03_AVIACO DATABASE TABLES (CONTINUED)
Table name: PILOT Database name: Ch03_AviaCo
The pilot licenses shown in the PILOT table include ATP = Airline Transport Pilot and COMM = Commercial Pilot. Businesses that operate on-demand air services are governed by Part 135 of the Federal Air Regulations (FARs), which are enforced by the Federal Aviation Administration (FAA). Such businesses are known as “Part 135 operators.” Part 135 operations require that pilots successfully complete flight proficiency checks every six months. The “Part 135” flight proficiency check date is recorded in PIL_PT135_DATE. To fly commercially, pilots must have at least a commercial license and a second-class medical certificate (PIL_MED_TYPE = 2).
The PIL_RATINGS include:
SEL = Single Engine, Land
SES = Single Engine, Sea
CFI = Certified Flight Instructor
Table name: EMPLOYEE
MEL = Multiengine, Land
Instr. = Instrument
CFII = Certified Flight Instructor, Instrument
Table name: CUSTOMER
The nulls in the CHARTER table’s CHAR_COPILOT column indicate that a copilot is not required for some charter trips or for some aircraft. Federal Aviation Administration (FAA) rules require a copilot on jet aircraft and on aircraft that have a gross take-off weight over 12,500 pounds. None of the aircraft in the AIRCRAFT table are governed by this requirement; however, some customers may require the presence of a copilot for insurance reasons. All charter trips are recorded in the CHARTER table.
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Note
Chapter 3
The Relational Database Model 115
Earlier in the chapter, you were instructed to avoid homonyms and synonyms. In this prob- lem, both the pilot and the copilot are listed in the PILOT table, but EMP_NUM cannot be used for both in the CHARTER table. Therefore, the synonyms CHAR_PILOT and CHAR_ COPILOT were used in the CHARTER table.
Although the solution works in this case, it is very restrictive, and it generates nulls when a copilot is not required. Worse, such nulls proliferate as crew requirements change. For example, if the AviaCo charter company grows and starts using larger air- craft, crew requirements may increase to include flight engineers and load masters. The CHARTER table would then have to be modified to include the additional crew assignments; such attributes as CHAR_FLT_ENGINEER and CHAR_LOADMASTER would have to be added to the CHARTER table. Given this change, each time a smaller aircraft flew a charter trip without the number of crew members required in larger aircraft, the missing crew members would yield additional nulls in the CHARTER table.
You will have a chance to correct those design shortcomings in Problem 27. The problem illustrates two important points:
1. Don’t use synonyms. If your design requires the use of synonyms, revise the design!
2. To the greatest possible extent, design the database to accommodate growth without requiring structural changes in the database tables. Plan ahead and try to anticipate the effects of change on the database.
24. For each table, identify each of the following when possible:
a. The primary key
b. A superkey
c. A candidate key
d. The foreign key(s)
e. A secondary key
25. Create the ERD. (Hint: Look at the table contents. You will discover that an AIR- CRAFT can fly many CHARTER trips but that each CHARTER trip is flown by one AIRCRAFT, that a MODEL references many AIRCRAFT but that each AIRCRAFT references a single MODEL, and so on.)
26. Create the relational diagram.
27. Modify the ERD you created in Problem 25 to eliminate the problems created by the use of synonyms. (Hint: Modify the CHARTER table structure by eliminating the CHAR_PILOT and CHAR_COPILOT attributes; then create a composite table named CREW to link the CHARTER and EMPLOYEE tables. Some crew members, such as flight attendants, may not be pilots. That’s why the EMPLOYEE table enters into this relationship.)
28. Create the relational diagram for the design you revised in Problem 27.
You want to see data on charters flown by either Robert Williams (employee number 105) or Elizabeth Travis (employee number 109) as pilot or copilot, but not charters flown by both of them. Complete Problems 29–31 to find this information.
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116 Part 2
Design Concepts
29. Create the table that would result from applying the SELECT and PROJECT relational operators to the CHARTER table to return only the CHAR_TRIP, CHAR_PILOT, and CHAR_COPILOT attributes for charters flown by either employee 105 or employee 109.
30. Create the table that would result from applying the SELECT and PROJECT rela- tional operators to the CHARTER table to return only the CHAR_TRIP, CHAR_ PILOT, and CHAR_COPILOT attributes for charters flown by both employee 105 and employee 109.
31. CreatethetablethatwouldresultfromapplyingaDIFFERENCErelationaloperator of your result from Problem 29 to your result from Problem 30.
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Chapter 4
Entity Relationship (ER) Modeling
In this chapter, you will learn:
• The main characteristics of entity relationship components
• How relationships between entities are defined, refined, and incorporated into the
database design process
• How ERD components affect database design and implementation
• That real-world database design often requires the reconciliation of conflicting goals
Preview
This chapter expands coverage of the data-modeling aspect of database design. Data modeling is the first step in the database design journey, serving as a bridge between real-world objects and the database model that is implemented in the computer. There- fore, the importance of data-modeling details, expressed graphically through entity relationship diagrams (ERDs), cannot be overstated.
Most of the basic concepts and definitions used in the entity relationship model (ERM) were introduced in Chapter 2, Data Models. For example, the basic components of entities and relationships and their representation should now be familiar to you. This chapter goes much deeper, analyzing the graphic depiction of relationships among the entities and showing how those depictions help you summarize the wealth of data required to implement a successful design.
Finally, the chapter illustrates how conflicting goals can be a challenge in database design and might require design compromises.
Data Files and Available Formats
CH04_TinyCollege P P P P CH04_Clinic P P P P CH04_TinyCollege_Alt P P P P CH04_PartCo P P P P CH04_ShortCo P P P P CH04_CollegeTry P P P P
Data Files Available on cengagebrain.com
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
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118 Part 2 Note
Design Concepts
Because this book generally focuses on the relational model, you might be tempted to conclude that the ERM is exclusively a relational tool. Actually, conceptual models such as the ERM can be used to understand and design the data requirements of an organization. Therefore, the ERM is independent of the database type. Conceptual models are used in the conceptual design of databases, while relational models are used in the logical design of databases. However, because you are familiar with the relational model from the pre- vious chapter, the relational model is used extensively in this chapter to explain ER con- structs and the way they are used to develop database designs.
4-1 The Entity Relationship Model (ERM)
You should remember from Chapter 2, Data Models, and Chapter 3, The Relational Data- base Model, that the ERM forms the basis of an ERD. The ERD represents the concep- tual database as viewed by the end user. ERDs depict the database’s main components: entities, attributes, and relationships. Because an entity represents a real-world object, the words entity and object are often used interchangeably. Thus, the entities (objects) of the Tiny College database design developed in this chapter include students, classes, teachers, and classrooms. The order in which the ERD components are covered in the chapter is dictated by the way the modeling tools are used to develop ERDs that can form the basis for successful database design and implementation.
In Chapter 2, you also learned about the various notations used with ERDs—the original Chen notation and the newer Crow’s Foot and UML notations. The first two notations are used at the beginning of this chapter to introduce some basic ER model- ing concepts. Some conceptual database modeling concepts can be expressed only using the Chen notation. However, because the emphasis is on design and implementation of databases, the Crow’s Foot and UML class diagram notations are used for the final Tiny College ER diagram example. Because of its emphasis on implementation, the Crow’s Foot notation can represent only what could be implemented. In other words:
• The Chen notation favors conceptual modeling.
• The Crow’s Foot notation favors a more implementation-oriented approach.
• The UML notation can be used for both conceptual and implementation modeling.
Content
To learn how to create ER diagrams with the help of Microsoft Visio, go to www.cengagebrain.com: Appendix A, Designing Databases with Visio Professional: A Tutorial, shows you how to cre- ate Crow’s Foot ERDs. Appendix H, Unified Modeling Language (UML), shows you how to create UML class diagrams.
Online
4-1a Entities
Recall that an entity is an object of interest to the end user. In Chapter 2, you learned that, at the ER modeling level, an entity actually refers to the entity set and not to a single entity occurrence. In other words, an entity in the ERM corresponds to a table—not to a row—in the relational environment. The ERM refers to a table row as an entity instance or entity occurrence. In the Chen, Crow’s Foot, and UML notations, an entity is repre- sented by a rectangle that contains the entity’s name. The entity name, a noun, is usually written in all capital letters.
4-1b Attributes
Attributes are characteristics of entities. For example, the STUDENT entity includes the attributes STU_LNAME, STU_FNAME, and STU_INITIAL, among many others. In the original Chen notation, attributes are represented by ovals and are connected
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to the entity rectangle with a line. Each oval contains the name of the attribute it represents. In the Crow’s Foot notation, the attributes are written in the attribute box below the entity rectangle. (See Figure 4.1.) Because the Chen representation con- sumes more space, software vendors have adopted the Crow’s Foot attribute display.
Chapter 4 Entity Relationship (ER) Modeling 119
FIGURE 4.1 THE ATTRIBUTES OF THE STUDENT ENTITY: CHEN AND CROW’S FOOT
Chen Model
STU_INITIAL
STU_FNAME
STU_LNAME STUDENT
Crow’s Foot Model
STU_EMAIL STU_PHONE
Required and Optional Attributes A required attribute is an attribute that must have a value; in other words, it cannot be left empty. As shown in Figure 4.1, the two boldfaced attributes in the Crow’s Foot notation indicate that data entry will be required. STU_LNAME and STU_FNAME require data entries because all students are assumed to have a last name and a first name. However, students might not have a middle name, and perhaps they do not yet have a phone number and an email address. Therefore, those attributes are not presented in boldface in the entity box. An optional attribute is an attribute that does not require a value; therefore, it can be left empty.
Domains Attributes have a domain. As you learned in Chapter 3, a domain is the set of possible values for a given attribute. For example, the domain for a grade point average (GPA) attribute is written (0,4) because the lowest possible GPA value is 0 and the highest possible value is 4. The domain for a gender attribute consists of only two possibilities: M or F (or some other equivalent code). The domain for a company’s date of hire attribute consists of all dates that fit in a range (for example, company startup date to current date).
Attributes may share a domain. For instance, a student address and a professor address share the same domain of all possible addresses. In fact, the data dictionary may let a newly declared attribute inherit the characteristics of an existing attribute if the same attribute name is used. For example, the PROFESSOR and STUDENT entities may each have an attribute named ADDRESS and could therefore share a domain.
Identifiers (Primary Keys) The ERM uses identifiers—one or more attributes that uniquely identify each entity instance. In the relational model, entities are mapped to tables, and the entity identifier is mapped as the table’s primary key (PK). Identifiers are underlined in the ERD. Key attributes are also underlined in a frequently used shorthand notation for the table structure, called a relational schema, that uses the following format:
TABLE NAME (KEY_ATTRIBUTE 1, ATTRIBUTE 2, ATTRIBUTE 3, … ATTRIBUTE K)
For example, a CAR entity may be represented by:
CAR (CAR_VIN, MOD_CODE, CAR_YEAR, CAR_COLOR)
Each car is identified by a unique vehicle identification number, or CAR_VIN.
Composite Identifiers Ideally, an entity identifier is composed of only a single attri- bute. For example, the table in Figure 4.2 uses a single-attribute primary key named
required attribute
In ER modeling, an attribute that must have a value. In other words, it cannot be left empty.
optional attribute
In ER modeling, an attribute that does not require a value; therefore, it can be left empty.
identifiers
One or more attributes that uniquely identify each entity instance.
relational schema
The organization of
a relational database as described by the database administrator.
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120 Part 2
Design Concepts
CLASS_CODE. However, it is possible to use a composite identifier, a primary key composed of more than one attribute. For instance, the Tiny College database admin- istrator may decide to identify each CLASS entity instance (occurrence) by using a composite primary key of CRS_CODE and CLASS_SECTION instead of using CLASS_ CODE. Either approach uniquely identifies each entity instance. Given the structure of the CLASS table shown in Figure 4.2, CLASS_CODE is the primary key, and the combi- nation of CRS_CODE and CLASS_SECTION is a proper candidate key. If the CLASS_ CODE attribute is deleted from the CLASS entity, the candidate key (CRS_CODE and CLASS_SECTION) becomes an acceptable composite primary key.
FIGURE 4.2 THE CLASS TABLE (ENTITY) COMPONENTS AND CONTENTS
Database name: Ch04_TinyCollege
Note
Remember that Chapter 3 made a commonly accepted distinction between COURSE and CLASS. A CLASS constitutes a specific time and place of a COURSE offering. A class is defined by the course description and its time and place, or section. Consider a profes- sor who teaches Database I, Section 2; Database I, Section 5; Database I, Section 8; and Spreadsheet II, Section 6. The professor teaches two courses (Database I and Spreadsheet II),
but four classes. Typically, the COURSE offerings are printed in a course catalog, while the CLASS offerings are printed in a class schedule for each term.
If the CLASS_CODE in Figure 4.2 is used as the primary key, the CLASS entity may be represented in shorthand form as follows:
CLASS (CLASS_CODE, CRS_CODE, CLASS_SECTION, CLASS_TIME, ROOM_CODE, PROF_NUM)
On the other hand, if CLASS_CODE is deleted, and the composite primary key is the combination of CRS_CODE and CLASS_SECTION, the CLASS entity may be repre- sented as follows:
CLASS (CRS_CODE, CLASS_SECTION, CLASS_TIME, ROOM_CODE, PROF_NUM)
Note that both key attributes are underlined in the entity notation.
Composite and Simple Attributes Attributes are classified as simple or composite. A composite attribute, not to be confused with a composite key, is an attribute that can
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composite identifier
In ER modeling, a key composed of more than one attribute.
composite attribute
An attribute that can be further subdivided to yield additional attributes. For example, a phone number such as 615-898- 2368 may be divided
into an area code (615), an exchange number (898), and a four-digit code (2368). Compare to simple attribute.
Copyright 2017 Cengage Lea Editorial review has deemed that

be further subdivided to yield additional attributes. For example, the attribute ADDRESS can be subdivided into street, city, state, and zip code. Similarly, the attribute PHONE_ NUMBER can be subdivided into area code and exchange number. A simple attribute is an attribute that cannot be subdivided. For example, age, sex, and marital status would be classified as simple attributes. To facilitate detailed queries, it is wise to change com- posite attributes into a series of simple attributes.
The database designer must always be on the lookout for composite attributes. It is common for business rules to use composite attributes to simplify policies, and users often describe entities in their environment using composite attributes. For example, a user at Tiny College might need to know a student’s name, address, and phone number. The designer must recognize that these are composite attributes and determine the cor- rect way to decompose the composite into simple attributes.
Single-Valued Attributes A single-valued attribute is an attribute that can have only a single value. For example, a person can have only one Social Security number, and a manufactured part can have only one serial number. Keep in mind that a single-valued attribute is not necessarily a simple attribute. For instance, a part’s serial number (such as SE-08-02-189935) is single-valued, but it is a composite attribute because it can be sub- divided into the region in which the part was produced (SE), the plant within that region (08), the shift within the plant (02), and the part number (189935).
Multivalued Attributes Multivalued attributes are attributes that can have many values. For instance, a person may have several college degrees, and a household may have several different phones, each with its own number. Similarly, a car’s color may be subdivided into many colors for the roof, body, and trim. In the Chen ERM, multivalued attributes are shown by a double line connecting the attribute to the entity. The Crow’s Foot notation does not identify multivalued attributes. The ERD in Figure 4.3 contains all of the components introduced thus far; note that CAR_VIN is the primary key, and CAR_COLOR is a multivalued attribute of the CAR entity.
Note
Chapter 4 Entity Relationship (ER) Modeling 121
simple attribute
An attribute that cannot be subdivided into meaningful components. Compare to composite attribute.
single-valued attribute
An attribute that can have only one value.
multivalued attribute
An attribute that can have many values for a single entity occurrence. For example, an EMP_ DEGREE attribute might store the string “BBA, MBA, PHD” to indicate three different degrees held.
FIGURE4.3 AMULTIVALUEDATTRIBUTEINANENTITY
Chen Model
Crow’s Foot Model
MOD_CODE CAR_VIN
CAR_YEAR CAR_COLOR
CAR
In the ERD models in Figure 4.3, the CAR entity’s foreign key (FK) has been typed as MOD_ CODE. This attribute was manually added to the entity. Actually, proper use of database modeling software will automatically produce the FK when the relationship is defined. In addition, the software will label the FK appropriately and write the FK’s implementa- tion details in a data dictionary. Therefore, when you use professional database modeling software, never type the FK attribute yourself; let the software handle that task when the relationship between the entities is defined. (You can see how this works in Appendix A, Designing Databases with Visio Professional: A Tutorial, at www.cengagebrain.com.)
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122 Part 2
Design Concepts
Implementing Multivalued Attributes Although the conceptual model can han- dle M:N relationships and multivalued attributes, you should not implement them in the RDBMS. Remember from Chapter 3 that in the relational table, each column and row intersection represents a single data value. So, if multivalued attributes exist, the designer must decide on one of two possible courses of action:
1. Within the original entity, create several new attributes, one for each component of the original multivalued attribute. For example, the CAR entity’s attribute CAR_ COLOR can be split to create the new attributes CAR_TOPCOLOR, CAR_BODY- COLOR, and CAR_TRIMCOLOR, which are then assigned to the CAR entity. (See Figure 4.4.)
Although this solution seems to work, its adoption can lead to major structural prob- lems in the table. It is only acceptable if every instance will have the same number of values for the multivalued attribute, and no instance will ever have more values. However, even in this case, it is a gamble that new changes in the environment will never create a situation where an instance would have more values than before. For example, if additional color components—such as a logo color—are added for some cars, the table structure must be modified to accommodate the new color section. In that case, cars that do not have such color sections generate nulls for the nonexistent components, or their color entries for those sections are entered as N/A to indicate “not applicable.” (The solution in Figure 4.4 is to split a multivalued attribute into new attributes, but imagine the problems this type of solution would cause if it were applied to an employee entity that contains employee degrees and certifications. If some employees have 10 degrees and certifications while most have fewer or none, the number of degree/certification attributes would be 10, and most of those attribute values would be null for most employees.) In short, although you have seen solution 1 applied, it is not always acceptable.
2. Create a new entity composed of the original multivalued attribute’s components. This new entity allows the designer to define color for different sections of the car. (See Table 4.1.) Then, this new CAR_COLOR entity is related to the original CAR entity in a 1:M relationship.
Using the approach illustrated in Table 4.1, you even get a fringe benefit: you can now
assign as many colors as necessary without having to change the table structure. The ERM shown in Figure 4.5 reflects the components listed in Table 4.1. This is the preferred way to deal with multivalued attributes. Creating a new entity in a 1:M relationship with the original entity yields several benefits: it is a more flexible, expandable solution, and it is compatible with the relational model!
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FIGURE4.4 SPLITTINGTHEMULTIVALUEDATTRIBUTEINTONEWATTRIBUTES
Chen Model
CAR_YEAR
Crow’s Foot Model
MOD_CODE CAR_VIN
CAR_TOPCOLOR
CAR_TRIMCOLOR CAR_BODYCOLOR
CAR

Chapter 4 Entity Relationship (ER) Modeling 123
TABLE 4.1
COMPONENTS OF THE MULTIVALUED ATTRIBUTE
SECTION
COLOR
Top
White
Body
Blue
Trim
Gold
Interior
Blue
FIGURE4.5 ANEWENTITYSETCOMPOSEDOFAMULTIVALUED ATTRIBUTE’S COMPONENTS
Note
If you are used to looking at relational diagrams such as the ones produced by Microsoft Access, you expect to see the relationship line in the relational diagram drawn from the PK to the FK. However, the relational diagram convention is not necessarily reflected in the ERD. In an ERD, the focus is on the entities and the relationships between them, rather than how those relationships are anchored graphically. In a complex ERD that includes both horizontally and vertically placed entities, the placement of the relationship lines is largely dictated by the designer’s decision to improve the readability of the design. (Remember that the ERD is used for communication between designers and end users.)
DerivedAttributes Finally,aderivedattributeisanattributewhosevalueiscalculated (derived) from other attributes. The derived attribute need not be physically stored within the database; instead, it can be derived by using an algorithm. For example, an employee’s age, EMP_AGE, may be found by computing the integer value of the difference between the current date and the EMP_DOB. If you use Microsoft Access, you would use the for- mula INT((DATE() – EMP_DOB)/365). In Microsoft SQL Server, you would use SELECT DATEDIFF(“YEAR”, EMP_DOB, GETDATE()), where DATEDIFF is a function that computes the difference between dates. The first parameter indicates the measurement (in this case, years). If you use Oracle, you would use SYSDATE instead of DATE(). (You are assuming, of course, that EMP_DOB was stored in the Julian date format.)
Similarly, the total cost of an order can be derived by multiplying the quantity ordered by the unit price. Or, the estimated average speed can be derived by dividing trip distance by the time spent en route. A derived attribute is indicated in the Chen notation by a dashed line that connects the attribute and the entity. (See Figure 4.6.) The Crow’s Foot notation does not have a method for distinguishing the derived attribute from other attributes.
Derived attributes are sometimes referred to as computed attributes. Computing a derived attribute can be as simple as adding two attribute values located on the same row, or it can be the result of aggregating the sum of values located on many table rows (from the same table or from a different table). The decision to store derived attributes in
derived attribute
An attribute that does not physically exist within the entity and is derived via an algorithm. For example, the Age attribute might be derived by subtracting the birth date from the current date.
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124 Part 2
Design Concepts
database tables depends on the processing requirements and the constraints placed on a particular application. The designer should be able to balance the design in accordance with such constraints. Table 4.2 shows the advantages and disadvantages of storing (or not storing) derived attributes in the database.
FIGURE4.6 DEPICTIONOFADERIVEDATTRIBUTE
Chen Model Crow’s Foot Model
EMP_FNAME EMP_LNAME
EMP_NUM
EMP_INITIAL EMP_DOB
EMP_AGE
EMPLOYEE
TABLE 4.2
ADVANTAGES AND DISADVANTAGES OF STORING DERIVED ATTRIBUTES
DERIVED ATTRIBUTE
STORED
NOT STORED
Advantage
Saves CPU processing cycles
Saves data access time
Data value is readily available
Can be used to keep track of historical data
Saves storage space
Computation always yields current value
Disadvantage
Requires constant maintenance to ensure derived value is current, especially if any values used in the calculation change
Uses CPU processing cycles Increases data access time
Adds coding complexity to queries
4-1c Relationships
Recall from Chapter 2 that a relationship is an association between entities. The entities that participate in a relationship are also known as participants, and each relationship is identified by a name that describes the relationship. The relationship name is an active or passive verb; for example, a STUDENT takes a CLASS, a PROFESSOR teaches a CLASS, a DEPARTMENT employs a PROFESSOR, a DIVISION is managed by an EMPLOYEE, and an AIRCRAFT is flown by a CREW.
Relationships between entities always operate in both directions. To define the relation- ship between the entities named CUSTOMER and INVOICE, you would specify that:
• A CUSTOMER may generate many INVOICEs.
• Each INVOICE is generated by one CUSTOMER.
Because you know both directions of the relationship between CUSTOMER and INVOICE, it is easy to see that this relationship can be classified as 1:M.
The relationship classification is difficult to establish if you know only one side of the relationship. For example, if you specify that:
A DIVISION is managed by one EMPLOYEE.
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participants
An ER term for entities that participate in
a relationship. For example, in the relationship “PROFESSOR teaches CLASS,” the teaches relationship
is based on the participants PROFESSOR and CLASS.
Copyright 2017 Cengage Lea Editorial review has deemed that

You don’t know if the relationship is 1:1 or 1:M. Therefore, you should ask the ques- tion “Can an employee manage more than one division?” If the answer is yes, the rela- tionship is 1:M, and the second part of the relationship is then written as:
An EMPLOYEE may manage many DIVISIONs.
If an employee cannot manage more than one division, the relationship is 1:1, and the second part of the relationship is then written as:
An EMPLOYEE may manage only one DIVISION.
4-1d Connectivity and Cardinality
You learned in Chapter 2 that entity relationships may be classified as one-to-one, one-to- many, or many-to-many. You also learned how such relationships were depicted in the Chen and Crow’s Foot notations. The term connectivity is used to describe the relationship classification.
Cardinality expresses the minimum and maximum number of entity occurrences associated with one occurrence of the related entity. In the ERD, cardinality is indicated by placing the appropriate numbers beside the entities, using the format (x,y). The first value represents the minimum number of associated entities, while the second value rep- resents the maximum number of associated entities. Many database designers who use Crow’s Foot modeling notation do not depict the specific cardinalities on the ER diagram itself because the specific limits described by the cardinalities cannot be implemented directly through the database design. Correspondingly, some Crow’s Foot ER modeling tools do not print the numeric cardinality range in the diagram; instead, you can add it as text if you want to have it shown. When the specific cardinalities are not included on the diagram in Crow’s Foot notation, cardinality is implied by the use of the symbols shown in Figure 4.7, which describe the connectivity and participation (discussed next). The numeric cardinality range has been added using the Microsoft Visio text drawing tool.
Knowing the minimum and maximum number of entity occurrences is very useful at the application software level. For example, Tiny College might want to ensure that a class is not taught unless it has at least 10 students enrolled. Similarly, if the classroom can hold only 30 students, the application software should use that cardinality to limit enrollment in the class. However, keep in mind that the DBMS cannot handle the implementation of the cardinalities at the table level—that capability is provided by the application software or by triggers. You will learn how to create and execute triggers in Chapter 8, Advanced SQL.
As you examine the Crow’s Foot diagram in Figure 4.7, keep in mind that the cardinalities represent the number of occurrences in the related entity. For example, the cardinality (1,4) next to the CLASS entity in the “PROFESSOR teaches CLASS” relationship indicates that each professor teaches up to four classes, which means that the PROFESSOR table’s primary key value occurs at least once and no more than four times as foreign key values in the CLASS table. If the cardinality had been written as (1,N), there would be no upper limit to the num- ber of classes a professor might teach. Similarly, the cardinality (1,1) next to the PROFESSOR entity indicates that each class is taught by one and only one professor. That is, each CLASS entity occurrence is associated with one and only one entity occurrence in PROFESSOR.
connectivity
The classification of the relationship between entities. Classifications include 1:1, 1:M, and M:N.
cardinality
A property that assigns a specific value to connectivity and expresses the range
of allowed entity occurrences associated with a single occurrence of the related entity.
Chapter 4 Entity Relationship (ER) Modeling 125
FIGURE4.7 CONNECTIVITYANDCARDINALITYINANERD
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126 Part 2
Design Concepts
Note
Online
Connectivities and cardinalities are established by concise statements known as busi- ness rules, which were introduced in Chapter 2. Such rules, derived from a precise and detailed description of an organization’s data environment, also establish the ERM’s enti- ties, attributes, relationships, connectivities, cardinalities, and constraints. Because busi- ness rules define the ERM’s components, making sure that all appropriate business rules are identified is an important part of a database designer’s job.
4-1e Existence Dependence
Content
The placement of the cardinalities in the ER diagram is a matter of convention. The Chen notation places the cardinalities on the side of the related entity. The Crow’s Foot and UML diagrams place the cardinalities next to the entity to which they apply.
Because the careful definition of complete and accurate business rules is crucial to good database design, their derivation is examined in detail in Appendix B, The University Lab: Conceptual Design. The modeling skills you are learning in this chapter are applied in the devel- opment of a real data- base design in Appendix B. The initial design shown in Appendix B is then modified in Appen- dix C, The University Lab: Conceptual Design Verification, Logical Design, and Implemen- tation. (Both appendixes are available at www. cengagebrain.com.)
An entity is said to be existence-dependent if it can exist in the database only when it is associated with another related entity occurrence. In implementation terms, an entity is existence-dependent if it has a mandatory foreign key—that is, a foreign key attribute that cannot be null. For example, if an employee wants to claim one or more dependents for tax-withholding purposes, the relationship “EMPLOYEE claims DEPENDENT” would be appropriate. In that case, the DEPENDENT entity is clearly existence-dependent on the EMPLOYEE entity because it is impossible for the dependent to exist apart from the EMPLOYEE in the database.
If an entity can exist apart from all of its related entities, then it is existence- independent, and it is referred to as a strong entity or regular entity. For example, sup- pose that the XYZ Corporation uses parts to produce its products. Furthermore, suppose that some of those parts are produced in-house and other parts are bought from vendors. In that scenario, it is quite possible for a PART to exist independently from a VENDOR in the relationship “PART is supplied by VENDOR” because at least some of the parts are not supplied by a vendor. Therefore, PART is existence-independent from VENDOR.
Note
4-1f Relationship Strength
The concept of relationship strength is based on how the primary key of a related entity is defined. To implement a relationship, the primary key of one entity (the parent entity, normally on the “one” side of the one-to-many relationship) appears as a foreign key in the related entity (the child entity, mostly the entity on the “many” side of the one- to-many relationship). Sometimes the foreign key also is a primary key component in the related entity. For example, in Figure 4.5, the CAR entity primary key (CAR_VIN) appears as both a primary key component and a foreign key in the CAR_COLOR entity. In this section, you will learn how various relationship strength decisions affect primary key arrangement in database design.
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The concept of relationship strength is not part of the original ERM. Instead, this con- cept applies directly to Crow’s Foot diagrams. Because Crow’s Foot diagrams are used extensively to design relational databases, it is important to understand relationship strength as it affects database implementation. The Chen ERD notation is oriented toward conceptual modeling and therefore does not distinguish between weak and strong relationships.
existence- dependent
A property of an entity whose existence depends on one or more other entities. In such
an environment, the existence-independent table must be created and loaded first because the existence-dependent key cannot reference a table that does not yet exist.
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Weak (Non-Identifying) Relationships A weak relationship, also known as a non-identifying relationship, exists if the primary key of the related entity does not contain a primary key component of the parent entity. By default, relationships are estab- lished by having the primary key of the parent entity appear as a foreign key (FK) on the related entity (also known as the child entity). For example, suppose the 1:M relationship between COURSE and CLASS is defined as:
COURSE (CRS_CODE, DEPT_CODE, CRS_DESCRIPTION, CRS_CREDIT) CLASS (CLASS_CODE, CRS_CODE, CLASS_SECTION, CLASS_TIME,
ROOM_CODE, PROF_NUM)
In this case, a weak relationship exists between COURSE and CLASS because CRS_CODE (the primary key of the parent entity) is only a foreign key in the CLASS entity. In this example, the CLASS primary key did not inherit a primary key component from the COURSE entity.
Figure 4.8 shows how the Crow’s Foot notation depicts a weak relationship by placing a dashed relationship line between the entities. The tables shown below the ERD illus- trate how such a relationship is implemented.
Chapter 4 Entity Relationship (ER) Modeling 127
FIGURE4.8 AWEAK(NON-IDENTIFYING)RELATIONSHIPBETWEEN COURSE AND CLASS
Table name: COURSE Database name: Ch04_TinyCollege
Table name: CLASS
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existence- independent
A property of an entity that can exist apart from one or more related entities. Such a table must be created first when referencing an existence- dependent table.
strong entity
An entity that is existence-independent, that is, it can exist apart from all of its related entities. Also called a regular entity.
regular entity
See strong entity.
weak (non- identifying) relationship
A relationship in which the primary key of the related entity does
not contain a primary key component of the parent entity.

128 Part 2
Design Concepts
FIGURE4.9 ASTRONG(IDENTIFYING)RELATIONSHIPBETWEEN COURSE AND CLASS
Table name: COURSE Database name: Ch04_TinyCollege_Alt
Table name: CLASS
strong (identifying) relationship
A relationship that occurs when two entities are existence- dependent; from
a database design perspective, this relationship exists whenever the primary key of the related entity contains the primary key of the parent entity.
Strong (Identifying) Relationships A strong (identifying) relationship exists when the primary key of the related entity contains a primary key component of the parent entity. For example, suppose the 1:M relationship between COURSE and CLASS is defined as:
COURSE (CRS_CODE, DEPT_CODE, CRS_DESCRIPTION, CRS_CREDIT)
CLASS (CRS_CODE, CLASS_SECTION, CLASS_TIME, ROOM_CODE, PROF_NUM)
In this case, the CLASS entity primary key is composed of CRS_CODE and CLASS_SEC- TION. Therefore, a strong relationship exists between COURSE and CLASS because CRS_ CODE (the primary key of the parent entity) is a primary key component in the CLASS entity. In other words, the CLASS primary key did inherit a primary key component from the COURSE entity. (Note that the CRS_CODE in CLASS is also the FK to the COURSE entity.)
The Crow’s Foot notation depicts the strong (identifying) relationship with a solid line between the entities, as shown in Figure 4.9.
As you examine Figure 4.9, you might wonder what the O symbol next to the CLASS entity signifies. You will discover the meaning of this cardinality in Section 4-1h, Rela- tionship Participation.
In summary, whether the relationship between COURSE and CLASS is strong or weak depends on how the CLASS entity’s primary key is defined. Remember that the nature of the relationship is often determined by the database designer, who must use professional
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Note
Chapter 4 Entity Relationship (ER) Modeling 129
Keep in mind that the order in which the tables are created and loaded is very important. For example, in the “COURSE generates CLASS” relationship, the COURSE table must be created before the CLASS table. After all, it would not be acceptable to have the CLASS table’s foreign key refer to a COURSE table that did not yet exist. In fact, you must load the data of the “1” side first in a 1:M relationship to avoid the possibility of referential integrity errors, regardless of whether the relationships are weak or strong.
judgment to determine which relationship type and strength best suit the database trans- action, efficiency, and information requirements. That point will be emphasized in detail!
4-1g Weak Entities
In contrast to the strong or regular entity mentioned in Section 4-1f, a weak entity is
one that meets two conditions:
1. The entity is existence-dependent; it cannot exist without the entity with which it has a relationship.
2. The entity has a primary key that is partially or totally derived from the parent entity in the relationship.
For example, a company insurance policy insures an employee and any dependents. For the purpose of describing an insurance policy, an EMPLOYEE might or might not have a DEPENDENT, but the DEPENDENT must be associated with an EMPLOYEE. Moreover, the DEPENDENT cannot exist without the EMPLOYEE; that is, a person cannot get insurance coverage as a dependent unless the person is a dependent of an employee. DEPENDENT is the weak entity in the relationship “EMPLOYEE has DEPENDENT.” This relationship is shown in Figure 4.10.
FIGURE4.10 AWEAKENTITYINANERD
Chen Model
1M has
EMPLOYEE
(0,N)
EMP_LNAME EMP_FNAME EMP_INITIAL EMP_DOB EMP_HIREDATE
(1,1)
EMP_NUM
EMP_NUM
DEP_NUM
DEP_FNAME DEP_DOB
DEPENDENT
Crow’s Foot Model
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weak entity
An entity that displays existence dependence and inherits the primary key of its parent
entity. For example, a DEPENDENT requires the existence of an EMPLOYEE.

130 Part 2
Design Concepts
Note that the Chen notation in Figure 4.10 identifies the weak entity by using a dou- ble-walled entity rectangle. The Crow’s Foot notation generated by Visio Professional uses the relationship line and the PK/FK designation to indicate whether the related entity is weak. A strong (identifying) relationship indicates that the related entity is weak. Such a relationship means that both conditions have been met for the weak entity definition—the related entity is existence-dependent, and the PK of the related entity contains a PK component of the parent entity.
Remember that the weak entity inherits part of its primary key from its strong coun- terpart. For example, at least part of the DEPENDENT entity’s key shown in Figure 4.10 was inherited from the EMPLOYEE entity:
EMPLOYEE (EMP_NUM, EMP_LNAME, EMP_FNAME, EMP_INITIAL, EMP_DOB, EMP_HIREDATE)
DEPENDENT (EMP_NUM, DEP_NUM, DEP_FNAME, DEP_DOB)
Figure 4.11 illustrates the implementation of the relationship between the weak entity (DEPENDENT) and its parent or strong counterpart (EMPLOYEE). Note that DEPEN- DENT’s primary key is composed of two attributes, EMP_NUM and DEP_NUM, and that EMP_NUM was inherited from EMPLOYEE.
Given this scenario, and with the help of this relationship, you can determine that:
Jeanine J. Callifante claims two dependents, Annelise and Jorge.
Keep in mind that the database designer usually determines whether an entity can be described as weak based on the business rules. An examination of Figure 4.8 might cause you to conclude that CLASS is a weak entity to COURSE. After all, it seems clear that a CLASS cannot exist without a COURSE, so there is existence dependence. For example, a student cannot enroll in the Accounting I class ACCT-211, Section 3 (CLASS_CODE 10014), unless there is an ACCT-211 course. However, note that the CLASS table’s primary key is CLASS_CODE, which is not derived from the COURSE parent entity. That is, CLASS may be represented by:
CLASS (CLASS_CODE, CRS_CODE, CLASS_SECTION, CLASS_TIME, ROOM_CODE, PROF_NUM)
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FIGURE4.11 AWEAKENTITYINASTRONGRELATIONSHIP
Table name: EMPLOYEE Database name: Ch04_ShortCo
Table name: DEPENDENT

The second weak entity requirement has not been met; therefore, by definition, the CLASS entity in Figure 4.8 may not be classified as weak. On the other hand, if the CLASS entity’s primary key had been defined as a composite key composed of the com- bination CRS_CODE and CLASS_SECTION, CLASS could be represented by:
CLASS (CRS_CODE, CLASS_SECTION, CLASS_TIME, ROOM_CODE, PROF_NUM)
In that case, as illustrated in Figure 4.9, the CLASS primary key is partially derived from COURSE because CRS_CODE is the COURSE table’s primary key. Given this decision, CLASS is a weak entity by definition. (In Visio Professional Crow’s Foot terms, the relationship between COURSE and CLASS is classified as strong, or identifying.) In any case, CLASS is always existence-dependent on COURSE, whether or not it is defined as weak.
4-1h Relationship Participation
Participation in an entity relationship is either optional or mandatory. Recall that relationships are bidirectional; that is, they operate in both directions. If COURSE is related to CLASS, then by definition, CLASS is related to COURSE. Because of the bidirectional nature of relationships, it is necessary to determine the con- nectivity of the relationship from COURSE to CLASS and the connectivity of the relationship from CLASS to COURSE. Similarly, the specific maximum and mini- mum cardinalities must be determined in each direction for the relationship. Once again, you must consider the bidirectional nature of the relationship when deter- mining participation.
Optional participation means that one entity occurrence does not require a corresponding entity occurrence in a particular relationship. For example, in the “COURSE generates CLASS” relationship, you noted that at least some courses do not generate a class. In other words, an entity occurrence (row) in the COURSE table does not necessarily require the existence of a corresponding entity occur- rence in the CLASS table. (Remember that each entity is implemented as a table.) Therefore, the CLASS entity is considered to be optional to the COURSE entity. In Crow’s Foot notation, an optional relationship between entities is shown by drawing a small circle (O) on the side of the optional entity, as illustrated in Figure 4.9. The existence of an optional entity indicates that its minimum cardinality is 0. (The term optionality is used to label any condition in which one or more optional relationships exist.)
Note
Mandatory participation means that one entity occurrence requires a corresponding entity occurrence in a particular relationship. If no optionality symbol is depicted with the entity, the entity is assumed to exist in a mandatory relationship with the related entity. If the mandatory participation is depicted graphically, it is typically shown as a small hash mark across the relationship line, similar to the Crow’s Foot depiction of a connectivity of 1. The existence of a mandatory relationship indicates that the minimum cardinality is at least 1 for the mandatory entity.
optional participation
In ER modeling, a condition in which one entity occurrence does not require a corresponding entity occurrence in a particular relationship.
mandatory participation
A relationship in
which one entity occurrence must
have a corresponding occurrence in another entity. For example, an EMPLOYEE works in a DIVISION. (A person cannot be an employee without being assigned to a company’s division.)
Chapter 4 Entity Relationship (ER) Modeling 131
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Remember that the burden of establishing the relationship is always placed on the entity that contains the foreign key. In most cases, that entity is on the “many” side of the relationship.

132 Part 2 Note
Design Concepts
You might be tempted to conclude that relationships are weak when they occur between entities in an optional relationship and that relationships are strong when they occur between entities in a mandatory relationship. However, this conclusion is not warranted. Keep in mind that relationship participation and relationship strength do not describe the same thing. You are likely to encounter a strong relationship when one entity is optional to another. For example, the relationship between EMPLOYEE and DEPENDENT is clearly a strong one, but DEPENDENT is clearly optional to EMPLOYEE. After all, you cannot require employees to have dependents. Also, it is just as possible for a weak relationship to be estab- lished when one entity is mandatory to another. The relationship strength depends on how the PK of the related entity is formulated, while the relationship participation depends on how the business rule is written. For example, the business rules “Each part must be supplied by a vendor” and “A part may or may not be supplied by a vendor” create different option- alities for the same entities! Failure to understand this distinction may lead to poor design decisions that cause major problems when table rows are inserted or deleted.
When you create a relationship in Microsoft Visio, the default relationship will be mandatory on the “1” side and optional on the “many” side. Table 4.3 shows the various connectivity and participation combinations that are supported by the Crow’s Foot nota- tion. Recall that these combinations are often referred to as cardinality in Crow’s Foot notation when specific cardinalities are not used.
TABLE 4.3
CROW’S FOOT SYMBOLS
SYMBOL
CARDINALITY
COMMENT
(0,N)
Zero or many; the “many” side is optional.
(1,N)
One or many; the “many” side is mandatory.
(1,1)
One and only one; the “1” side is mandatory.
(0,1)
Zero or one; the “1” side is optional.
Because relationship participation is an important component of database design, you should examine a few more scenarios. Suppose that Tiny College employs some professors who conduct research without teaching classes. If you examine the “PROFESSOR teaches CLASS” relationship, it is quite possible for a PROFESSOR not to teach a CLASS. There- fore, CLASS is optional to PROFESSOR. On the other hand, a CLASS must be taught by a PROFESSOR. Therefore, PROFESSOR is mandatory to CLASS. Note that the ERD model in Figure 4.12 shows the cardinality next to CLASS to be (0,3), indicating that a professor may teach no classes or as many as three classes. Also, each CLASS table row references one and only one PROFESSOR row—assuming each class is taught by one and only one professor—represented by the (1,1) cardinality next to the PROFESSOR table.
FIGURE4.12 ANOPTIONALCLASSENTITYINTHERELATIONSHIP “PROFESSOR TEACHES CLASS”
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It is important that you clearly understand the distinction between mandatory and optional participation in relationships. Otherwise, you might develop designs in which awkward and unnecessary temporary rows (entity instances) must be created just to accommodate the creation of required entities.
It is also important to understand that the semantics of a problem might determine the type of participation in a relationship. For example, suppose that Tiny College offers several courses; each course has several classes. Note again the distinction between class and course in this discussion: a CLASS constitutes a specific offering (or section) of a COURSE. Typically, courses are listed in the university’s course catalog, while classes are listed in the class schedules that students use to register for their classes.
By analyzing the CLASS entity’s contribution to the “COURSE generates CLASS” relationship, it is easy to see that a CLASS cannot exist without a COURSE. Therefore, you can conclude that the COURSE entity is mandatory in the relationship. However, two scenarios for the CLASS entity may be written, as shown in Figures 4.13 and 4.14.
The different scenarios are a function of the problem’s semantics; that is, they depend on how the relationship is defined.
1. CLASS is optional. It is possible for the department to create the COURSE entity first and then create the CLASS entity after making the teaching assignments. In the real world, such a scenario is very likely; there may be courses for which sections (classes) have not yet been defined. In fact, some courses are taught only once a year and do not generate classes each semester.
2. CLASS is mandatory. This condition is created by the constraint imposed by the semantics of the statement “Each COURSE generates one or more CLASSes.” In ER terms, each COURSE in the “generates” relationship must have at least one CLASS. Therefore, a CLASS must be created as the COURSE is created to comply with the semantics of the problem.
Keep in mind the practical aspects of the scenario presented in Figure 4.14. Given the semantics of this relationship, the system should not accept a course that is not associated with at least one class section. Is such a rigid environment desirable from an operational point of view? For example, when a new COURSE is created, the database first updates the COURSE table, thereby inserting a COURSE entity that does not yet have a CLASS associated with it.
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Chapter 4 Entity Relationship (ER) Modeling 133
FIGURE4.13 CLASSISOPTIONALTOCOURSE
FIGURE4.14 COURSEANDCLASSINAMANDATORYRELATIONSHIP

134 Part 2
Design Concepts
Naturally, the apparent problem seems to be solved when CLASS entities are inserted into the corresponding CLASS table. However, because of the mandatory relationship, the system will temporarily violate the business rule constraint. For practical purposes, it would be desirable to classify the CLASS as optional to produce a more flexible design.
Finally, as you examine the scenarios in Figures 4.13 and 4.14, keep in mind the role of the DBMS. To maintain data integrity, the DBMS must ensure that the “many” side (CLASS) is associated with a COURSE through the foreign key rules.
4-1i Relationship Degree
A relationship degree indicates the number of entities or participants associated with a relationship. A unary relationship exists when an association is maintained within a single entity. A binary relationship exists when two entities are associated. A ternary relationship exists when three entities are associated. Although higher degrees exist, they are rare and are not specifically named. (For example, an association of four entities is described simply as a four-degree relationship.) Figure 4.15 shows these types of relationship degrees.
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FIGURE 4.15 THREE TYPES OF RELATIONSHIP DEGREE
relationship degree
The number of
entities or participants associated with
a relationship. A relationship degree can be unary, binary, ternary, or higher.
unary relationship
An ER term used to describe an association within an entity. For example, an EMPLOYEE might manage another EMPLOYEE.
binary relationship
An ER term for an association (relationship) between two entities. For example, PROFESSOR teaches CLASS.
ternary relationship
An ER term used to describe an association (relationship) between three entities. For example, a DOCTOR prescribes a DRUG for a PATIENT.
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Unary Relationships In the case of the unary relationship shown in Figure 4.15, an employee within the EMPLOYEE entity is the manager for one or more employees within that entity. In this case, the existence of the “manages” relationship means that EMPLOYEE requires another EMPLOYEE to be the manager—that is, EMPLOYEE has a relationship with itself. Such a relationship is known as a recursive relationship. The various cases of recursive relationships are explained in Section 4-1j.
Binary Relationships A binary relationship exists when two entities are associated in a relationship. Binary relationships are the most common type of relationship. In fact, to simplify the conceptual design, most higher-order (ternary and higher) rela- tionships are decomposed into appropriate equivalent binary relationships whenever possible. In Figure 4.15, “a PROFESSOR teaches one or more CLASSes” represents a binary relationship.
Ternary and Higher-Order Relationships Although most relationships are binary, the use of ternary and higher-order relationships does allow the designer some latitude regarding the semantics of a problem. A ternary relationship implies an association among three different entities. For example, in Figure 4.16, note the relationships and their consequences, which are represented by the following business rules:
• A DOCTOR writes one or more PRESCRIPTIONs.
• A PATIENT may receive one or more PRESCRIPTIONs.
• A DRUG may appear in one or more PRESCRIPTIONs. (To simplify this example, assume that the business rule states that each prescription contains only one drug. In short, if a doctor prescribes more than one drug, a separate prescription must be written for each drug.)
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Chapter 4 Entity Relationship (ER) Modeling 135
recursive relationship
A relationship found within a single entity type. For example, an EMPLOYEE is married to an EMPLOYEE or a PART is a component of another PART.
FIGURE 4.16 THE IMPLEMENTATION OF A TERNARY RELATIONSHIP
Table name: DRUG Table name: PATIENT
Table name: DOCTOR Table name: PRESCRIPTION
Database name: Ch04_Clinic

136 Part 2
Design Concepts
As you examine the table contents in Figure 4.16, note that it is possible to track all transactions. For instance, you can tell that the first prescription was written by doctor 32445 for patient 102, using the drug DRZ.
4-1j Recursive Relationships
As you just learned, a recursive relationship is one in which a relationship can exist between occurrences of the same entity set. (Naturally, such a condition is found within a unary relationship.) For example, a 1:M unary relationship can be expressed by “an EMPLOYEE may manage many EMPLOYEEs, and each EMPLOYEE is managed by one EMPLOYEE.” Also, as long as polygamy is not legal, a 1:1 unary relationship may be expressed by “an EMPLOYEE may be married to one and only one other EMPLOYEE.” Finally, the M:N unary relationship may be expressed by “a COURSE may be a prereq- uisite to many other COURSEs, and each COURSE may have many other COURSEs as prerequisites.” Those relationships are shown in Figure 4.17.
FIGURE4.17 ANERREPRESENTATIONOFRECURSIVERELATIONSHIPS
The 1:1 relationship shown in Figure 4.17 can be implemented in the single table shown in Figure 4.18. Note that you can determine that James Ramirez is married to Louise Ramirez, who is married to James Ramirez. Also, Anne Jones is married to Anton Shapiro, who is married to Anne Jones.
FIGURE4.18 THE1:1RECURSIVERELATIONSHIP“EMPLOYEEIS MARRIED TO EMPLOYEE”
Database name: Ch04_PartCo Table name: EMPLOYEE_V1
Unary relationships are common in manufacturing industries. For example, Figure 4.19 illustrates that a rotor assembly (C-130) is composed of many parts, but each part is used to create only one rotor assembly. Figure 4.19 indicates that a rotor assembly is composed of four 2.5-cm washers, two cotter pins, one 2.5-cm steel shank, four 10.25-cm rotor blades, and two 2.5-cm hex nuts. The relationship implemented in Figure 4.19 thus enables you to track each part within each rotor assembly.
If a part can be used to assemble several different kinds of other parts and is itself composed of many parts, two tables are required to implement the “PART contains PART” relationship. Figure 4.20 illustrates such an environment. Parts tracking is increasingly important as manag- ers become more aware of the legal ramifications of producing more complex output. In many industries, especially those involving aviation, full parts tracking is required by law.
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Chapter 4 Entity Relationship (ER) Modeling 137
FIGURE4.19 ANOTHERUNARYRELATIONSHIP:“PARTCONTAINSPART”
Table name: PART_V1 Database name: Ch04_PartCo
FIGURE 4.20 THE IMPLEMENTATION OF THE M:N RECURSIVE RELATIONSHIP “PART CONTAINS PART”
Table name: COMPONENT Database name: Ch04_PartCo
Table name: PART
The M:N recursive relationship might be more familiar in a school environment. For instance, note how the M:N “COURSE requires COURSE” relationship illustrated in Fig- ure 4.17 is implemented in Figure 4.21. In this example, MATH-243 is a prerequisite to QM-261 and QM-362, while both MATH-243 and QM-261 are prerequisites to QM-362.
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FIGURE4.21 IMPLEMENTATIONOFTHEM:NRECURSIVERELATIONSHIP “COURSE REQUIRES COURSE”
Table name: COURSE Database name: Ch04_TinyCollege
Table name: PREREQ

138 Part 2
Design Concepts
Finally, the 1:M recursive relationship “EMPLOYEE manages EMPLOYEE,” shown in Figure 4.17, is implemented in Figure 4.22.
One common pitfall when working with unary relationships is to confuse participa- tion with referential integrity. In theory, participation and referential integrity are very different concepts and are normally easy to distinguish in binary relationships. In practi- cal terms, conversely, participation and referential integrity are very similar because they are both implemented through constraints on the same set of attributes. This similarity often leads to confusion when the concepts are applied within the limited structure of a unary relationship. Consider the unary 1:1 spousal relationship between employees, which is described in Figure 4.18. Participation, as described previously, is bidirectional, meaning that it must be addressed in both directions along the relationship. Participa- tion in Figure 4.18 addresses the following questions:
• Must every employee have a spouse who is an employee?
• Must every employee be a spouse to another employee?
For the data shown in Figure 4.18, the correct answer to both questions is “No.” It is possible to be an employee and not have another employee as a spouse. Also, it is possible to be an employee and not be the spouse of another employee.
Referential integrity deals with the correspondence of values in the foreign key with values in the related primary key. Referential integrity is not bidirectional, and therefore answers only one question:
• Must every employee spouse be a valid employee?
For the data shown in Figure 4.18, the correct answer is “Yes.” Another way to frame this question is to consider whether every value provided for the EMP_SPOUSE attri- bute must match some value in the EMP_NUM attribute.
In practical terms, both participation and referential integrity involve the values used as primary keys and foreign keys to implement the relationship. Referential integrity requires that the values in the foreign key correspond to values in the primary key. In one direction, participation considers whether the foreign key can contain a null. In Figure 4.18, for example, employee Robert Delaney is not required to have a value in EMP_SPOUSE. In the other direc- tion, participation considers whether every value in the primary key must appear as a value in the foreign key. In Figure 4.18, for example, employee Robert Delaney’s value for EMP_NUM (348) is not required to appear as a value in EMP_SPOUSE for any other employee.
4-1k Associative (Composite) Entities
M:N relationships are a valid construct at the conceptual level, and therefore are found fre-
quently during the ER modeling process. However, implementing the M:N relationship,
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FIGURE4.22 IMPLEMENTATIONOFTHE1:MRECURSIVERELATIONSHIP “EMPLOYEE MANAGES EMPLOYEE”
Table name: EMPLOYEE_V2
Database name: Ch04_PartCo

particularly in the relational model, requires the use of an additional entity, as you learned in Chapter 3. The ER model uses the associative entity to represent an M:N relationship between two or more entities. This associative entity, also called a composite or bridge entity, is in a 1:M relationship with the parent entities and is composed of the primary key attributes of each parent entity. Furthermore, the associative entity can have additional attributes of its own, as shown by the ENROLL associative entity in Figure 4.23. When using the Crow’s Foot notation, the associative entity is identified as a strong (identifying) relationship, as indicated by the solid relationship lines between the parents and the associative entity.
Note that the composite ENROLL entity in Figure 4.23 is existence-dependent on the other two entities; the composition of the ENROLL entity is based on the primary keys of the entities that are connected by the composite entity. The composite entity may also contain additional attributes that play no role in the connective process. For example, although the entity must be composed of at least the STUDENT and CLASS primary keys, it may also include such additional attributes as grades, absences, and other data uniquely identified by the student’s performance in a specific class.
Finally, keep in mind that the ENROLL table’s key (CLASS_CODE and STU_NUM) is composed entirely of the primary keys of the CLASS and STUDENT tables. Therefore, no null entries are possible in the ENROLL table’s key attributes.
Implementing the small database shown in Figure 4.23 requires that you define the relationships clearly. Specifically, you must know the “1” and the “M” sides of each rela- tionship, and you must know whether the relationships are mandatory or optional. For example, note the following point:
• A class may exist (at least at the start of registration) even though it contains no stu- dents. Therefore, in Figure 4.24, an optional symbol should appear on the STUDENT side of the M:N relationship between STUDENT and CLASS.
You might argue that to be classified as a STUDENT, a person must be enrolled in at least one CLASS. Therefore, CLASS is mandatory to STUDENT from a purely conceptual point of view. However, when a student is admitted to college, that student has not yet signed up for any classes. Therefore, at least initially, CLASS is optional to STUDENT. Note that the practical considerations in the data environment help dictate the use of optionalities.
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Chapter 4 Entity Relationship (ER) Modeling 139
FIGURE4.23 CONVERTINGTHEM:NRELATIONSHIPINTOTWO1:M RELATIONSHIPS
Table name: STUDENT Database name: Ch04_CollegeTry
Table name: ENROLL
Table name: CLASS

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FIGURE 4.24 THE M:N RELATIONSHIP BETWEEN STUDENT AND CLASS
If CLASS is not optional to STUDENT from a database point of view, a class assignment must be made when the student is admitted. However, that’s not how the process actually works, and the database design must reflect this. In short, the optionality reflects practice.
Because the M:N relationship between STUDENT and CLASS is decomposed into two 1:M relationships through ENROLL, the optionalities must be transferred to ENROLL. (See Figure 4.25.) In other words, it now becomes possible for a class not to occur in ENROLL if no student has signed up for that class. Because a class need not occur in ENROLL, the ENROLL entity becomes optional to CLASS. Also, because the ENROLL entity is created before any students have signed up for a class, the ENROLL entity is also optional to STUDENT, at least initially.
FIGURE4.25 ACOMPOSITEENTITYINANERD
iterative process
A process based on repetition of steps and procedures.
• As students begin to sign up for their classes, they will be entered into the ENROLL entity. Naturally, if a student takes more than one class, that student will occur more than once in ENROLL. For example, note that in the ENROLL table in Figure 4.23, STU_NUM = 321452 occurs three times. On the other hand, each student occurs only once in the STUDENT entity. (Note that the STUDENT table in Figure 4.23 has only one STU_NUM = 321452 entry.) Therefore, in Figure 4.25, the relationship between STUDENT and ENROLL is shown to be 1:M, with the “M” on the ENROLL side.
• As you can see in Figure 4.23, a class can occur more than once in the ENROLL table. For example, CLASS_CODE = 10014 occurs twice. However, CLASS_CODE = 10014 occurs only once in the CLASS table to reflect that the relationship between CLASS and ENROLL is 1:M. Note that in Figure 4.25, the “M” is located on the ENROLL side, while the “1” is located on the CLASS side.
4-2 Developing an ER Diagram
The process of database design is iterative rather than a linear or sequential process. The verb iterate means “to do again or repeatedly.” Thus, an iterative process is based on repetition of processes and procedures. Building an ERD usually involves the following activities:
• Create a detailed narrative of the organization’s description of operations. • Identify the business rules based on the description of operations.
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• Identify the main entities and relationships from the business rules.
• Develop the initial ERD.
• Identify the attributes and primary keys that adequately describe the entities.
• Revise and review the ERD.
During the review process, additional objects, attributes, and relationships proba- bly will be uncovered. Therefore, the basic ERM will be modified to incorporate the newly discovered ER components. Subsequently, another round of reviews might yield additional components or clarification of the existing diagram. The process is repeated until the end users and designers agree that the ERD is a fair representation of the organization’s activities and functions.
During the design process, the database designer does not depend simply on inter- views to help define entities, attributes, and relationships. A surprising amount of infor- mation can be gathered by examining the business forms and reports that an organization uses in its daily operations.
To illustrate the use of the iterative process that ultimately yields a workable ERD, start with an initial interview with the Tiny College administrators. The interview pro- cess yields the following business rules:
1. Tiny College (TC) is divided into several schools: business, arts and sciences, educa- tion, and applied sciences. Each school is administered by a dean who is a professor. Each professor can be the dean of only one school, and a professor is not required to be the dean of any school. Therefore, a 1:1 relationship exists between PROFESSOR and SCHOOL. Note that the cardinality can be expressed by writing (1,1) next to the entity PROFESSOR and (0,1) next to the entity SCHOOL.
2. Eachschoolcomprisesseveraldepartments.Forexample,theschoolofbusinesshasan accounting department, a management/marketing department, an economics/finance department, and a computer information systems department. Note again the cardinal- ity rules: The smallest number of departments operated by a school is one, and the larg- est number of departments is indeterminate (N). On the other hand, each department belongs to only a single school; thus, the cardinality is expressed by (1,1). That is, the minimum number of schools to which a department belongs is one, as is the maximum number. Figure 4.26 illustrates these first two business rules.
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Chapter 4 Entity Relationship (ER) Modeling 141
FIGURE 4.26 THE FIRST TINY COLLEGE ERD SEGMENT

142 Part 2 Note
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It is again appropriate to evaluate the reason for maintaining the 1:1 relationship between PROFESSOR and SCHOOL in the “PROFESSOR is dean of SCHOOL” relationship. It is worth repeating that the existence of 1:1 relationships often indicates a misidentification of attri- butes as entities. In this case, the 1:1 relationship could easily be eliminated by storing the dean’s attributes in the SCHOOL entity. This solution would also make it easier to answer the queries “Who is the dean?” and “What are the dean’s credentials?” The downside of this solution is that it requires the duplication of data that is already stored in the PROFESSOR table, thus setting the stage for anomalies. However, because each school is run by a single dean, the problem of data duplication is rather minor. The selection of one approach over another often depends on information requirements, transaction speed, and the database designer’s professional judgment. In short, do not use 1:1 relationships lightly, and make sure that each 1:1 relationship within the database design is defensible.
3. Eachdepartmentmayoffercourses.Forexample,themanagement/marketingdepart- ment offers courses such as Introduction to Management, Principles of Marketing, and Production Management. The ERD segment for this condition is shown in Fig- ure 4.27. Note that this relationship is based on the way Tiny College operates. For example, if Tiny College had some departments that were classified as “research only,” they would not offer courses; therefore, the COURSE entity would be optional to the DEPARTMENT entity.
4. The relationship between COURSE and CLASS was illustrated in Figure 4.9. Nev- ertheless, it is worth repeating that a CLASS is a section of a COURSE. That is, a department may offer several sections (classes) of the same database course. Each of those classes is taught by a professor at a given time in a given place. In short, a 1:M relationship exists between COURSE and CLASS. Additionally, each class is offered during a given semester. SEMESTER defines the year and the term that the class will be offered. Note that this is different from the date when the student actually enrolls in a class. For example, students are able to enroll in summer and fall term classes near the end of the spring term. It is possible that the Tiny Col- lege calendar is set with semester beginning and ending dates prior to the creation of the semester class schedule so CLASS is optional to SEMESTER. This design will also help for reporting purposes, for example, you could answer questions such as: what classes were offered X semester? Or, what classes did student Y take on semester X? Because a course may exist in Tiny College’s course catalog even when it is not offered as a class in a given semester, CLASS is optional to COURSE. Therefore, the relationships between SEMESTER, COURSE, and CLASS look like Figure 4.28.
FIGURE 4.27 THE SECOND TINY COLLEGE ERD SEGMENT
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5. Each department should have one or more professors assigned to it. One and only one of those professors chairs the department, and no professor is required to accept the chair position. Therefore, DEPARTMENT is optional to PROFESSOR in the “chairs” relationship. Those relationships are summarized in the ER segment shown in Figure 4.29.
6. Each professor may teach up to four classes; each class is a section of a course. A pro- fessor may also be on a research contract and teach no classes at all. The ERD segment in Figure 4.30 depicts those conditions.
7. A student may enroll in several classes but take each class only once during any given enrollment period. For example, during the current enrollment period, a student may decide to take five classes—Statistics, Accounting, English, Data- base, and History—but that student would not be enrolled in the same Statistics
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Chapter 4 Entity Relationship (ER) Modeling 143
FIGURE 4.28 THE THIRD TINY COLLEGE ERD SEGMENT
FIGURE 4.29 THE FOURTH TINY COLLEGE ERD SEGMENT

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FIGURE 4.30 THE FIFTH TINY COLLEGE ERD SEGMENT
class five times during the enrollment period! Each student may enroll in up to six classes, and each class may have up to 35 students, thus creating an M:N rela- tionship between STUDENT and CLASS. Because a CLASS can initially exist at the start of the enrollment period even though no students have enrolled in it, STUDENT is optional to CLASS in the M:N relationship. This M:N relationship must be divided into two 1:M relationships through the use of the ENROLL entity, shown in the ERD segment in Figure 4.31. However, note that the optional sym- bol is shown next to ENROLL. If a class exists but has no students enrolled in it, that class does not occur in the ENROLL table. Note also that the ENROLL entity is weak: it is existence-dependent, and its (composite) PK is composed of the PKs of the STUDENT and CLASS entities. You can add the cardinalities (0,6) and (0,35) next to the ENROLL entity to reflect the business rule constraints, as shown in Figure 4.31. (Visio Professional does not automatically generate such cardinalities, but you can use a text box to accomplish that task.)
8. Each department has several (or many) students whose major is offered by that department. However, each student has only a single major and is therefore asso- ciated with a single department. (See Figure 4.32.) However, in the Tiny College environment, it is possible—at least for a while—for a student not to declare a major field of study. Such a student would not be associated with a department; therefore, DEPARTMENT is optional to STUDENT. It is worth repeating that the relation- ships between entities and the entities themselves reflect the organization’s operat- ing environment. That is, the business rules define the ERD components.
9. Each student has an advisor in his or her department; each advisor counsels several students. An advisor is also a professor, but not all professors advise students. There- fore, STUDENT is optional to PROFESSOR in the “PROFESSOR advises STUDENT” relationship. (See Figure 4.33.)
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FIGURE 4.31 THE SIXTH TINY COLLEGE ERD SEGMENT

Chapter 4 Entity Relationship (ER) Modeling 145
FIGURE 4.32 THE SEVENTH TINY COLLEGE ERD SEGMENT
FIGURE 4.33 THE EIGHTH TINY COLLEGE ERD SEGMENT
10. As you can see in Figure 4.34, the CLASS entity contains a ROOM_CODE attribute. Given the naming conventions, it is clear that ROOM_CODE is an FK to another entity. Clearly, because a class is taught in a room, it is reasonable to assume that the ROOM_CODE in CLASS is the FK to an entity named ROOM. In turn, each room is located in a building. So, the last Tiny College ERD is created by observing that a BUILDING can contain many ROOMs, but each ROOM is found in a single BUILDING. In this ERD segment, it is clear that some buildings do not contain (class) rooms. For example, a storage building might not contain any named rooms at all.
Using the preceding summary, you can identify the following entities:
FIGURE 4.34 THE NINTH TINY COLLEGE ERD SEGMENT
PROFESSOR
SCHOOL
DEPARTMENT
COURSE
CLASS
SEMESTER
STUDENT
BUILDING
ROOM
ENROLL (the associative entity between STUDENT and CLASS)
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146 Part 2
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Once you have discovered the relevant entities, you can define the initial set of relationships among them. Next, you describe the entity attributes. Identifying the attributes of the entities helps you to better understand the relationships among enti- ties. Table 4.4 summarizes the ERM’s components, and names the entities and their relations.
TABLE 4.4
COMPONENTS OF THE ERM
ENTITY
RELATIONSHIP
CONNECTIVITY
ENTITY
SCHOOL
operates
1:M
DEPARTMENT
DEPARTMENT
has
1:M
STUDENT
DEPARTMENT
employs
1:M
PROFESSOR
DEPARTMENT
offers
1:M
COURSE
COURSE
generates
1:M
CLASS
SEMESTER
includes
1:M
CLASS
PROFESSOR
is dean of
1:1
SCHOOL
PROFESSOR
chairs
1:1
DEPARTMENT
PROFESSOR
teaches
1:M
CLASS
PROFESSOR
advises
1:M
STUDENT
STUDENT
enrolls in
M:N
CLASS
BUILDING
contains
1:M
ROOM
ROOM
is used for
1:M
CLASS
Note: ENROLL is the composite entity that implements the M:N relationship “STUDENT enrolls in CLASS.”
You must also define the connectivity and cardinality for the just-discovered rela- tions based on the business rules. However, to avoid crowding the diagram, the car- dinalities are not shown. Figure 4.35 shows the Crow’s Foot ERD for Tiny College. Note that this is an implementation-ready model, so it shows the ENROLL compos- ite entity.
Figure 4.36 shows the conceptual UML class diagram for Tiny College. Note that this class diagram depicts the M:N relationship between STUDENT and CLASS. Fig- ure 4.37 shows the implementation-ready UML class diagram for Tiny College (note that the ENROLL composite entity is shown in this class diagram). If you are a good observer, you will also notice that the UML class diagrams in Figures 4.36 and 4.37 show the entity and attribute names but do not identify the primary key attributes. The reason goes back to UML’s roots. UML class diagrams are an object-oriented modeling language, and therefore do not support the notion of “primary or foreign keys” found mainly in the relational world. Rather, in the object-oriented world, objects inherit a unique object identifier at creation time. For more information, see Appendix G, Object-Oriented Databases.
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Chapter 4 Entity Relationship (ER) Modeling 147
FIGURE 4.35 THE COMPLETED TINY COLLEGE ERD
4-3 Database Design Challenges: Conflicting Goals
Database designers must often make design compromises that are triggered by conflict- ing goals, such as adherence to design standards (design elegance), processing speed, and information requirements.
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148 Part 2
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FIGURE 4.36 THE CONCEPTUAL UML CLASS DIAGRAM FOR TINY COLLEGE
• Design standards. The database design must conform to design standards. Such stan- dards guide you in developing logical structures that minimize data redundancies, thereby minimizing the likelihood that destructive data anomalies will occur. You have also learned how standards prescribe avoiding nulls to the greatest extent pos- sible. In fact, you have learned that design standards govern the presentation of all components within the database design. In short, design standards allow you to work with well-defined components and to evaluate the interaction of those components with some precision. Without design standards, it is nearly impossible to formulate a proper design process, to evaluate an existing design, or to trace the likely logical impact of changes in design.
• Processing speed. In many organizations, particularly those that generate large num- bers of transactions, high processing speeds are often a top priority in database design. High processing speed means minimal access time, which may be achieved by minimizing the number and complexity of logically desirable relationships.
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Chapter 4 Entity Relationship (ER) Modeling 149
FIGURE 4.37 THE IMPLEMENTATION-READY UML CLASS DIAGRAM FOR TINY COLLEGE
For example, a “perfect” design might use a 1:1 relationship to avoid nulls, while a design that emphasizes higher transaction speed might combine the two tables to avoid the use of an additional relationship, using dummy entries to avoid the nulls. If the focus is on data-retrieval speed, you might also be forced to include derived attributes in the design.
• Information requirements. The quest for timely information might be the focus of database design. Complex information requirements may dictate data transfor- mations, and they may expand the number of entities and attributes within the design. Therefore, the database may have to sacrifice some of its “clean” design structures and high transaction speed to ensure maximum information genera- tion. For example, suppose that a detailed sales report must be generated period- ically. The sales report includes all invoice subtotals, taxes, and totals; even the invoice lines include subtotals. If the sales report includes hundreds of thousands (or even millions) of invoices, computing the totals, taxes, and subtotals is likely
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150 Part 2
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to take some time. If those computations had been made and the results had been stored as derived attributes in the INVOICE and LINE tables at the time of the transaction, the real-time transaction speed might have declined. However, that loss of speed would only be noticeable if there were many simultaneous transac- tions. The cost of a slight loss of transaction speed at the front end and the addi- tion of multiple derived attributes is likely to pay off when the sales reports are generated (not to mention that it will be simpler to generate the queries).
A design that meets all logical requirements and design conventions is an import- ant goal. However, if this perfect design fails to meet the customer’s transaction speed and information requirements, the designer will not have done a proper job from the end user’s point of view. Compromises are a fact of life in the real world of database design.
Even while focusing on the entities, attributes, relationships, and constraints, the designer should begin thinking about end-user requirements such as performance, secu- rity, shared access, and data integrity. The designer must consider processing require- ments and verify that all update, retrieval, and deletion options are available. Finally, a design is of little value unless the end product can deliver all specified query and report- ing requirements.
You will probably discover that even the best design process produces an ERD that requires further changes mandated by operational requirements. Such changes should not discourage you from using the process. ER modeling is essential in the development of a sound design that can meet the demands of adjustment and growth. Using ERDs yields perhaps the richest bonus of all: a thorough understanding of how an organization really functions.
Occasionally, design and implementation problems do not yield “clean” implemen- tation solutions. To get a sense of the design and implementation choices a database designer faces, you will revisit the 1:1 recursive relationship “EMPLOYEE is married to EMPLOYEE,” first examined in Figure 4.18. Figure 4.38 shows three different ways of implementing such a relationship.
Note that the EMPLOYEE_V1 table in Figure 4.38 is likely to yield data anomalies. For example, if Anne Jones divorces Anton Shapiro, two records must be updated—by setting the respective EMP_SPOUSE values to null—to properly reflect that change. If only one record is updated, inconsistent data occurs. The problem becomes even worse if several of the divorced employees then marry each other. In addition, that implementa- tion also produces undesirable nulls for employees who are not married to other employ- ees in the company.
Another approach would be to create a new entity shown as MARRIED_V1 in a 1:M relationship with EMPLOYEE. (See Figure 4.38.) This second implementation does eliminate the nulls for employees who are not married to other employees in the same company. (Such employees would not be entered in the MARRIED_V1 table.) However, this approach still yields possible duplicate values. For example, the marriage between employees 345 and 347 may still appear twice, once as 345,347 and once as 347,345. (Because each of those permutations is unique the first time it appears, the creation of a unique index will not solve the problem.)
• •
As you can see, the first two implementations yield several problems:
Both solutions use synonyms. The EMPLOYEE_V1 table uses EMP_NUM and EMP_ SPOUSE to refer to an employee. The MARRIED_V1 table uses the same synonyms.
Both solutions are likely to produce redundant data. For example, it is possible to enter employee 345 as married to employee 347 and to enter employee 347 as married to employee 345.
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Chapter 4 Entity Relationship (ER) Modeling 151
FIGURE 4.38 VARIOUS IMPLEMENTATIONS OF THE 1:1 RECURSIVE RELATIONSHIP
Table name: EMPLOYEE_V1
Database name: Ch04_PartCo
Table name: EMPLOYEE Table name: MARRIED_V1
First implementation
Table name: MARRIAGE Table name: MARPART Table name: EMPLOYEE
Second implementation
The relational diagram for the third implementation
Third implementation
• Both solutions are likely to produce inconsistent data. For example, it is possible to have data pairs such as 345,347 and 348,345 and 347,349, none of which will vio- late entity integrity requirements because they are all unique. However, this solution would allow any one employee to be married to multiple employees.
A third approach would be to have two new entities, MARRIAGE and MARPART, in a 1:M relationship. MARPART contains the EMP_NUM foreign key to EMPLOYEE. (See the relational diagram in Figure 4.38.) However, even this approach has issues. It requires the collection of additional data regarding the employees’ marriage—the marriage date. If the business users do not need this data, then requiring them to collect it would be inappropriate. To ensure that an employee occurs only once in any given marriage, you would have to create a unique index on the EMP_NUM attribute in the MARPART table. Another potential problem with this solution is that the database implementation would theoretically allow more than two employees to “participate” in the same marriage.
As you can see, a recursive 1:1 relationship yields many different solutions with varying degrees of effectiveness and adherence to basic design principles. Any of the preceding solutions would likely involve the creation of program code to help ensure the integrity and consistency of the data. In a later chapter, you will exam- ine the creation of database triggers that can do exactly that. Your job as a database designer is to use your professional judgment to yield a solution that meets the
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152 Part 2
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requirements imposed by business rules, processing requirements, and basic design principles.
Finally, document, document, and document! Put all design activities in writing, and then review what you have written. Documentation not only helps you stay on track during the design process, it also enables you and your coworkers to pick up the design thread when the time comes to modify the design. Although the need for doc- umentation should be obvious, one of the most vexing problems in database and sys- tems analysis work is that this need is often ignored in the design and implementation stages. The development of organizational documentation standards is an important aspect of ensuring data compatibility and coherence.
Summary
• The ERM uses ERDs to represent the conceptual database as viewed by the end user. The ERM’s main components are entities, relationships, and attributes. The ERD includes connectivity and cardinality notations, and can also show relationship strength, relationship participation (optional or mandatory), and degree of relation- ship (such as unary, binary, or ternary).
• Connectivity describes the relationship classification (1:1, 1:M, or M:N). Cardinality expresses the specific number of entity occurrences associated with an occurrence of a related entity. Connectivities and cardinalities are usually based on business rules.
• In the ERM, an M:N relationship is valid at the conceptual level. However, when imple- menting the ERM in a relational database, the M:N relationship must be mapped to a set of 1:M relationships through a composite entity.
• ERDs may be based on many different ERMs. However, regardless of which model is selected, the modeling logic remains the same. Because no ERM can accurately por- tray all real-world data and action constraints, application software must be used to augment the implementation of at least some of the business rules.
• Unified Modeling Language (UML) class diagrams are used to represent the static data structures in a data model. The symbols used in the UML class and ER diagrams are very similar. The UML class diagrams can be used to depict data models at the conceptual or implementation abstraction levels.
• Database designers, no matter how well they can produce designs that conform to all applicable modeling conventions, are often forced to make design compromises. Those compromises are required when end users have vital transaction-speed and information requirements that prevent the use of “perfect” modeling logic and adher- ence to all modeling conventions. Therefore, database designers must use their pro- fessional judgment to determine how and to what extent the modeling conventions are subject to modification. To ensure that their professional judgments are sound, database designers must have detailed and in-depth knowledge of data-modeling conventions. It is also important to document the design process from beginning to end, which helps keep the design process on track and allows for easy modifications in the future.
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1. What two conditions must be met before an entity can be classified as a weak entity? Give an example of a weak entity.
2. What is a strong (or identifying) relationship, and how is it depicted in a Crow’s Foot ERD?
3. Given the business rule “an employee may have many degrees,” discuss its effect on attributes, entities, and relationships. (Hint: Remember what a multivalued attribute is and how it might be implemented.)
4. What is a composite entity, and when is it used?
5. Suppose you are working within the framework of the conceptual model in Figure Q4.5.
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Chapter 4 Entity Relationship (ER) Modeling 153
FIGURE Q4.5 THE CONCEPTUAL MODEL FOR QUESTION 5
Key Terms
binary relationship cardinality
composite attribute composite identifier connectivity
derived attribute existence-dependent existence-independent identifier
iterative process
mandatory participation multivalued attribute optional attribute optional participation participants
recursive relationship regular entity relational schema relationship degree required attribute
simple attribute
single-valued attribute
strong entity
strong (identifying) relationship
ternary relationship unary relationship weak entity
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
weak (non-identifying) relationship
Review Questions

154 Part 2
Design Concepts
Given the conceptual model in Figure Q4.5:
a. Write the business rules that are reflected in it.
b. Identify all of the cardinalities.
6. What is a recursive relationship? Give an example.
7. How would you (graphically) identify each of the following ERM components in a Crow’s Foot notation?
a. an entity
b. the cardinality (0,N)
c. a weak relationship
d. a strong relationship
8. Discuss the difference between a composite key and a composite attribute. How would each be indicated in an ERD?
9. What two courses of action are available to a designer who encounters a multivalued attribute?
10. What is a derived attribute? Give an example.
11. HowisarelationshipbetweenentitiesindicatedinanERD?Giveanexampleusing the Crow’s Foot notation.
12. Discusstwowaysinwhichthe1:MrelationshipbetweenCOURSEandCLASScan be implemented. (Hint: Think about relationship strength.)
13. HowisacompositeentityrepresentedinanERD,andwhatisitsfunction?Illustrate the Crow’s Foot notation.
14. Whatthree(oftenconflicting)databaserequirementsmustbeaddressedindatabase design?
15. Briefly, but precisely, explain the difference between single-valued attributes and simple attributes. Give an example of each.
16. What are multivalued attributes, and how can they be handled within the database design?
Questions 17–20 are based on the ERD in Figure Q4.17.
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FIGURE Q4.17 THE ERD FOR QUESTIONS 17-20

17. Write the 10 cardinalities that are appropriate for this ERD.
18. Write the business rules reflected in this ERD.
19. WhattwoattributesmustbecontainedinthecompositeentitybetweenSTOREand PRODUCT? Use proper terminology in your answer.
20. DescribepreciselythecompositionoftheDEPENDENTweakentity’sprimarykey. Use proper terminology in your answer.
21. Thelocalcityyouthleagueneedsadatabasesystemtohelptrackchildrenwhosign up to play soccer. Data needs to be kept on each team, the children who will play on each team, and their parents. Also, data needs to be kept on the coaches for each team.
Draw a data model with the entities and attributes described here. Entities required: Team, Player, Coach, and Parent
Attributes required:
Team: Team ID number, Team name, and Team colors
Player: Player ID number, Player first name, Player last name, and Player age
Coach: Coach ID number, Coach first name, Coach last name, and Coach home phone number
Parent: Parent ID number, Parent last name, Parent first name, Home phone num- ber, and Home address (Street, City, State, and Zip code)
The following relationships must be defined:
• • •
Team is related to Player. Team is related to Coach. Player is related to Parent.
Connectivities and participations are defined as follows:
• A Team may or may not have a Player.
• A Player must have a Team.
• A Team may have many Players.
• A Player has only one Team.
• A Team may or may not have a Coach.
• A Coach must have a Team.
• A Team may have many Coaches.
• A Coach has only one Team.
• A Player must have a Parent.
• A Parent must have a Player.
• A Player may have many Parents.
• A Parent may have many Players.
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Chapter 4 Entity Relationship (ER) Modeling 155

156 Part 2 Design Concepts
Problems
1. Use the following business rules to create a Crow’s Foot ERD. Write all appropriate connectivities and cardinalities in the ERD.
• A department employs many employees, but each employee is employed by only one department.
• Some employees, known as “rovers,” are not assigned to any department.
• A division operates many departments, but each department is operated by only
one division.
• An employee may be assigned many projects, and a project may have many employees assigned to it.
• A project must have at least one employee assigned to it.
• One of the employees manages each department, and each department is man-
aged by only one employee.
• One of the employees runs each division, and each division is run by only one employee.
2. Create a complete ERD in Crow’s Foot notation that can be implemented in the relational model using the following description of operations. Hot Water (HW) is a small start-up company that sells spas. HW does not carry any stock. A few spas are set up in a simple warehouse so customers can see some of the models available, but any products sold must be ordered at the time of the sale.
• HW can get spas from several different manufacturers.
• Each manufacturer produces one or more different brands of spas.
• Each and every brand is produced by only one manufacturer.
• Every brand has one or more models.
• Every model is produced as part of a brand. For example, Iguana Bay Spas is a manufacturer that produces Big Blue Iguana spas, a premium-level brand, and Lazy Lizard spas, an entry-level brand. The Big Blue Iguana brand offers several models, including the BBI-6, an 81-jet spa with two 6-hp motors, and the BBI-10, a 102-jet spa with three 6-hp motors.
• Every manufacturer is identified by a manufacturer code. The company name, address, area code, phone number, and account number are kept in the system for every manufacturer.
• For each brand, the brand name and brand level (premium, mid-level, or entry- level) are kept in the system.
• For each model, the model number, number of jets, number of motors, number of horsepower per motor, suggested retail price, HW retail price, dry weight, water capacity, and seating capacity must be kept in the system.
3. The Jonesburgh County Basketball Conference (JCBC) is an amateur basketball association. Each city in the county has one team as its representative. Each team has a maximum of 12 players and a minimum of 9 players. Each team also has up to 3 coaches (offensive, defensive, and physical training coaches). During the season,
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Chapter 4 Entity Relationship (ER) Modeling 157 each team plays 2 games (home and visitor) against each of the other teams. Given
those conditions, do the following:
• Identify the connectivity of each relationship.
• Identify the type of dependency that exists between CITY and TEAM.
• Identify the cardinality between teams and players and between teams and city.
• Identify the dependency between COACH and TEAM and between TEAM and PLAYER.
• Draw the Chen and Crow’s Foot ERDs to represent the JCBC database.
• Draw the UML class diagram to depict the JCBC database.
4. Create an ERD based on the Crow’s Foot notation using the following requirements:
• An INVOICE is written by a SALESREP. Each sales representative can write many invoices, but each invoice is written by a single sales representative.
• The INVOICE is written for a single CUSTOMER. However, each customer can have many invoices.
• An INVOICE can include many detail lines (LINE), each of which describes one product bought by the customer.
• The product information is stored in a PRODUCT entity.
• The product’s vendor information is found in a VENDOR entity.
5. The Hudson Engineering Group (HEG) has contacted you to create a conceptual model whose application will meet the expected database requirements for the com- pany’s training program. The HEG administrator gives you the following description of the training group’s operating environment. (Hint: Some of the following sentences identify the volume of data rather than cardinalities. Can you tell which ones?)
The HEG has 12 instructors and can handle up to 30 trainees per class. HEG offers 5 Advanced Technology courses, each of which may generate several classes. If a class has fewer than 10 trainees, it will be canceled. Therefore, it is possible for a course not to generate any classes. Each class is taught by one instructor. Each instructor may teach up to 2 classes or may be assigned to do research only. Each trainee may take up to 2 classes per year.
Given that information, do the following:
a. Define all of the entities and relationships. (Use Table 4.4 as your guide.)
b. Describe the relationship between instructor and class in terms of connectivity, cardinality, and existence dependence.
6. Automata, Inc., produces specialty vehicles by contract. The company operates sev- eral departments, each of which builds a particular vehicle, such as a limousine, truck, van, or RV.
• Before a new vehicle is built, the department places an order with the purchasing department to request specific components. Automata’s purchasing department is interested in creating a database to keep track of orders and to accelerate the process of delivering materials.
• The order received by the purchasing department may contain several different items. An inventory is maintained so the most frequently requested items are delivered almost immediately. When an order comes in, it is checked to deter- mine whether the requested item is in inventory. If an item is not in inventory, it must be ordered from a supplier. Each item may have several suppliers.
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158 Part 2
Design Concepts
Given that functional description of the processes at Automata’s purchasing depart- ment, do the following:
a. Identify all of the main entities.
b. Identify all of the relations and connectivities among entities.
c. Identify the type of existence dependence in all the relationships.
d. Give at least two examples of the types of reports that can be obtained from the database.
7. United Helpers is a nonprofit organization that provides aid to people after natural disasters. Based on the following brief description of operations, create the appropri- ate fully labeled Crow’s Foot ERD.
• Volunteers carry out the tasks of the organization. The name, address, and tele- phone number are tracked for each volunteer. Each volunteer may be assigned to several tasks, and some tasks require many volunteers. A volunteer might be in the system without having been assigned a task yet. It is possible to have tasks that no one has been assigned. When a volunteer is assigned to a task, the system should track the start time and end time of that assignment.
• Each task has a task code, task description, task type, and task status. For example, there may be a task with task code “101,” a description of “answer the telephone,” a type of “recurring,” and a status of “ongoing.” Another task might have a code of “102,” a description of “prepare 5,000 packages of basic medical supplies,” a type of “packing,” and a status of “open.”
• For all tasks of type “packing,” there is a packing list that specifies the contents of the packages. There are many packing lists to produce different packages, such as basic medical packages, child-care packages, and food packages. Each packing list has an ID number, a packing list name, and a packing list description, which describes the items that should make up the package. Every packing task is associated with only one pack- ing list. A packing list may not be associated with any tasks, or it may be associated with many tasks. Tasks that are not packing tasks are not associated with any packing list.
• Packing tasks result in the creation of packages. Each individual package of sup- plies produced by the organization is tracked, and each package is assigned an ID number. The date the package was created and its total weight are recorded. A given package is associated with only one task. Some tasks (such as “answer the phones”) will not produce any packages, while other tasks (such as “prepare 5,000 packages of basic medical supplies”) will be associated with many packages.
• The packing list describes the ideal contents of each package, but it is not always possible to include the ideal number of each item. Therefore, the actual items included in each package should be tracked. A package can contain many differ- ent items, and a given item can be used in many different packages.
• Each item that the organization provides has an item ID number, item descrip- tion, item value, and item quantity on hand stored in the system. Along with tracking the actual items that are placed in each package, the quantity of each item placed in the package must be tracked as well. For example, a packing list may state that basic medical packages should include 100 bandages, 4 bottles of iodine, and 4 bottles of hydrogen peroxide. However, because of the limited sup- ply of items, a given package may include only 10 bandages, 1 bottle of iodine, and no hydrogen peroxide. The fact that the package includes bandages and iodine needs to be recorded along with the quantity of each item included. It is possible
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Chapter 4 Entity Relationship (ER) Modeling 159 for the organization to have items that have not been included in any package yet,
but every package will contain at least one item.
8. Using the Crow’s Foot notation, create an ERD that can be implemented for a med- ical clinic using the following business rules:
• A patient can make many appointments with one or more doctors in the clinic, and a doctor can accept appointments with many patients. However, each appointment is made with only one doctor and one patient.
• Emergency cases do not require an appointment. However, for appointment management purposes, an emergency is entered in the appointment book as “unscheduled.”
• If kept, an appointment yields a visit with the doctor specified in the appoint- ment. The visit yields a diagnosis and, when appropriate, treatment.
• With each visit, the patient’s records are updated to provide a medical history.
• Each patient visit creates a bill. Each patient visit is billed by one doctor, and each
doctor can bill many patients.
• Each bill must be paid. However, a bill may be paid in many installments, and a payment may cover more than one bill.
• A patient may pay the bill directly, or the bill may be the basis for a claim submit- ted to an insurance company.
• If the bill is paid by an insurance company, the deductible is submitted to the patient for payment.
9. Create a Crow’s Foot notation ERD to support the following business operations:
• A friend of yours has opened Professional Electronics and Repairs (PEAR) to repair smartphones, laptops, tablets, and MP3 players. She wants you to create a database to help her run her business.
• When a customer brings a device to PEAR for repair, data must be recorded about the customer, the device, and the repair. The customer’s name, address, and a con- tact phone number must be recorded (if the customer has used the shop before, the information already in the system for the customer is verified as being cur- rent). For the device to be repaired, the type of device, model, and serial number are recorded (or verified if the device is already in the system). Only customers who have brought devices into PEAR for repair will be included in this system.
• Since a customer might sell an older device to someone else who then brings the device to PEAR for repair, it is possible for a device to be brought in for repair by more than one customer. However, each repair is associated with only one cus- tomer. When a customer brings in a device to be fixed, it is referred to as a repair request, or just “repair,” for short. Each repair request is given a reference number, which is recorded in the system along with the date of the request, and a descrip- tion of the problem(s) that the customer wants fixed. It is possible for a device to be brought to the shop for repair many different times, and only devices that are brought in for repair are recorded in the system. Each repair request is for the repair of one and only one device. If a customer needs multiple devices fixed, then each device will require its own repair request.
• There are a limited number of repair services that PEAR can perform. For each repair service, there is a service ID number, description, and charge. “Charge” is how much the customer is charged for the shop to perform the service, including
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160 Part 2
Design Concepts
any parts used. The actual repair of a device is the performance of the services necessary to address the problems described by the customer. Completing a repair request may require the performance of many services. Each service can be performed many different times during the repair of different devices, but each service will be performed only once during a given repair request.
• All repairs eventually require the performance of at least one service, but which services will be required may not be known at the time the repair request is made. It is possible for services to be available at PEAR but that have never been required in performing any repair.
• Some services involve only labor activities and no parts are required, but most services require the replacement of one or more parts. The quantity of each part required in the performance of each service should also be recorded. For each part, the part number, part description, quantity in stock, and cost is recorded in the system. The cost indicated is the amount that PEAR pays for the part. Some parts may be used in more than one service, but each part is required for at least one service.
10. Luxury-Oriented Scenic Tours (LOST) provides guided tours to groups of visitors to the Washington D.C. area. In recent years, LOST has grown quickly and is having difficulty keeping up with all of the various information needs of the company. The company’s operations are as follows:
• LOST offers many different tours. For each tour, the tour name, approxi- mate length (in hours), and fee charged is needed. Guides are identified by an employee ID, but the system should also record a guide’s name, home address, and date of hire. Guides take a test to be qualified to lead specific tours. It is important to know which guides are qualified to lead which tours and the date that they completed the qualification test for each tour. A guide may be qualified to lead many different tours. A tour can have many different qualified guides. New guides may or may not be qualified to lead any tours, just as a new tour may or may not have any qualified guides.
• Every tour must be designed to visit at least three locations. For each location, a name, type, and official description are kept. Some locations (such as the White House) are visited by more than one tour, while others (such as Arlington Ceme- tery) are visited by a single tour. All locations are visited by at least one tour. The order in which the tour visits each location should be tracked as well.
• When a tour is actually given, that is referred to as an “outing.” LOST sched- ules outings well in advance so they can be advertised and so employees can understand their upcoming work schedules. A tour can have many scheduled outings, although newly designed tours may not have any outings scheduled. Each outing is for a single tour and is scheduled for a particular date and time. All outings must be associated with a tour. All tours at LOST are guided tours, so a guide must be assigned to each outing. Each outing has one and only one guide. Guides are occasionally asked to lead an outing of a tour even if they are not officially qualified to lead that tour. Newly hired guides may not have ever been scheduled to lead any outings. Tourists, called “clients” by LOST, pay to join a scheduled outing. For each client, the name and telephone number are recorded. Clients may sign up to join many different outings, and each outing can have many clients. Information is kept only on clients who have signed up for at least one outing, although newly scheduled outings may not have any clients signed up yet.
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a. Create a Crow’s Foot notation ERD to support LOST operations.
b. The operations provided state that it is possible for a guide to lead an outing of a tour even if the guide is not officially qualified to lead outings of that tour. Imagine that the business rules instead specified that a guide is never, under any circumstance, allowed to lead an outing unless he or she is qualified to lead outings of that tour. How could the data model in Part a. be modified to
enforce this new constraint?
Note
Chapter 4 Entity Relationship (ER) Modeling 161
You can use the following cases and additional problems from the Instructor Online Com- panion as the basis for class projects. These problems illustrate the challenge of translating a description of operations into a set of business rules that will define the components for an ERD you can implement successfully. These problems can also be used as the basis for discussions about the components and contents of a proper description of operations. If you want to create databases that can be successfully implemented, you must learn to separate the generic background material from the details that directly affect database design. You must also keep in mind that many constraints cannot be incorporated into the database design; instead, such constraints are handled by the application software.
11. The administrators of Tiny College are so pleased with your design and implemen- tation of their student registration and tracking system that they want you to expand the design to include the database for their motor vehicle pool. A brief description of operations follows:
• Faculty members may use the vehicles owned by Tiny College for officially sanc- tioned travel. For example, the vehicles may be used by faculty members to travel to off-campus learning centers, to travel to locations at which research papers are presented, to transport students to officially sanctioned locations, and to travel for public service purposes. The vehicles used for such purposes are managed by Tiny College’s Travel Far But Slowly (TFBS) Center.
• Using reservation forms, each department can reserve vehicles for its faculty, who are responsible for filling out the appropriate trip completion form at the end of a trip. The reservation form includes the expected departure date, vehicle type required, destination, and name of the authorized faculty member. The faculty member who picks up a vehicle must sign a checkout form to log out the vehicle and pick up a trip completion form. (The TFBS employee who releases the vehicle for use also signs the checkout form.) The faculty member’s trip completion form includes the faculty member’s identification code, the vehicle’s identification, the odometer readings at the start and end of the trip, maintenance complaints (if any), gallons of fuel purchased (if any), and the Tiny College credit card number used to pay for the fuel. If fuel is purchased, the credit card receipt must be stapled to the trip completion form. Upon receipt of the trip completion form, the faculty mem- ber’s department is billed at a mileage rate based on the vehicle type used: sedan, station wagon, panel truck, minivan, or minibus. (Hint: Do not use more entities than are necessary. Remember the difference between attributes and entities!)
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Cases

162 Part 2
Design Concepts
• All vehicle maintenance is performed by TFBS. Each time a vehicle requires maintenance, a maintenance log entry is completed on a prenumbered mainte- nance log form. The maintenance log form includes the vehicle identification, brief description of the type of maintenance required, initial log entry date, date the maintenance was completed, and name of the mechanic who released the vehicle back into service. (Only mechanics who have an inspection authorization may release a vehicle back into service.)
• As soon as the log form has been initiated, the log form’s number is transferred to a maintenance detail form; the log form’s number is also forwarded to the parts department manager, who fills out a parts usage form on which the maintenance log number is recorded. The maintenance detail form contains separate lines for each maintenance item performed, for the parts used, and for identification of the mechanic who performed the maintenance. When all maintenance items have been completed, the maintenance detail form is stapled to the maintenance log form, the maintenance log form’s completion date is filled out, and the mechanic who releases the vehicle back into service signs the form. The stapled forms are then filed, to be used later as the source for various maintenance reports.
• TFBS maintains a parts inventory, including oil, oil filters, air filters, and belts of various types. The parts inventory is checked daily to monitor parts usage and to reorder parts that reach the “minimum quantity on hand” level. To track parts usage, the parts manager requires each mechanic to sign out the parts that are used to perform each vehicle’s maintenance; the parts manager records the main- tenance log number under which the part is used.
• Each month TFBS issues a set of reports. The reports include the mileage driven by vehicle, by department, and by faculty members within a department. In addi- tion, various revenue reports are generated by vehicle and department. A detailed parts usage report is also filed each month. Finally, a vehicle maintenance sum- mary is created each month.
Given that brief summary of operations, draw the appropriate (and fully labeled) ERD. Use the Crow’s foot methodology to indicate entities, relationships, connectiv- ities, and participations.
12. During peak periods, Temporary Employment Corporation (TEC) places temporary workers in companies. TEC’s manager gives you the following description of the business:
• TEC has a file of candidates who are willing to work.
• Any candidate who has worked before has a specific job history. (Naturally, no job history exists if the candidate has never worked.) Each time the candidate works, one additional job history record is created.
• Each candidate has earned several qualifications. Each qualification may be earned by more than one candidate. (For example, more than one candidate may have earned a Bachelor of Business Administration degree or a Microsoft Net- work Certification, and clearly a candidate may have earned both a BBA and a Microsoft Network Certification.)
• TEC offers courses to help candidates improve their qualifications.
• Every course develops one specific qualification; however, TEC does not offer a course for every qualification. Some qualifications are developed through multi- ple courses.
• Some courses cover advanced topics that require specific qualifications as pre- requisites. Some courses cover basic topics that do not require any prerequisite
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Chapter 4 Entity Relationship (ER) Modeling 163 qualifications. A course can have several prerequisites. A qualification can be a
prerequisite for more than one course.
• Courses are taught during training sessions. A training session is the presentation of a single course. Over time, TEC will offer many training sessions for each course; however, new courses may not have any training sessions scheduled right away.
• Candidates can pay a fee to attend a training session. A training session can accommodate several candidates, although new training sessions will not have any candidates registered at first.
• TEC also has a list of companies that request temporaries.
• Each time a company requests a temporary employee, TEC makes an entry in the Openings folder. That folder contains an opening number, a company name, required qualifications, a starting date, an anticipated ending date, and hourly pay.
• Each opening requires only one specific or main qualification.
• When a candidate matches the qualification, the job is assigned, and an entry is made in the Placement Record folder. The folder contains such information as an opening number, candidate number, and total hours worked. In addition, an entry is made in the job history for the candidate.
• An opening can be filled by many candidates, and a candidate can fill many openings.
• TEC uses special codes to describe a candidate’s qualifications for an opening. The
list of codes is shown in Table P4.12.
TABLE P4.12
CODE
DESCRIPTION
SEC-45
Secretarial work; candidate must type at least 45 words per minute
SEC-60
Secretarial work; candidate must type at least 60 words per minute
CLERK
General clerking work
PRG-VB
Programmer, Visual Basic
PRG-C++
Programmer, C++
DBA-ORA
Database Administrator, Oracle
DBA-DB2
Database Administrator, IBM DB2
DBA-SQLSERV
Database Administrator, MS SQL Server
SYS-1
Systems Analyst, level 1
SYS-2
Systems Analyst, level 2
NW-NOV
Network Administrator, Novell experience
WD-CF
Web Developer, ColdFusion
TEC’s management wants to keep track of the following entities:
COMPANY, OPENING, QUALIFICATION, CANDIDATE, JOB_HISTORY, PLACEMENT, COURSE, and SESSION. Given that information, do the following:
a. Draw the Crow’s Foot ERDs for this enterprise.
b. Identify all necessary relationships.
c. Identify the connectivity for each relationship.
d. Identify the mandatory and optional dependencies for the relationships.
e. Resolve all M:N relationships.
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164 Part 2
Design Concepts
13. Use the following description of the operations of the RC_Charter2 Company to complete this exercise.
• The RC_Charter2 Company operates a fleet of aircraft under the Federal Air Regula- tions (FAR) Part 135 (air taxi or charter) certificate, enforced by the FAA. The aircraft are available for air taxi (charter) operations within the United States and Canada.
• Charter companies provide so-called unscheduled operations—that is, charter flights take place only after a customer reserves the use of an aircraft at a des- ignated date and time to fly to one or more designated destinations; the aircraft transports passengers, cargo, or some combination of passengers and cargo. Of course, a customer can reserve many different charter trips during any time frame. However, for billing purposes, each charter trip is reserved by one and only one customer. Some of RC_Charter2’s customers do not use the compa- ny’s charter operations; instead, they purchase fuel, use maintenance services, or use other RC_Charter2 services. However, this database design will focus on the charter operations only.
• Each charter trip yields revenue for the RC_Charter2 Company. This revenue is generated by the charges a customer pays upon the completion of a flight. The charter flight charges are a function of aircraft model used, distance flown, wait- ing time, special customer requirements, and crew expenses. The distance flown charges are computed by multiplying the round-trip miles by the model’s charge per mile. Round-trip miles are based on the actual navigational path flown. The sample route traced in Figure P4.13 illustrates the procedure. Note that the num- ber of round-trip miles is calculated to be 130 + 200 + 180 + 390 = 900.
• Depending on whether a customer has RC_Charter2 credit authorization, the customer may do the following:
a. Pay the entire charter bill upon the completion of the charter flight.
b. Pay a part of the charter bill and charge the remainder to the account. The charge amount may not exceed the available credit.
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FIGURE P4.13 ROUND-TRIP MILE DETERMINATION
Pax Pickup
Intermediate Stop 200 miles
130 miles
Home Base
180 miles
390 miles
Destination

Chapter 4 Entity Relationship (ER) Modeling 165 c. Charge the entire charter bill to the account. The charge amount may not
exceed the available credit.
d. Customers may pay all or part of the existing balance for previous charter trips. Such payments may be made at any time and are not necessarily tied to a specific charter trip. The charter mileage charge includes the expense of the pilot(s) and other crew required by FAR 135. However, if customers request additional crew not required by FAR 135, those customers are charged for the crew members on an hourly basis. The hourly crew-member charge is based on each crew member’s qualifications.
e. The database must be able to handle crew assignments. Each charter trip requires the use of an aircraft, and a crew flies each aircraft. The smaller, pis- ton-engine charter aircraft require a crew consisting of only a single pilot. All jets and other aircraft that have a gross takeoff weight of at least 12,500 pounds require a pilot and a copilot, while some of the larger aircraft used to transport passengers may require flight attendants as part of the crew. Some of the older aircraft require the assignment of a flight engineer, and larger cargo-carrying aircraft require the assignment of a loadmaster. In short, a crew can consist of more than one person, and not all crew members are pilots.
f. The charter flight’s aircraft waiting charges are computed by multiplying the hours waited by the model’s hourly waiting charge. Crew expenses are limited to meals, lodging, and ground transportation.
The RC_Charter2 database must be designed to generate a monthly summary of all charter trips, expenses, and revenues derived from the charter records. Such records are based on the data that each pilot in command is required to record for each char- ter trip: trip date(s) and time(s), destination(s), aircraft number, pilot data and other crew data, distance flown, fuel usage, and other data pertinent to the charter flight. Such charter data is then used to generate monthly reports that detail revenue and operating cost information for customers, aircraft, and pilots. All pilots and other crew members are RC_Charter2 Company employees; that is, the company does not use contract pilots and crew.
FAR Part 135 operations are conducted under a strict set of requirements that govern the licensing and training of crew members. For example, pilots must have earned either a commercial license or an Airline Transport Pilot (ATP) license. Both licenses require appropriate ratings, which are specific competency requirements. For example, consider the following:
• To operate a multiengine aircraft designed for takeoffs and landings on land only, the appropriate rating is MEL, or Multiengine Landplane. When a multiengine aircraft can take off and land on water, the appropriate rating is MES, or Mul- tiengine Seaplane.
• The instrument rating is based on a demonstrated ability to conduct all flight operations with sole reference to cockpit instrumentation. The instrument rating is required to operate an aircraft under Instrument Meteorological Conditions (IMC), and all such operations are governed under FAR-specified Instrument Flight Rules (IFR). In contrast, operations conducted under “good weather” or visual flight conditions are based on the FAR Visual Flight Rules (VFR).
• The type rating is required for all aircraft with a takeoff weight of more than 12,500 pounds or for aircraft that are purely jet-powered. If an aircraft uses jet engines to drive propellers, that aircraft is said to be turboprop-powered. A turboprop—that
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166 Part 2
Design Concepts
is, a turbo-propeller-powered aircraft—does not require a type rating unless it meets the 12,500-pound weight limitation.
• Although pilot licenses and ratings are not time limited, exercising the privilege of the license and ratings under Part 135 requires both a current medical certifi- cate and a current Part 135 checkride. The following distinctions are important:
a. The medical certificate may be Class I or Class II. The Class I medical is more stringent than the Class II, and it must be renewed every six months. The Class II medical must be renewed yearly. If the Class I medical is not renewed during the six-month period, it automatically reverts to a Class II certificate. If the Class II medical is not renewed within the specified period, it automat- ically reverts to a Class III medical, which is not valid for commercial flight operations.
b. APart135checkrideisapracticalflightexaminationthatmustbesuccessfully completed every six months. The checkride includes all flight maneuvers and procedures specified in Part 135.
Nonpilot crew members must also have the proper certificates to meet specific job requirements. For example, loadmasters need an appropriate certificate, as do flight attendants. Crew members such as loadmasters and flight attendants may be required in operations that involve large aircraft with a takeoff weight of more than 12,500 pounds and more than 19 passengers; these crew members are also required to pass a written and practical exam periodically. The RC_Charter2 Company is required to keep a complete record of all test types, dates, and results for each crew member, as well as examination dates for pilot medical certificates.
In addition, all flight crew members are required to submit to periodic drug testing; the results must be tracked as well. Note that nonpilot crew members are not required to take pilot-specific tests such as Part 135 checkrides, nor are pilots required to take crew tests such as loadmaster and flight attendant practical exams. However, many crew members have licenses and certifications in several areas. For example, a pilot may have an ATP and a loadmaster certificate. If that pilot is assigned to be a loadmaster on a given charter flight, the loadmaster certificate is required. Similarly, a flight attendant may have earned a commercial pilot’s license. Sample data formats are shown in Table P4.13.
Pilots and other crew members must receive recurrency training appropriate to their work assignments. Recurrency training is based on an FAA-approved curricu- lum that is job specific. For example, pilot recurrency training includes a review of all applicable Part 135 flight rules and regulations, weather data interpretation, com- pany flight operations requirements, and specified flight procedures. The RC_Char- ter2 Company is required to keep a complete record of all recurrency training for each crew member subject to the training.
The RC_Charter2 Company is required to maintain a detailed record of all crew credentials and all training mandated by Part 135. The company must keep a com- plete record of each requirement and of all compliance data.
To conduct a charter flight, the company must have a properly maintained aircraft available. A pilot who meets all of the FAA’s licensing and currency requirements must fly the aircraft as Pilot in Command (PIC). For aircraft that are powered by pis- ton engines or turboprops and have a gross takeoff weight under 12,500 pounds, sin- gle-pilot operations are permitted under Part 135 as long as a properly maintained autopilot is available. However, even if FAR Part 135 permits single-pilot operations, many customers require the presence of a copilot who is capable of conducting the flight operations under Part 135.
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Chapter 4
Entity Relationship (ER) Modeling 167
TABLE P4.13
PART A TESTS
TEST CODE
1
2
3
4
5
6
7
PART B RESULTS EMPLOYEE
101
103
112
103
112
101
101
125
PART C LICENSES AND CERTIFICATIONS
LICENSE OR CERTIFICATE
ATP Comm Med-1 Med-2 Instr MEL LM
FA
EMPLOYEE LICENSE OR CERTIFICATE
101 Comm
TEST FREQUENCY
6 months 6 months 12 months 12 months 12 months Random
6 months
TEST RESULT
Pass-1 Pass-1 Pass-2 Pass-1 Pass-1 Pass-1 Pass-2 Pass-1
LICENSE OR CERTIFICATE DESCRIPTION
Airline Transport Pilot
Commercial license
Medical certificate, Class I
Medical certificate, Class II
Instrument rating
Multiengine Land aircraft rating
Loadmaster
Flight Attendant
DATE EARNED
12-Nov-93 28-Jun-94 9-Aug-94 21-Dec-95 23-Jun-02 18-Jan-96 27-Nov-05
TEST DESCRIPTION
Part 135 Flight Check
Medical, Class I
Medical, Class II
Loadmaster Practical
Flight Attendant Practical
Drug test
Operations, written exam
TEST CODE
TEST DATE
1
12-Nov-15
6
23-Dec-15
4
23-Dec-15
7
11-Jan-16
7
16-Jan-16
7
16-Jan-16
6
11-Feb-16
2
15-Feb-16
101
101
103
112
103
112
Instr MEL Comm FA Instr LM
The RC_Charter2 operations manager anticipates the lease of turbojet-powered aircraft, which are required to have a crew consisting of a pilot and copilot. Both the pilot and copilot must meet the same Part 135 licensing, ratings, and training requirements.
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168 Part 2
Design Concepts
The company also leases larger aircraft that exceed the 12,500-pound gross takeoff weight. Those aircraft might carry enough passengers to require the presence of one or more flight attendants. If those aircraft carry cargo that weighs more than 12,500 pounds, a loadmaster must be assigned as a crew member to supervise the loading and securing of the cargo. The database must be designed to meet the anticipated capability for additional charter crew assignments.
a. Giventhisincompletedescriptionofoperations,writeallapplicablebusinessrules to establish entities, relationships, optionalities, connectivities, and cardinalities. (Hint: Use the following five business rules as examples, and write the remaining business rules in the same format.) A customer may request many charter trips.
• Each charter trip is requested by only one customer.
• Some customers have not yet requested a charter trip.
• An employee may be assigned to serve as a crew member on many charter trips.
• Each charter trip may have many employees assigned to serve as crew members.
b. Draw the fully labeled and implementable Crow’s Foot ERD based on the busi- ness rules you wrote in Part a. of this problem. Include all entities, relationships, optionalities, connectivities, and cardinalities.
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Chapter 5
Advanced Data Modeling
In this chapter, you will learn:
• About the extended entity relationship (EER) model
• How entity clusters are used to represent multiple entities and relationships • The characteristics of good primary keys and how to select them
• How to use flexible solutions for special data-modeling cases
Preview
In the previous two chapters, you learned how to use entity relationship diagrams (ERDs) to properly create a data model. In this chapter, you will learn about the extended entity relationship (EER) model. The EER model builds on ER concepts and adds support for entity supertypes, subtypes, and entity clustering.
Most current database implementations are based on relational databases. Because the relational model uses keys to create associations among tables, it is essential to learn the characteristics of good primary keys and how to select them. Selecting a good primary key is too important to be left to chance, so this chapter covers the critical aspects of primary key identification and placement.
Focusing on practical database design, this chapter also illustrates some special design cases that highlight the importance of flexible designs, which can be adapted to meet the demands of changing data and information requirements. Data modeling is a vital step in the development of databases that in turn provides a good foundation for successful application development. Remember that good database applications cannot be based on bad database designs, and no amount of outstanding coding can overcome the limitations of poor database design.
Data Files and Available Formats
CH05_AirCo P P P P CH05_GCSdata P P P P CH05_TinyCollege P P P P
Data Files Available on cengagebrain.com
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
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170 Part 2 Note
Design Concepts
The extended entity relationship model discussed in this chapter includes advanced data modeling constructs such as specialization hierarchies. Although Microsoft Visio 2010 and earlier versions handled these constructs neatly, newer versions of Visio starting with Microsoft Visio 2013 removed support for many database modeling activities, including specialization hierarchies.
5-1 The Extended Entity Relationship Model
As the complexity of the data structures being modeled has increased and as application software requirements have become more stringent, the need to capture more infor- mation in the data model has increased. The extended entity relationship model (EERM), sometimes referred to as the enhanced entity relationship model, is the result of adding more semantic constructs to the original entity relationship (ER) model. As you might expect, a diagram that uses the EERM is called an EER diagram (EERD). In the following sections, you will learn about the main EER model constructs—entity supertypes, entity subtypes, and entity clustering—and see how they are represented in ERDs/EERDs.
5-1a Entity Supertypes and Subtypes
Because most employees possess a wide range of skills and special qualifications, data modelers must find a variety of ways to group employees based on their characteristics. For instance, a retail company could group employees as salaried and hourly, while a university could group employees as faculty, staff, and administrators.
The grouping of employees into various types provides two important benefits:
• It avoids unnecessary nulls in attributes when some employees have characteristics
that are not shared by other employees.
• It enables a particular employee type to participate in relationships that are unique to that employee type.
To illustrate those benefits, you will explore the case of an aviation business that employs pilots, mechanics, secretaries, accountants, database managers, and many other types of employees. Figure 5.1 illustrates how pilots share certain charac- teristics with other employees, such as a last name (EMP_LNAME) and hire date (EMP_HIRE_DATE). On the other hand, many pilot characteristics are not shared by other employees. For example, unlike other employees, pilots must meet special requirements such as flight hour restrictions, flight checks, and periodic training. Therefore, if all employee characteristics and special qualifications were stored in a single EMPLOYEE entity, you would have a lot of nulls or you would have to cre- ate a lot of needless dummy entries. In this case, special pilot characteristics such as EMP_LICENSE, EMP_RATINGS, and EMP_MED_TYPE will generate nulls for employees who are not pilots. In addition, pilots participate in some relationships that are unique to their qualifications. For example, not all employees can fly air- planes; only employees who are pilots can participate in the “employee flies airplane” relationship.
Based on the preceding discussion, you would correctly deduce that the PILOT entity stores only attributes that are unique to pilots, and that the EMPLOYEE entity stores attributes that are common to all employees. Based on that hierarchy, you can conclude
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
extended entity relationship model (EERM)
Sometimes referred
to as the enhanced entity relationship model; the result of adding more semantic constructs, such as entity supertypes, entity subtypes, and entity clustering, to the original entity relationship (ER) model.
EER diagram (EERD)
The entity relationship diagram resulting
from the application
of extended entity relationship concepts that provide additional semantic content in the ER model.
Copyright 2017 Cengage Lea Editorial review has deemed that

that PILOT is a subtype of EMPLOYEE, and that EMPLOYEE is the supertype of PILOT. In modeling terms, an entity supertype is a generic entity type that is related to one or more entity subtypes. The entity supertype contains common characteristics, and the entity subtypes each contain their own unique characteristics.
Two criteria help the designer determine when to use subtypes and supertypes:
• There must be different, identifiable kinds or types of the entity in the user’s environment.
• The different kinds or types of instances should each have one or more attributes that are unique to that kind or type of instance.
In the preceding example, because pilots meet both criteria of being an identifiable kind of employee and having unique attributes that other employees do not possess, it is appropriate to create PILOT as a subtype of EMPLOYEE. Assume that mechan- ics and accountants also each have attributes that are unique to them, respectively, and that clerks do not. In that case, MECHANIC and ACCOUNTANT would also be legiti- mate subtypes of EMPLOYEE because they are identifiable kinds of employees and have unique attributes. CLERK would not be an acceptable subtype of EMPLOYEE because it only satisfies one of the criteria—it is an identifiable kind of employee—but none of the attributes are unique to just clerks. In the next section, you will learn how entity super- types and subtypes are related in a specialization hierarchy.
5-1b Specialization Hierarchy
Entity supertypes and subtypes are organized in a specialization hierarchy, which depicts the arrangement of higher-level entity supertypes (parent entities) and lower-level entity subtypes (child entities). Figure 5.2 shows the specialization hierarchy formed by an EMPLOYEE supertype and three entity subtypes—PILOT, MECHANIC, and ACCOUNTANT. The specialization hierarchy reflects the 1:1 relationship between EMPLOYEE and its subtypes. For example, a PILOT subtype occurrence is related to one instance of the EMPLOYEE supertype, and a MECHANIC subtype occurrence is related to one instance of the EMPLOYEE supertype. The terminology and symbols in Figure 5.2 are explained throughout this chapter.
The relationships depicted within the specialization hierarchy are sometimes described in terms of “is-a” relationships. For example, a pilot is an employee, a mechanic is an employee, and an accountant is an employee. It is important to understand that within a specialization hierarchy, a subtype can exist only within the context of a supertype, and every subtype can have only one supertype to which it is directly related. However, a
entity supertype
In a generalization/ specialization hierarchy, a generic entity type that contains the common characteristics of entity subtypes.
entity subtype
In a generalization/ specialization hierarchy, a subset of an entity supertype. The entity supertype contains the common characteristics and the subtypes contain the unique characteristics of each entity.
specialization hierarchy
A hierarchy based on the top-down process of identifying lower- level, more specific entity subtypes from
a higher-level entity supertype. Specialization is based on grouping unique characteristics and relationships of the subtypes.
Chapter 5 Advanced Data Modeling 171
FIGURE5.1 NULLSCREATEDBYUNIQUEATTRIBUTES
Database name: Ch05_AirCo
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172 Part 2
Design Concepts
FIGURE5.2 ASPECIALIZATIONHIERARCHY
Online
Content
This chapter covers only specialization hierar- chies. The EER model also supports special- ization lattices, in which a subtype can have multiple parents (super- types). However, those concepts are better cov- ered under the object- oriented model in Appendix G, Object- Oriented Databases. The appendix is available at www.cengagebrain.com.
inheritance
In the EERD, the property that enables an entity subtype to inherit the attributes and relationships of the entity supertype.
specialization hierarchy can have many levels of supertype/subtype relationships—that is, you can have a specialization hierarchy in which a supertype has many subtypes. In turn, one of the subtypes is the supertype to other lower-level subtypes.
As you can see in Figure 5.2, the arrangement of entity supertypes and subtypes in a specialization hierarchy is more than a cosmetic convenience. Specialization hierarchies enable the data model to capture additional semantic content (meaning) into the ERD. A specialization hierarchy provides the means to:
• Support attribute inheritance.
• Define a special supertype attribute known as the subtype discriminator. • Define disjoint/overlapping constraints and complete/partial constraints.
The following sections cover such characteristics and constraints in more detail.
5-1c Inheritance
The property of inheritance enables an entity subtype to inherit the attributes and relation- ships of the supertype. As discussed earlier, a supertype contains attributes that are com- mon to all of its subtypes. In contrast, subtypes contain only the attributes that are unique to the subtype. For example, Figure 5.2 illustrates that pilots, mechanics, and accountants all inherit the employee number, last name, first name, middle initial, and hire date from the EMPLOYEE entity. However, Figure 5.2 also illustrates that pilots have unique attri- butes; the same is true for mechanics and accountants. One important inheritance charac- teristic is that all entity subtypes inherit their primary key attribute from their supertype. Note in Figure 5.2 that the EMP_NUM attribute is the primary key for each of the subtypes.
At the implementation level, the supertype and its subtype(s) depicted in the special- ization hierarchy maintain a 1:1 relationship. For example, the specialization hierarchy
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lets you replace the undesirable EMPLOYEE table structure in Figure 5.1 with two tables—one representing the supertype EMPLOYEE and the other representing the subtype PILOT. (See Figure 5.3.)
Entity subtypes inherit all relationships in which the supertype entity participates. For exam- ple, Figure 5.2 shows the EMPLOYEE entity supertype participating in a 1:M relationship with a DEPENDENT entity. Through inheritance, all subtypes also participate in that relationship. In specialization hierarchies with multiple levels of supertype and subtypes, a lower-level subtype inherits all of the attributes and relationships from all of its upper-level supertypes.
Inheriting the relationships of their supertypes does not mean that subtypes cannot have relationships of their own. Figure 5.4 illustrates a 1:M relationship between EMPLOYEE, a subtype of PERSON, and OFFICE. Because only employees and no other type of person will ever have an office within this system, the relationship is modeled with the subtype directly.
Chapter 5 Advanced Data Modeling 173
FIGURE 5.3 THE EMPLOYEE-PILOT SUPERTYPE-SUBTYPE RELATIONSHIP
Database name: Ch05_AirCo Table name: EMPLOYEE Table name: PILOT
FIGURE5.4 SPECIALIZATIONHIERARCHYWITHOVERLAPPINGSUBTYPES
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174 Part 2
Design Concepts
5-1d Subtype Discriminator
Online
Content
For a tutorial on using Visio 2010 to create a specialization hierar- chy, see Appendix A, Designing Databases with Visio Professional: A Tutorial, at www. cengagebrain.com.
Note
A subtype discriminator is the attribute in the supertype entity that determines to which subtype the supertype occurrence is related. In Figure 5.2, the subtype discrimi- nator is the employee type (EMP_TYPE).
It is common practice to show the subtype discriminator and its value for each subtype in the ER diagram, as shown in Figure 5.2. However, not all ER modeling tools follow that practice. For example, Microsoft Visio shows the subtype discriminator but not its value. In Figure 5.2, a text tool was used to manually add the discriminator value above the entity subtype, close to the connector line. Using Figure 5.2 as your guide, note that the supertype is related to a PILOT subtype if the EMP_TYPE has a value of “P.” If the EMP_TYPE value is “M,” the supertype is related to a MECHANIC subtype. If the EMP_ TYPE value is “A,” the supertype is related to the ACCOUNTANT subtype.
Note that the default comparison condition for the subtype discriminator attribute is the equality comparison. However, in some situations the subtype discriminator is not necessarily based on an equality comparison. For example, based on business requirements, you might create two new pilot subtypes: pilot-in-command (PIC)-qualified and copilot-qualified only. A PIC-qualified pilot must have more than 1,500 PIC flight hours. In this case, the subtype dis- criminator would be “Flight_Hours,” and the criteria would be > 1,500 or <= 1,500, respectively. 5-1e Disjoint and Overlapping Constraints An entity supertype can have disjoint or overlapping entity subtypes. In the aviation example, an employee can be a pilot, a mechanic, or an accountant. Assume that one of the business rules dictates that an employee cannot belong to more than one subtype at a time; that is, an employee cannot be a pilot and a mechanic at the same time. Disjoint subtypes, also known as nonoverlapping subtypes, are subtypes that contain a unique subset of the supertype entity set; in other words, each entity instance of the supertype can appear in only one of the subtypes. For example, in Figure 5.2, an employee (super- type) who is a pilot (subtype) can appear only in the PILOT subtype, not in any of the other subtypes. In an ERD, such disjoint subtypes are indicated by the letter d inside the category shape. On the other hand, if the business rule specifies that employees can have multiple classifications, the EMPLOYEE supertype may contain overlapping job classification sub- types. Overlapping subtypes are subtypes that contain nonunique subsets of the super- type entity set; that is, each entity instance of the supertype may appear in more than one subtype. For example, in a university environment, a person may be an employee, a student, or both. In turn, an employee may be a professor as well as an administrator. Because an employee may also be a student, STUDENT and EMPLOYEE are overlap- ping subtypes of the supertype PERSON, just as PROFESSOR and ADMINISTRATOR are overlapping subtypes of the supertype EMPLOYEE. Figure 5.4 illustrates overlapping subtypes with the letter o inside the category shape. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. In Visio 2010, you select the subtype discriminator when creating a category by using the Category shape from the available shapes. The Category shape is a small circle with a horizontal line underneath that connects the supertype to its subtypes. Visio 2013 does not support specialization hierarchy. subtype discriminator The attribute in the supertype entity that determines to which entity subtype each supertype occurrence is related. disjoint subtype In a specialization hierarchy, a unique and nonoverlapping subtype entity set. nonoverlapping subtype See disjoint subtype. overlapping subtype In a specialization hierarchy, a condition in which each entity instance (row) of the supertype can appear in more than one subtype. Copyright 2017 Cengage Lea Editorial review has deemed that It is common practice to show disjoint and overlapping symbols in the ERD. (See Figures 5.2 and 5.4.) However, not all ER modeling tools follow that practice. For example, by default, Visio shows only the subtype discriminator (using the Category shape), but not the disjoint and overlapping symbols. The Visio text tool was used to manually add the d and o symbols in Figures 5.2 and 5.4. Note As you learned earlier in this section, the implementation of disjoint subtypes is based on the value of the subtype discriminator attribute in the supertype. However, implementing overlapping subtypes requires the use of one discriminator attribute for each subtype. For example, in the case of the Tiny College database design in Chapter 4, Entity Relationship (ER) Modeling, a professor can also be an administrator. Therefore, the EMPLOYEE supertype would have the subtype discriminator attributes and values shown in Table 5.1. Chapter 5 Advanced Data Modeling 175 Alternative notations exist for representing disjoint and overlapping subtypes. For exam- ple, Toby J. Teorey popularized the use of G and Gs to indicate disjoint and overlapping subtypes. completeness constraint A constraint that specifies whether each entity supertype occurrence must also be a member of at least one subtype. The completeness constraint can be partial or total. TABLE 5.1 DISCRIMINATOR ATTRIBUTES WITH OVERLAPPING SUBTYPES DISCRIMINATOR ATTRIBUTES COMMENT PROFESSOR ADMINISTRATOR Y N The Employee is a member of the Professor subtype. N Y The Employee is a member of the Administrator subtype. Y Y The Employee is both a Professor and an Administrator. 5-1f Completeness Constraint The completeness constraint specifies whether each entity supertype occurrence must also be a member of at least one subtype. The completeness constraint can be partial or total. Partial completeness means that not every supertype occurrence is a member of a sub- type; some supertype occurrences may not be members of any subtype. Total completeness means that every supertype occurrence must be a member of at least one subtype. The ERDs in Figures 5.2 and 5.4 represent the completeness constraint based on the Visio Category shape. A single horizontal line under the circle represents a partial com- pleteness constraint; a double horizontal line under the circle represents a total com- pleteness constraint. Note Given the disjoint and overlapping subtypes and completeness constraints, it is possi- ble to have the specialization hierarchy constraint scenarios shown in Table 5.2. partial completeness In a generalization/ specialization hierarchy, a condition in which some supertype occurrences might not be members of any subtype. total completeness In a generalization/ specialization hierarchy, a condition in which every supertype occurrence must be a member of at least one subtype. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Alternative notations exist to represent the completeness constraint. For example, some notations use a single line (partial) or double line (total) to connect the supertype to the Category shape. 176 Part 2 Design Concepts TABLE 5.2 SPECIALIZATION HIERARCHY CONSTRAINT SCENARIOS TYPE DISJOINT CONSTRAINT OVERLAPPING CONSTRAINT Partial Supertype has optional subtypes. Subtype discriminator can be null. Subtype sets are unique. Supertype has optional subtypes. Subtype discriminators can be null. Subtype sets are not unique. Total Every supertype occurrence is a member of only one subtype. Subtype discriminator cannot be null. Subtype sets are unique. Every supertype occurrence is a member of at least one subtype. Subtype discriminators cannot be null. Subtype sets are not unique. 5-1g Specialization and Generalization You can use various approaches to develop entity supertypes and subtypes. For example, you can first identify a regular entity, and then identify all entity subtypes based on their distinguishing characteristics. You can also start by identifying multiple entity types and then later extract the common characteristics of those entities to create a higher-level supertype entity. Specialization is the top-down process of identifying lower-level, more specific entity subtypes from a higher-level entity supertype. Specialization is based on grouping the unique characteristics and relationships of the subtypes. In the aviation example, you used specialization to identify multiple entity subtypes from the original employee supertype. Generalization is the bottom-up process of identifying a higher-level, more generic entity supertype from lower-level entity subtypes. Generalization is based on grouping the common characteristics and relationships of the subtypes. For example, you might identify multiple types of musical instruments: piano, violin, and guitar. Using the generalization approach, you could identify a “string instrument” entity supertype to hold the common characteristics of the multiple subtypes. 5-2 Entity Clustering Developing an ER diagram entails the discovery of possibly hundreds of entity types and their respective relationships. Generally, the data modeler will develop an initial ERD that contains a few entities. As the design approaches completion, the ERD will contain hundreds of entities and relationships that crowd the diagram to the point of making it unreadable and inefficient as a communication tool. In those cases, you can use entity clusters to minimize the number of entities shown in the ERD. An entity cluster is a “virtual” entity type used to represent multiple entities and relationships in the ERD. An entity cluster is formed by combining multiple interrelated entities into a single, abstract entity object. An entity cluster is considered “virtual” or “abstract” in the sense that it is not actually an entity in the final ERD. Instead, it is a temporary entity used to represent multiple entities and relationships, with the purpose of simplifying the ERD and thus enhancing its readability. Figure 5.5 illustrates the use of entity clusters based on the Tiny College example in Chapter 4. Note that the ERD contains two entity clusters: • OFFERING, which groups the SEMESTER, COURSE, and CLASS entities and relationships • LOCATION, which groups the ROOM and BUILDING entities and relationships rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. specialization In a specialization hierarchy, the grouping of unique attributes into a subtype entity. generalization In a specialization hierarchy, the grouping of common attributes into a supertype entity. entity cluster A “virtual” entity type used to represent multiple entities and relationships in the ERD. An entity cluster is formed by combining multiple interrelated entities into a single abstract entity object. An entity cluster is considered “virtual” or “abstract” because it is not actually an entity in the final ERD. Copyright 2017 Cengage Lea Editorial review has deemed that Note also that the ERD in Figure 5.5 does not show attributes for the entities. When using entity clusters, the key attributes of the combined entities are no longer available. Without the key attributes, primary key inheritance rules change. In turn, the change in the inheritance rules can have undesirable consequences, such as changes in relationships—from identifying to nonidentifying or vice versa—and the loss of foreign key attributes from some entities. To eliminate those problems, the general rule is to avoid the display of attributes when entity clusters are used. 5-3 Entity Integrity: Selecting Primary Keys Arguably, the most important characteristic of an entity is its primary key (a single attri- bute or some combination of attributes), which uniquely identifies each entity instance. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 5 Advanced Data Modeling 177 FIGURE 5.5 TINY COLLEGE ERD USING ENTITY CLUSTERS is used for 178 Part 2 Design Concepts The primary key’s function is to guarantee entity integrity. Furthermore, primary keys and foreign keys work together to implement relationships in the relational model. Therefore, the importance of properly selecting the primary key has a direct bearing on the efficiency and effectiveness of database implementation. 5-3a Natural Keys and Primary Keys The concept of a unique identifier is commonly encountered in the real world. For exam- ple, you use class or section numbers to register for classes, invoice numbers to identify specific invoices, and account numbers to identify credit cards. Those examples illustrate natural identifiers or keys. A natural key or natural identifier is a real-world, generally accepted identifier used to distinguish—that is, uniquely identify—real-world objects. As its name implies, a natural key is familiar to end users and forms part of their day-to- day business vocabulary. Usually, if an entity has a natural identifier, a data modeler uses it as the primary key of the entity being modeled. Generally, most natural keys make acceptable primary key identifiers. The next section presents some basic guidelines for selecting primary keys. 5-3b Primary Key Guidelines A primary key is the attribute or combination of attributes that uniquely identifies entity instances in an entity set. However, can the primary key be based on, for example, 12 attributes? And just how long can a primary key be? In previous examples, why was EMP_NUM selected as a primary key of EMPLOYEE and not a combination of EMP_ LNAME, EMP_FNAME, EMP_INITIAL, and EMP_DOB? Can a single, 256-byte text attribute be a good primary key? There is no single answer to those questions, but data- base experts have built a body of practice over the years. This section examines that body of documented practices. First, you should understand the function of a primary key. Its main function is to uniquely identify an entity instance or row within a table. In particular, given a primary key value—that is, the determinant—the relational model can determine the value of all dependent attributes that “describe” the entity. Note that identification and description are separate semantic constructs in the model. The function of the primary key is to guar- antee entity integrity, not to “describe” the entity. Second, primary keys and foreign keys are used to implement relationships among entities. However, the implementation of such relationships is done mostly behind the scenes, hidden from end users. In the real world, end users identify objects based on the characteristics they know about the objects. For example, when shopping at a grocery store, you select products by taking them from a display shelf and reading the labels, not by looking at the stock number. It is wise for database applications to mimic the human selection process as much as possible. Therefore, database applications should let the end user choose among multiple descriptive narratives of different objects, while using primary key values behind the scenes. Keeping those concepts in mind, look at Table 5.3, which summarizes desirable primary key characteristics. 5-3c When To Use Composite Primary Keys In the previous section, you learned about the desirable characteristics of primary keys. For example, you learned that the primary key should use the minimum number of attributes possible. However, that does not mean that composite primary keys are not permitted in a model. In fact, composite primary keys are particularly useful in two cases: rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. natural key (natural identifier) A generally accepted identifier for real-world objects. As its name implies, a natural key is familiar to end users and forms part of their day-to-day business vocabulary. Copyright 2017 Cengage Lea Editorial review has deemed that Chapter 5 Advanced Data Modeling 179 TABLE 5.3 DESIRABLE PRIMARY KEY CHARACTERISTICS PK CHARACTERISTIC RATIONALE Unique values The PK must uniquely identify each entity instance. A primary key must be able to guarantee unique values. It cannot contain nulls. Nonintelligent The PK should not have embedded semantic meaning other than to uniquely identify each entity instance. An attribute with embedded semantic meaning is probably better used as a descriptive characteristic of the entity than as an identifier. For example, a student ID of 650973 would be preferred over Smith, Martha L. as a primary key identifier. No change over time If an attribute has semantic meaning, it might be subject to updates, which is why names do not make good primary keys. If Vickie Smith is the primary key, what happens if she changes her name when she gets married? If a primary key is subject to change, the foreign key values must be updated, thus adding to the database work load. Furthermore, changing a primary key value means that you are basically changing the identity of an entity. In short, the PK should be permanent and unchangeable. Preferably single-attribute A primary key should have the minimum number of attributes possible (irreducible). Single- attribute primary keys are desirable but not required. Single-attribute primary keys simplify the implementation of foreign keys. Having multiple-attribute primary keys can cause primary keys of related entities to grow through the possible addition of many attributes, thus adding to the database workload and making (application) coding more cumbersome. Preferably numeric Unique values can be better managed when they are numeric, because the database can use internal routines to implement a counter-style attribute that automatically increments values with the addition of each new row. In fact, most database systems include the ability to use special constructs, such as Autonumber in Microsoft Access, sequence in Oracle, or uniqueidentifier in MS SQL Server to support self-incrementing primary key attributes. Security-compliant The selected primary key must not be composed of any attribute(s) that might be considered a security risk or violation. For example, using a Social Security number as a PK in an EMPLOYEE table is not a good idea. • As identifiers of composite entities, in which each primary key combination is allowed only once in the M:N relationship • As identifiers of weak entities, in which the weak entity has a strong identifying rela- tionship with the parent entity To illustrate the first case, assume that you have a STUDENT entity set and a CLASS entity set. In addition, assume that those two sets are related in an M:N relationship via an ENROLL entity set, in which each student/class combination may appear only once in the composite entity. Figure 5.6 shows the ERD to represent such a relationship. As shown in Figure 5.6, the composite primary key automatically provides the benefit of ensuring that there cannot be duplicate values—that is, it ensures that the same stu- dent cannot enroll more than once in the same class. In the second case, a weak entity in a strong identifying relationship with a parent entity is normally used to represent one of two situations: 1. A real-world object that is existence-dependent on another real-world object. Such objects are distinguishable in the real world. A dependent and an employee are two separate people who exist independently of each other. However, such objects can exist in the model only when they relate to each other in a strong identifying rela- tionship. For example, the relationship between EMPLOYEE and DEPENDENT is one of existence dependency, in which the primary key of the dependent entity is a composite key that contains the key of the parent entity. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 180 Part 2 Design Concepts FIGURE 5.6 THE M:N RELATIONSHIP BETWEEN STUDENT AND CLASS Database name: Ch05_Tinycollege Table name: STUDENT Table name: CLASS (first four fields) Table name: ENROLL (first three fields) 2. A real-world object that is represented in the data model as two separate entities in a strong identifying relationship. For example, the real-world invoice object is repre- sented by two entities in a data model: INVOICE and LINE. Clearly, the LINE entity does not exist in the real world as an independent object, but as part of an INVOICE. In both situations, having a strong identifying relationship ensures that the dependent entity can exist only when it is related to the parent entity. In summary, the selection of a composite primary key for composite and weak entity types provides benefits that enhance the integrity and consistency of the model. 5-3d When To Use Surrogate Primary Keys In some instances a primary key doesn’t exist in the real world or the existing natural key might not be a suitable primary key. In these cases, it is standard practice to create a surrogate key. A surrogate key is a primary key created by the database designer to simplify the identification of entity instances. The surrogate key has no meaning in the user’s environment—it exists only to distinguish one entity instance from another (just like any other primary key). One practical advantage of a surrogate key is that because it has no intrinsic meaning, values for it can be generated by the DBMS to ensure that unique values are always provided. For example, consider the case of a park recreation facility that rents rooms for small parties. The manager of the facility keeps track of all events, using a folder with the for- mat shown in Table 5.4. Given the data shown in Table 5.4, you would model the EVENT entity as follows: EVENT (DATE, TIME_START, TIME_END, ROOM, EVENT_NAME, PARTY_OF) What primary key would you suggest? In this case, there is no simple natural key that could be used as a primary key in the model. Based on the primary key concepts you learned in previous chapters, you might suggest one of these options: rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. surrogate key A system-assigned primary key, generally numeric and auto- incremented. Copyright 2017 Cengage Lea Editorial review has deemed that Chapter 5 Advanced Data Modeling 181 TABLE 5.4 DATA USED TO KEEP TRACK OF EVENTS DATE TIME_START TIME_END ROOM EVENT_NAME PARTY_OF 6/17/2016 11:00a.m. 2:00p.m. Allure Burton Wedding 60 6/17/2016 11:00a.m. 2:00p.m. Bonanza Adams Office 12 6/17/2016 3:00p.m. 5:30p.m. Allure Smith Family 15 6/17/2016 3:30p.m. 5:30p.m. Bonanza Adams Office 12 6/18/2016 1:00p.m. 3:00p.m. Bonanza Boy Scouts 33 6/18/2016 11:00a.m. 2:00p.m. Allure March of Dimes 25 6/18/2016 11:00a.m. 12:30p.m. Bonanza Smith Family 12 (DATE, TIME_START, ROOM) or (DATE, TIME_END, ROOM) Assume that you select the composite primary key (DATE, TIME_START, ROOM) for the EVENT entity. Next, you determine that one EVENT may use many RESOURCEs (such as tables, projectors, PCs, and stands) and that the same RESOURCE may be used for many EVENTs. The RESOURCE entity would be represented by the following attributes: RESOURCE (RSC_ID, RSC_DESCRIPTION, RSC_TYPE, RSC_QTY, RSC_PRICE) Given the business rules, the M:N relationship between RESOURCE and EVENT would be represented via the EVNTRSC composite entity with a composite primary key as follows: EVNTRSC (DATE, TIME_START, ROOM, RSC_ID, QTY_USED) You now have a lengthy, four-attribute composite primary key. What would happen if the EVNTRSC entity’s primary key were inherited by another existence-dependent entity? At this point, you can see that the composite primary key could make the data- base implementation and program coding unnecessarily complex. As a data modeler, you probably noticed that the EVENT entity’s selected primary key might not fare well, given the primary key guidelines in Table 5.3. In this case, the EVENT entity’s selected primary key contains embedded semantic information and is formed by a combination of date, time, and text data columns. In addition, the selected primary key would cause lengthy primary keys for existence-dependent entities. The preferred alternative is to use a numeric, single-attribute surrogate primary key. Surrogate primary keys are accepted practice in today’s complex data environments. They are especially helpful when there is no natural key, when the selected candidate key has embedded semantic contents, or when the selected candidate key is too long or cum- bersome. However, there is a trade-off: if you use a surrogate key, you must ensure that the candidate key of the entity in question performs properly through the use of “unique index” and “not null” constraints. Note Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. This example shows a case in which entity integrity is maintained but semantic correctness of business rules is not. For example, you could have two events that overlap and whose primary keys are perfectly compliant. The only way to ensure adherence to this type of business rule (two events cannot overlap—occur on the same room at the same time) would be via application programming code. 182 Part 2 Design Concepts 5-4 Design Cases: Learning Flexible Database Design Data modeling and database design require skills that are acquired through experience. In turn, experience is acquired through practice—regular and frequent repetition, apply- ing the concepts learned to specific and different design problems. This section presents four special design cases that highlight the importance of flexible designs, proper identi- fication of primary keys, and placement of foreign keys. Note In describing the various modeling concepts throughout this book, the focus is on rela- tional models. Also, given the focus on the practical nature of database design, all design issues are addressed with the implementation goal in mind. Therefore, there is no sharp line of demarcation between design and implementation. At the pure conceptual stage of the design, foreign keys are not part of an ER diagram. The ERD displays only entities and relationships. Entity instances are distinguished by iden- tifiers that may become primary keys. During design, the modeler attempts to understand and define the entities and relationships. Foreign keys are the mechanism through which the relationship designed in an ERD is implemented in a relational model. 5-4a Design Case 1: Implementing 1:1 Relationships Foreign keys work with primary keys to properly implement relationships in the rela- tional model. The basic rule is very simple: put the primary key of the “one” side (the par- ent entity) on the “many” side (the dependent entity) as a foreign key. However, where do you place the foreign key when you are working with a 1:1 relationship? For exam- ple, take the case of a 1:1 relationship between EMPLOYEE and DEPARTMENT based on the business rule “one EMPLOYEE is the manager of one DEPARTMENT, and one DEPARTMENT is managed by one EMPLOYEE.” In that case, there are two options for selecting and placing the foreign key: 1. Place a foreign key in both entities. This option is derived from the basic rule you learned in Chapter 4. Place EMP_NUM as a foreign key in DEPARTMENT, and place DEPT_ID as a foreign key in EMPLOYEE. However, this solution is not recom- mended because it duplicates work, and it could conflict with other existing relation- ships. (Remember that DEPARTMENT and EMPLOYEE also participate in a 1:M relationship—one department employs many employees.) 2. Placeaforeignkeyinoneoftheentities.Inthatcase,theprimarykeyofoneofthetwo entities appears as a foreign key in the other entity. That is the preferred solution, but a question remains: which primary key should be used as a foreign key? The answer is found in Table 5.5, which shows the rationale for selecting the foreign key in a 1:1 relationship based on the relationship properties in the ERD. Figure 5.7 illustrates the “EMPLOYEE manages DEPARTMENT” relationship. Note that in this case, EMPLOYEE is mandatory to DEPARTMENT. Therefore, EMP_NUM is placed as the foreign key in DEPARTMENT. Alternatively, you might also argue that the “manager” role is played by the EMPLOYEE in the DEPARTMENT. As a designer, you must recognize that 1:1 relationships exist in the real world; there- fore, they should be supported in the data model. In fact, a 1:1 relationship is used to Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 5 Advanced Data Modeling 183 TABLE 5.5 SELECTION OF FOREIGN KEY IN A 1:1 RELATIONSHIP CASE ER RELATIONSHIP CONSTRAINTS ACTION I One side is mandatory and the other side is optional. Place the PK of the entity on the mandatory side in the entity on the optional side as a FK, and make the FK mandatory. II Both sides are optional. Select the FK that causes the fewest nulls, or place the FK in the entity in which the (relationship) role is played. III Both sides are mandatory. See Case II, or consider revising your model to ensure that the two entities do not belong together in a single entity. FIGURE5.7 THE1:1RELATIONSHIPBETWEENDEPARTMENTANDEMPLOYEE ensure that two entity sets are not placed in the same table. In other words, EMPLOYEE and DEPARTMENT are clearly separate and unique entity types that do not belong together in a single entity. If you grouped them together in one entity, what would you name that entity? 5-4b Design Case 2: Maintaining History of Time-Variant Data Company managers generally realize that good decision making is based on the infor- mation generated through the data stored in databases. Such data reflects both current and past events. Company managers use the data stored in databases to answer questions such as “How do the current company profits compare to those of previous years?” and “What are XYZ product’s sales trends?” In other words, the data stored in databases reflects not only current data, but historic data. Normally, data changes are managed by replacing the existing attribute value with the new value, without regard to the previous value. However, in some situations the history of values for a given attribute must be preserved. From a data-modeling point of view, time-variant data refer to data whose values change over time and for which you must keep a history of the data changes. You could argue that all data in a database is subject to change over time and is therefore time variant. However, some attribute values, such as your date of birth or your Social Security number, are not time variant. On the other hand, attributes such as your student GPA or your bank account balance are subject to change over time. Sometimes the data changes are externally originated and event driven, such as a product price change. On other occasions, changes are based on well-defined schedules, such as the daily stock quote “open” and “close” values. time-variant data Data whose values are a function of time. For example, time- variant data can be seen at work when a company’s history of all administrative appointments is tracked. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 184 Part 2 Design Concepts The storage of time-variant data requires changes in the data model; the type of change depends on the nature of the data. Some time-variant data is equivalent to having a mul- tivalued attribute in your entity. To model this type of time-variant data, you must create a new entity in a 1:M relationship with the original entity. This new entity will contain the new value, the date of the change, and any other attribute that is pertinent to the event being modeled. For example, if you want to track salary histories for each employee, then the EMP_SALARY attribute becomes multivalued, as shown in Figure 5.8. In this case, for each employee, there will be one or more records in the SALARY_HIST entity, which stores the salary amount and the date when the new salary goes into effect. Other time-variant data can turn a 1:M relationship into an M:N relationship. Assume that in addition to employee data, your data model includes data about the different departments in the organization and which employee manages each depart- ment. Assuming that each department is managed by only one employee and each employee can manage one department at most, then a 1:1 relationship would exist between EMPLOYEE and DEPARTMENT. This relationship would record the current manager of each department. However, if you want to keep track of the history of all department managers as well as the current manager, you can create the model shown in Figure 5.9. Note that in Figure 5.9, the MGR_HIST entity has a 1:M relationship with EMPLOYEE and a 1:M relationship with DEPARTMENT to reflect the fact that an employee could be the manager of many different departments over time, and a department could have many different employee managers. Because you are record- ing time-variant data, you must store the DATE_ASSIGN attribute in the MGR_HIST entity to provide the date that the employee (EMP_NUM) became the department manager. The primary key of MGR_HIST permits the same employee to be the man- ager of the same department, but on different dates. If that scenario is not the case in your environment—if, for example, an employee is the manager of a department only once—you could make DATE_ASSIGN a nonprime attribute in the MGR_HIST entity. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE5.8 MAINTAININGSALARYHISTORY Chapter 5 Advanced Data Modeling 185 FIGURE5.9 MAINTAININGMANAGERHISTORY Note in Figure 5.9 that the “manages” relationship is optional in theory and redun- dant in practice. At any time, you could identify the manager of a department by retrieving the most recent DATE_ASSIGN date from MGR_HIST for a given depart- ment. On the other hand, the ERD in Figure 5.9 differentiates between current data and historic data. The current manager relationship is implemented by the “manages” relationship between EMPLOYEE and DEPARTMENT. Additionally, the historic data is managed through EMP_MGR_HIST and DEPT_MGR_HIST. The trade-off with that model is that each time a new manager is assigned to a department, there will be two data modifications: one update in the DEPARTMENT entity and one insert in the MGR_HIST entity. The flexibility of the model proposed in Figure 5.9 becomes more apparent when you add the 1:M “one department employs many employees” relationship. In that case, the PK of the “1” side (DEPT_ID) appears in the “many” side (EMPLOYEE) as a foreign key. Now suppose you would like to keep track of the job history for each of the com- pany’s employees—you’d probably want to store the department, the job code, the date assigned, and the salary. To accomplish that task, you could modify the model in Figure 5.9 by adding a JOB_HIST entity. Figure 5.10 shows the use of the new JOB_HIST entity to maintain the employee’s history. Again, it is worth emphasizing that the “manages” and “employs” relationships are theoretically optional and redundant in practice. You can always find out where each employee works by looking at the job history and selecting only the most current data row for each employee. However, as you will discover in Chapter 7, Introduction to Structured Query Language (SQL), and in Chapter 8, Advanced SQL, finding where each employee works is not a trivial task. Therefore, the model represented in Figure 5.10 includes the admittedly redundant but unquestionably useful “manages” and “employs” relationships to separate current data from his- toric data. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 186 Part 2 Design Concepts FIGURE5.10 MAINTAININGJOBHISTORY 5-4c Design Case 3: Fan Traps Creating a data model requires proper identification of the data relationships among entities. However, due to miscommunication or incomplete understanding of the busi- ness rules or processes, it is not uncommon to misidentify relationships among entities. Under those circumstances, the ERD may contain a design trap. A design trap occurs when a relationship is improperly or incompletely identified and is therefore represented in a way that is not consistent with the real world. The most common design trap is known as a fan trap. A fan trap occurs when you have one entity in two 1:M relationships to other enti- ties, thus producing an association among the other entities that is not expressed in the model. For example, assume that the JCB basketball league has many divisions. Each division has many players, and each division has many teams. Given those “incomplete” business rules, you might create an ERD that looks like the one in Figure 5.11. As you can see in Figure 5.11, DIVISION is in a 1:M relationship with TEAM and in a 1:M relationship with PLAYER. Although that representation is semantically correct, the relationships are not properly identified. For example, there is no way to identify which players belong to which team. Figure 5.11 also shows a sample instance relationship rep- resentation for the ERD. Note that the relationship lines for the DIVISION instances fan out to the TEAM and PLAYER entity instances—thus the “fan trap” label. Figure 5.12 shows the correct ERD after the fan trap has been eliminated. Note that, in this case, DIVISION is in a 1:M relationship with TEAM. In turn, TEAM is in a 1:M relationship with PLAYER. Figure 5.12 also shows the instance relationship representa- tion after eliminating the fan trap. Given the design in Figure 5.12, note how easy it is to see which players play for which team. However, to find out which players play in which division, you first need to see what teams belong to each division; then you need to find out which players play on each team. In other words, there is a transitive relationship between DIVISION and PLAYER via the TEAM entity. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. design trap A problem that occurs when a relationship is improperly or incompletely identified and therefore is represented in a way that is not consistent with the real world. The most common design trap is known as a fan trap. fan trap A design trap that occurs when one entity is in two 1:M relationships with other entities, thus producing an association among the other entities that is not expressed in the model. Copyright 2017 Cengage Lea Editorial review has deemed that Chapter 5 Advanced Data Modeling 187 FIGURE5.11 INCORRECTERDWITHFANTRAPPROBLEM FIGURE5.12 CORRECTEDERDAFTERREMOVALOFTHEFANTRAP 5-4d Design Case 4: Redundant Relationships Although redundancy is often good to have in computer environments (multiple backups in multiple places, for example), redundancy is seldom good in the database environment. (As you learned in Chapter 3, The Relational Database Model, redun- dancies can cause data anomalies in a database.) Redundant relationships occur when there are multiple relationship paths between related entities. The main con- cern with redundant relationships is that they remain consistent across the model. However, it is important to note that some designs use redundant relationships as a way to simplify the design. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 188 Part 2 Design Concepts An example of redundant relationships was first introduced in Figure 5.9 during the discussion of maintaining a history of time-variant data. However, the use of the redun- dant “manages” and “employs” relationships was justified by the fact that such relation- ships dealt with current data rather than historic data. Another more specific example of a redundant relationship is represented in Figure 5.13. In Figure 5.13, note the transitive 1:M relationship between DIVISION and PLAYER through the TEAM entity set. Therefore, the relationship that connects DIVISION and PLAYER is redundant, for all practical purposes. In that case, the relationship could be safely deleted without losing any information-generation capabilities in the model. FIGURE5.13 AREDUNDANTRELATIONSHIP Summary • The extended entity relationship (EER) model adds semantics to the ER model via entity supertypes, subtypes, and clusters. An entity supertype is a generic entity type that is related to one or more entity subtypes. • A specialization hierarchy depicts the arrangement and relationships between entity supertypes and entity subtypes. Inheritance means that an entity subtype inherits the attributes and relationships of the supertype. Subtypes can be disjoint or overlapping. A subtype discriminator is used to determine to which entity subtype the supertype occurrence is related. The subtypes can exhibit partial or total completeness. There are basically two approaches to developing a specialization hierarchy of entity supertypes and subtypes: specialization and generalization. • An entity cluster is a “virtual” entity type used to represent multiple entities and rela- tionships in the ERD. An entity cluster is formed by combining multiple interrelated entities and relationships into a single, abstract entity object. • Natural keys are identifiers that exist in the real world. Natural keys sometimes make good primary keys, but not always. Primary keys must have unique values, they should be nonintelligent, they must not change over time, and they are preferably numeric and composed of a single attribute. • Composite keys are useful to represent M:N relationships and weak (strong identify- ing) entities. • Surrogate primary keys are useful when there is no natural key that makes a suitable primary key, when the primary key is a composite primary key with multiple data types, or when the primary key is too long to be usable. • In a 1:1 relationship, place the PK of the mandatory entity as a foreign key in the optional entity, as an FK in the entity that causes the fewest nulls, or as an FK where the role is played. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. • Time-variant data refers to data whose values change over time and require that you keep a history of data changes. To maintain the history of time-variant data, you must create an entity that contains the new value, the date of change, and any other time-relevant data. This entity maintains a 1:M relationship with the entity for which the history is to be maintained. • A fan trap occurs when you have one entity in two 1:M relationships to other entities, and there is an association among the other entities that is not expressed in the model. Redundant relationships occur when there are multiple relationship paths between related entities. The main concern with redundant relationships is that they remain consistent across the model. Chapter 5 Advanced Data Modeling 189 1. What is an entity supertype, and why is it used? 2. What kinds of data would you store in an entity subtype? 3. What is a specialization hierarchy? 4. What is a subtype discriminator? Give an example of its use. 5. What is an overlapping subtype? Give an example. 6. What is the difference between partial completeness and total completeness? For Questions 7–9, refer to Figure Q5.7. 7. List all of the attributes of a movie. 8. According to the data model, is it required that every entity instance in the PROD- UCT table be associated with an entity instance in the CD table? Why, or why not? 9. Is it possible for a book to appear in the BOOK table without appearing in the PRODUCT table? Why, or why not? 10. What is an entity cluster, and what advantages are derived from its use? 11. What primary key characteristics are considered desirable? Explain why each char- acteristic is considered desirable. 12. Under what circumstances are composite primary keys appropriate? 13. What is a surrogate primary key, and when would you use one? Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Key Terms completeness constraint design trap disjoint subtype EER diagram (EERD) entity cluster entity subtype entity supertype extended entity relationship model (EERM) fan trap generalization inheritance natural key (natural identifier) nonoverlapping subtype overlapping subtype partial completeness specialization specialization hierarchy subtype discriminator surrogate key time-variant data total completeness Online Content Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com. Review Questions 190 Part 2 Design Concepts FIGURE Q5.7 THE PRODUCT DATA MODEL 14. When implementing a 1:1 relationship, where should you place the foreign key if one side is mandatory and one side is optional? Should the foreign key be mandatory or optional? 15. Whatistime-variantdata,andhowwouldyoudealwithsuchdatafromadatabase design point of view? 16. What is the most common design trap, and how does it occur? Problems 1. Given the following business scenario, create a Crow’s Foot ERD using a special- ization hierarchy if appropriate. Two-Bit Drilling Company keeps information on employees and their insurance dependents. Each employee has an employee num- ber, name, date of hire, and title. If an employee is an inspector, then the date of cer- tification and certification renewal date should also be recorded in the system. For all employees, the Social Security number and dependent names should be kept. All dependents must be associated with one and only one employee. Some employees will not have dependents, while others will have many dependents. 2. Given the following business scenario, create a Crow’s Foot ERD using a special- ization hierarchy if appropriate. Tiny Hospital keeps information on patients and hospital rooms. The system assigns each patient a patient ID number. In addition, the patient’s name and date of birth are recorded. Some patients are resident patients who spend at least one night in the hospital, and others are outpatients who are treated and released. Resident patients are assigned to a room. Each room is iden- tified by a room number. The system also stores the room type (private or semipri- vate) and room fee. Over time, each room will have many patients. Each resident patient will stay in only one room. Every room must have had a patient, and every resident patient must have a room. 3. Given the following business scenario, create a Crow’s Foot ERD using a special- ization hierarchy if appropriate. Granite Sales Company keeps information on Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. employees and the departments in which they work. For each department, the department name, internal mail box number, and office phone extension are kept. A department can have many assigned employees, and each employee is assigned to only one department. Employees can be salaried, hourly, or work on contract. All employees are assigned an employee number, which is kept along with the employee’s name and address. For hourly employees, hourly wages and target weekly work hours are stored; for example, the company may target 40 hours/ week for some employees, 32 for others, and 20 for others. Some salaried employ- ees are salespeople who can earn a commission in addition to their base salary. For all salaried employees, the yearly salary amount is recorded in the system. For salespeople, their commission percentage on sales and commission percentage on profit are stored in the system. For example, John is a salesperson with a base sal- ary of $50,000 per year plus a 2 percent commission on the sales price for all sales he makes, plus another 5 percent of the profit on each of those sales. For contract employees, the beginning date and end date of their contracts are stored along with the billing rate for their hours. 4. In Chapter 4, you saw the creation of the Tiny College database design, which reflected such business rules as “a professor may advise many students” and “a professor may chair one department.” Modify the design shown in Figure 4.36 to include these business rules: • An employee could be staff, a professor, or an administrator. • A professor may also be an administrator. • Staff employees have a work-level classification, such as Level I or Level II. • Only professors can chair a department. A department is chaired by only one professor. • Only professors can serve as the dean of a college. Each of the university’s colleges is served by one dean. • A professor can teach many classes. • Administrators have a position title. Given that information, create the complete ERD that contains all primary keys, foreign keys, and main attributes. 5. Tiny College wants to keep track of the history of all its administrative appoint- ments, including dates of appointment and dates of termination. (Hint: Time-variant data is at work.) The Tiny College chancellor may want to know how many deans worked in the College of Business between January 1, 1960, and January 1, 2016, or who the dean of the College of Education was in 1990. Given that information, create the complete ERD that contains all primary keys, foreign keys, and main attributes. 6. Some Tiny College staff employees are information technology (IT) personnel. Some IT personnel provide technology support for academic programs, some pro- vide technology infrastructure support, and some provide support for both. IT per- sonnel are not professors; they are required to take periodic training to retain their technical expertise. Tiny College tracks all IT personnel training by date, type, and results (completed versus not completed). Given that information, create the com- plete ERD that contains all primary keys, foreign keys, and main attributes. 7. The FlyRight Aircraft Maintenance (FRAM) division of the FlyRight Company (FRC) performs all maintenance for FRC’s aircraft. Produce a data model segment that reflects the following business rules: Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 5 Advanced Data Modeling 191 192 Part 2 Design Concepts • All mechanics are FRC employees. Not all employees are mechanics. • Some mechanics are specialized in engine (EN) maintenance. Others are spe- cialized in airframe (AF) maintenance or avionics (AV) maintenance. (Avionics are the electronic components of an aircraft that are used in communication and navigation.) All mechanics take periodic refresher courses to stay current in their areas of expertise. FRC tracks all courses taken by each mechanic—date, course type, certification (Y/N), and performance. • FRC keeps an employment history of all mechanics. The history includes the date hired, date promoted, and date terminated. Given those requirements, create the Crow’s Foot ERD segment. Cases 8. “Martial Arts R Us” (MARU) needs a database. MARU is a martial arts school with hundreds of students. The database must keep track of all the classes that are offered, who is assigned to teach each class, and which students attend each class. Also, it is important to track the progress of each student as they advance. Create a complete Crow’s Foot ERD for these requirements: • Students are given a student number when they join the school. The number is stored along with their name, date of birth, and the date they joined the school. • All instructors are also students, but clearly not all students are instructors. In addition to the normal student information, for all instructors, the date that they start working as an instructor must be recorded along with their instructor status (compensated or volunteer). • An instructor may be assigned to teach any number of classes, but each class has one and only one assigned instructor. Some instructors, especially volunteer instructors, may not be assigned to any class. • A class is offered for a specific level at a specific time, day of the week, and location. For example, one class taught on Mondays at 5:00 p.m. in Room 1 is an intermediate-level class. Another class taught on Mondays at 6:00 p.m. in Room 1 is a beginner-level class. A third class taught on Tuesdays at 5:00 p.m. in Room 2 is an advanced-level class. • Students may attend any class of the appropriate level during each week, so there is no expectation that any particular student will attend any particular class ses- sion. Therefore, the attendance of students at each individual class meeting must be tracked. • A student will attend many different class meetings, and each class meeting is normally attended by many students. Some class meetings may not be attended by any students. New students may not have attended any class meetings yet. • At any given meeting of a class, instructors other than the assigned instruc- tor may show up to help. Therefore, a given class meeting may have a head instructor and many assistant instructors, but it will always have at least the one instructor who is assigned to that class. For each class meeting, the date of the class and the instructors’ roles (head instructor or assistant instructor) need to be recorded. For example, Mr. Jones is assigned to teach the Monday, 5:00 p.m., intermediate class in Room 1. During a particular meeting of that class, Mr. Jones was the head instructor and Ms. Chen served as an assistant instructor. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. • Each student holds a rank in the martial arts. The rank name, belt color, and rank requirements are stored. Most ranks have numerous rank requirements, but each requirement is associated with only one particular rank. All ranks except white belt have at least one requirement. • A given rank may be held by many students. While it is customary to think of a student as having a single rank, it is necessary to track each student’s progress through the ranks. Therefore, every rank that a student attains is kept in the sys- tem. New students joining the school are automatically given the rank of white belt. The date that a student is awarded each rank should be kept in the system. All ranks have at least one student who has achieved that rank at some time. 9. The Journal of E-commerce Research Knowledge is a prestigious information systems research journal. It uses a peer-review process to select manuscripts for publication. Only about 10 percent of the manuscripts submitted to the journal are accepted for publication. A new issue of the journal is published each quarter. Create a complete ERD to support the business needs described below. • Unsolicited manuscripts are submitted by authors. When a manuscript is received, the editor assigns it a number and records some basic information about it in the system, including the title of the manuscript, the date it was received, and a manu- script status of “received.” Information about the author(s) is also recorded, includ- ing each author’s name, mailing address, email address, and affiliation (the author’s school or company). Every manuscript must have an author. Only authors who have submitted manuscripts are kept in the system. It is typical for a manuscript to have several authors. A single author may have submitted many different manuscripts to the journal. Additionally, when a manuscript has multiple authors, it is important to record the order in which the authors are listed in the manuscript credits. • At his or her earliest convenience, the editor will briefly review the topic of the manuscript to ensure that its contents fall within the scope of the journal. If the content is not appropriate for the journal, the manuscript’s status is changed to “rejected,” and the author is notified via email. If the content is within the scope of the journal, then the editor selects three or more reviewers to review the manu- script. Reviewers work for other companies or universities and read manuscripts to ensure their scientific validity. For each reviewer, the system records a reviewer number, name, email address, affiliation, and areas of interest. Areas of interest are predefined areas of expertise that the reviewer has specified. An area of inter- est is identified by an IS code and includes a description (for example, IS2003 is the code for “database modeling”). A reviewer can have many areas of interest, and an area of interest can be associated with many reviewers. All reviewers must specify at least one area of interest. It is unusual, but possible, to have an area of interest for which the journal has no reviewers. The editor will change the status of the manuscript to “under review” and record which reviewers received the manuscript and the date it was sent to each reviewer. A reviewer will typically receive several manuscripts to review each year, although new reviewers may not have received any manuscripts yet. • The reviewers will read the manuscript at their earliest convenience and pro- vide feedback to the editor. The feedback from each reviewer includes rating the manuscript on a 10-point scale for appropriateness, clarity, methodology, and contribution to the field, as well as a recommendation for publication (accept or reject). The editor will record all of this information in the system for each review received, along with the date the feedback was received. Once all of the reviewers Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 5 Advanced Data Modeling 193 194 Part 2 Design Concepts have provided their evaluations, the editor will decide whether to publish the manuscript and change its status to “accepted” or “rejected.” If the manuscript will be published, the date of acceptance is recorded. • Once a manuscript has been accepted for publication, it must be scheduled. For each issue of the journal, the publication period (fall, winter, spring, or summer), publication year, volume, and number are recorded. An issue will contain many manuscripts, although the issue may be created in the system before it is known which manuscripts will be published in that issue. An accepted manuscript appears in only one issue of the journal. Each manuscript goes through a typesetting pro- cess that formats the content, including fonts, font size, line spacing, justification, and so on. Once the manuscript has been typeset, its number of pages is recorded in the system. The editor will then decide which issue each accepted manuscript will appear in and the order of manuscripts within each issue. The order and the beginning page number for each manuscript must be stored in the system. Once the manuscript has been scheduled for an issue, the status of the manuscript is changed to “scheduled.” Once an issue is published, the print date for the issue is recorded, and the status of each manuscript in that issue is changed to “published.” 10. Global Unified Technology Sales (GUTS) is moving toward a “bring your own device” (BYOD) model for employee computing. Employees can use traditional desktop computers in their offices. They can also use a variety of personal mobile computing devices such as tablets, smartphones, and laptops. The new computing model introduces some security risks that GUTS is attempting to address. The company wants to ensure that any devices connecting to their servers are properly registered and approved by the Information Technology department. Create a com- plete ERD to support the business needs described below: • Every employee works for a department that has a department code, name, mail box number, and phone number. The smallest department currently has 5 employees, and the largest department has 40 employees. This system will only track in which depart- ment an employee is currently employed. Very rarely, a new department can be created within the company. At such times, the department may exist temporarily without any employees. For every employee, an employee number and name (first, last, and middle initial) are recorded in the system. It is also necessary to keep each employee’s title. • An employee can have many devices registered in the system. Each device is assigned an identification number when it is registered. Most employees have at least one device, but newly hired employees might not have any devices registered initially. For each device, the brand and model need to be recorded. Only devices that are registered to an employee will be in the system. While unlikely, it is possible that a device could transfer from one employee to another. However, if that happens, only the employee who currently owns the device is tracked in the system. When a device is registered in the system, the date of that registration needs to be recorded. • Devices can be either desktop systems that reside in a company office or mobile devices. Desktop devices are typically provided by the company and are intended to be a permanent part of the company network. As such, each desktop device is assigned a static IP address, and the MAC address for the computer hardware is kept in the system. A desktop device is kept in a static location (building name and office number). This location should also be kept in the system so that, if the device becomes compromised, the IT department can dispatch someone to remediate the problem. • For mobile devices, it is important to also capture the device’s serial number, which operating system (OS) it is using, and the version of the OS. The IT depart- ment is also verifying that each mobile device has a screen lock enabled and has Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 5 Advanced Data Modeling 195 encryption enabled for data. The system should support storing information on whether or not each mobile device has these capabilities enabled. • Once a device is registered in the system, and the appropriate capabilities are enabled if it is a mobile device, the device may be approved for connections to one or more servers. Not all devices meet the requirements to be approved at first so the device might be in the system for a period of time before it is approved to connect to any server. GUTS has a number of servers, and a device must be approved for each server individually. Therefore, it is possible for a single device to be approved for several servers but not for all servers. • Each server has a name, brand, and IP address. Within the IT department’s facilities are a number of climate-controlled server rooms where the physical servers can be located. Which room each server is in should also be recorded. Further, it is necessary to track which operating system is being used on each server. Some servers are virtual servers and some are physical servers. If a server is a virtual server, then the system should track which physical server it is running on. A single physical server can host many virtual servers, but each virtual server is hosted on only one physical server. Only physical servers can host a virtual server. In other words, one virtual server can- not host another virtual server. Not all physical servers host a virtual server. • A server will normally have many devices that are approved to access the server, but it is possible for new servers to be created that do not yet have any approved devices. When a device is approved for connection to a server, the date of that approval should be recorded. It is also possible for a device that was approved for a server to lose its approval. If that happens, the date that the approval was removed should be recorded. If a device loses its approval, it may regain that approval at a later date if whatever circumstance that lead to the removal is resolved. • A server can provide many user services, such as email, chat, homework manag- ers, and others. Each service on a server has a unique identification number and name. The date that GUTS began offering that service should be recorded. Each service runs on only one server although new servers might not offer any services initially. Client-side services are not tracked in this system so every service must be associated with a server. • Employees must get permission to access a service before they can use it. Most employees have permissions to use a wide array of services, but new employees might not have permission on any service. Each service can support multiple approved employees as users, but new services might not have any approved users at first. The date on which the employee is approved to use a service is tracked by the system. The first time an employee is approved to access a service, the employee must create a username and password. This will be the same username and password that the employee will use for every service for which the employee is eventually approved. 11. Global Computer Solutions (GCS) is an information technology consulting company with many offices throughout the United States. The company’s success is based on its ability to maximize its resources—that is, its ability to match highly skilled employees with projects according to region. To better manage its projects, GCS has contacted you to design a database so GCS managers can keep track of their customers, employees, projects, project schedules, assignments, and invoices. The GCS database must support all of GCS’s operations and information require- ments. A basic description of the main entities follows: • The employees of GCS must have an employee ID, a last name, a middle initial, a first name, a region, and a date of hire recorded in the system. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 196 Part 2 Design Concepts • Valid regions are as follows: Northwest (NW), Southwest (SW), Midwest North (MN), Midwest South (MS), Northeast (NE), and Southeast (SE). • Each employee has many skills, and many employees have the same skill. • Each skill has a skill ID, description, and rate of pay. Valid skills are as follows: Data Entry I, Data Entry II, Systems Analyst I, Systems Analyst II, Database Designer I, Database Designer II, Cobol I, Cobol II, C++ I, C++ II, VB I, VB II, ColdFusion I, ColdFusion II, ASP I, ASP II, Oracle DBA, MS SQL Server DBA, Network Engineer I, Network Engineer II, Web Administrator, Technical Writer, and Project Manager. Table P5.11a shows an example of the Skills Inventory. TABLE P5.11A SKILL EMPLOYEE Data Entry I Seaton Amy; Williams Josh; Underwood Trish Data Entry II Williams Josh; Seaton Amy Systems Analyst I Craig Brett; Sewell Beth; Robbins Erin; Bush Emily; Zebras Steve Systems Analyst II Chandler Joseph; Burklow Shane; Robbins Erin DB Designer I Yarbrough Peter; Smith Mary DB Designer II Yarbrough Peter; Pascoe Jonathan Cobol I Kattan Chris; Ephanor Victor; Summers Anna; Ellis Maria Cobol II Kattan Chris; Ephanor Victor; Batts Melissa C++ I Smith Jose; Rogers Adam; Cope Leslie C++ II Rogers Adam; Bible Hanah VB I Zebras Steve; Ellis Maria VB II Zebras Steve; Newton Christopher ColdFusion I Duarte Miriam; Bush Emily ColdFusion II Bush Emily; Newton Christopher ASP I Duarte Miriam; Bush Emily ASP II Duarte Miriam; Newton Christopher Oracle DBA Smith Jose; Pascoe Jonathan SQL Server DBA Yarbrough Peter; Smith Jose Network Engineer I Bush Emily; Smith Mary Network Engineer II Bush Emily; Smith Mary Web Administrator Bush Emily; Smith Mary; Newton Christopher Technical Writer Kilby Surgena; Bender Larry Project Manager Paine Brad; Mudd Roger; Kenyon Tiffany; Connor Sean • GCS has many customers. Each customer has a customer ID, name, phone number, and region. • GCS works by projects. A project is based on a contract between the cus- tomer and GCS to design, develop, and implement a computerized solution. Each project has specific characteristics such as the project ID, the customer to which the project belongs, a brief description, a project date (the date the contract was signed), an estimated project start date and end date, an esti- mated project budget, an actual start date, an actual end date, an actual cost, and one employee assigned as the manager of the project. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. • The actual cost of the project is updated each Friday by adding that week’s cost to the actual cost. The week’s cost is computed by multiplying the hours each employee worked by the rate of pay for that skill. • The employee who is the manager of the project must complete a project schedule, which effectively is a design and development plan. In the project schedule (or plan), the manager must determine the tasks that will be performed to take the project from beginning to end. Each task has a task ID, a brief task description, starting and ending dates, the types of skills needed, and the number of employees (with the required skills) needed to complete the task. General tasks are the initial interview, database and system design, implementation, coding, testing, and final evaluation and sign-off. For example, GCS might have the project schedule shown in Table P5.11b. Chapter 5 Advanced Data Modeling 197 TABLE P5.11B PROJECT ID: 1 DESCRIPTION: SALES MANAGEMENT SYSTEM COMPANY: SEE ROCKS CONTRACT DATE: 2/12/2016 REGION: NW START DATE: 3/1/2016 END DATE: 7/1/2016 BUDGET: $15,500 START DATE END DATE TASK DESCRIPTION SKILL(S) REQUIRED QUANTITY REQUIRED 3/1/16 3/6/16 Initial interview Project Manager Systems Analyst II DB Designer I 1 1 1 3/11/16 3/15/16 Database design DB Designer I 1 3/11/16 4/12/16 System design Systems Analyst II Systems Analyst I 1 2 3/18/16 3/22/16 Database implementation Oracle DBA 1 3/25/16 5/20/16 System coding and testing Cobol I Cobol II Oracle DBA 2 1 1 3/25/16 6/7/16 System documentation Technical Writer 1 6/10/16 6/14/16 Final evaluation Project Manager Systems Analyst II DB Designer I Cobol II 1 1 1 1 6/17/16 6/21/16 On-site system online and data loading Project Manager Systems Analyst II DB Designer I Cobol II 1 1 1 1 7/1/16 7/1/16 Sign-off Project Manager 1 • GCS pools all of its employees by region; from this pool, employees are assigned to a specific task scheduled by the project manager. For example, in the first project’s schedule, you know that a Systems Analyst II, Database Designer I, and Project Manager are needed for the period from 3/1/16 to 3/6/16. The project manager is assigned when the project is created and remains for the duration of the project. Using that information, GCS searches the employees who are located in the same region as the customer, matches the skills required, and assigns the employees to the project task. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 198 Part 2 Design Concepts • Each project schedule task can have many employees assigned to it, and a given employee can work on multiple project tasks. However, an employee can work on only one project task at a time. For example, if an employee is already assigned to work on a project task from 2/20/16 to 3/3/16, the employee cannot work on another task until the current assignment is closed (ends). The date that an assign- ment is closed does not necessarily match the ending date of the project schedule task because a task can be completed ahead of or behind schedule. • Given all of the preceding information, you can see that the assignment associates an employee with a project task, using the project schedule. Therefore, to keep track of the assignment, you require at least the following information: assign- ment ID, employee, project schedule task, assignment start date, and assignment end date. The end date could be any date, as some projects run ahead of or behind schedule. Table P5.11c shows a sample assignment form. TABLE P5.11C PROJECT ID: 1 DESCRIPTION: SALES MANAGEMENT SYSTEM COMPANY: SEE ROCKS CONTRACT DATE: 2/12/2016 AS OF: 03/29/16 SCHEDULED ACTUAL ASSIGNMENTS PROJECT TASK START DATE END DATE SKILL EMPLOYEE START DATE END DATE Initial interview 3/1/16 3/6/16 Project Mgr. Sys. Analyst II DB Designer I 101-Connor S. 102-Burklow S. 103-Smith M. 3/1/16 3/1/16 3/1/16 3/6/16 3/6/16 3/6/16 Database design 3/11/16 3/15/16 DB Designer I 104-Smith M. 3/11/16 3/14/16 System design 3/11/16 4/12/16 Sys. Analyst II Sys. Analyst I Sys. Analyst I 105-Burklow S. 106-Bush E. 107-Zebras S. 3/11/16 3/11/16 3/11/16 Database implementation 3/18/16 3/22/16 Oracle DBA 108-Smith J. 3/15/16 3/19/16 System coding and testing 3/25/16 5/20/16 Cobol I Cobol I Cobol II Oracle DBA 109-Summers A. 110-Ellis M. 111-Ephanor V. 112-Smith J. 3/21/16 3/21/16 3/21/16 3/21/16 System documentation 3/25/16 6/7/16 Tech. Writer 113-Kilby S. 3/25/16 Final evaluation 6/10/16 6/14/16 Project Mgr. Sys. Analyst II DB Designer I Cobol II On-site system online and data loading 6/17/16 6/21/16 Project Mgr. Sys. Analyst II DB Designer I Cobol II Sign-off 7/1/16 7/1/16 Project Mgr. (Note: The assignment number is shown as a prefix of the employee name—for example, 101 or 102.) Assume that the assignments shown previously are the only ones as of the date of this design. The assignment number can be any number that matches your database design. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. • Employee work hours are kept in a work log, which contains a record of the actual hours worked by employees on a given assignment. The work log is a form that the employee fills out at the end of each week (Friday) or at the end of each month. The form contains the date, which is either the current Friday of the month or the last workday of the month if it does not fall on a Friday. The form also contains the assignment ID, the total hours worked either that week or up to the end of the month, and the bill number to which the work-log entry is charged. Obviously, each work-log entry can be related to only one bill. A sample list of the current work-log entries for the first sample project is shown in Table P5.11d. Chapter 5 Advanced Data Modeling 199 TABLE P5.11D EMPLOYEE NAME Burklow S. Connor S. Smith M. Burklow S. Connor S. Smith M. Burklow S. Bush E. Smith J. Smith M. Zebras S. Burklow S. Bush E. Ellis M. Ephanor V. Smith J. Smith J. Summers A. Zebras S. Burklow S. Bush E. Ellis M. Ephanor V. Kilby S. Smith J. Summers A. Zebras S. WEEK ENDING 3/1/16 3/1/16 3/1/16 3/8/16 3/8/16 3/8/16 3/15/16 3/15/16 3/15/16 3/15/16 3/15/16 3/22/16 3/22/16 3/22/16 3/22/16 3/22/16 3/22/16 3/22/16 3/22/16 3/29/16 3/29/16 3/29/16 3/29/16 3/29/16 3/29/16 3/29/16 3/29/16 ASSIGNMENT NUMBER 1-102 1-101 1-103 1-102 1-101 1-103 1-105 1-106 1-108 1-104 1-107 1-105 1-106 1-110 1-111 1-108 1-112 1-109 1-107 1-105 1-106 1-110 1-111 1-113 1-112 1-109 1-107 HOURS WORKED 4 4 4 24 24 24 40 40 6 32 35 40 40 12 12 12 12 12 35 40 40 35 35 40 35 35 35 BILL NUMBER xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx Note: xxx represents the bill ID. Use the one that matches the bill number in your database. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 200 Part 2 Design Concepts • Finally, every 15 days, a bill is written and sent to the customer for the total hours worked on the project during that period. When GCS generates a bill, it uses the bill number to update the work-log entries that are part of the bill. In summary, a bill can refer to many work-log entries, and each work- log entry can be related to only one bill. GCS sent one bill on 3/15/16 for the first project (SEE ROCKS), totaling the hours worked between 3/1/16 and 3/15/16. Therefore, you can safely assume that there is only one bill in this table and that the bill covers the work-log entries shown in the preceding form. Your assignment is to create a database that fulfills the operations described in this problem. The minimum required entities are employee, skill, customer, region, project, project schedule, assignment, work log, and bill. (There are additional required entities that are not listed.) • Create all of the required tables and required relationships. • Create the required indexes to maintain entity integrity when using surrogate primary keys. • Populate the tables as needed, as indicated in the sample data and forms. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables In this chapter, you will learn: • What normalization is and what role it plays in the database design process • About the normal forms 1NF, 2NF, 3NF, BCNF, and 4NF • How normal forms can be transformed from lower normal forms to higher normal forms • That normalization and ER modeling are used concurrently to produce a good database design • That some situations require denormalization to generate information efficiently Preview Good database design must be matched to good table structures. In this chapter, you will learn to evaluate and design good table structures to control data redundancies, thereby avoiding data anomalies. The process that yields such desirable results is known as normalization. To recognize and appreciate the characteristics of a good table structure, it is useful to examine a poor one. Therefore, the chapter begins by examining the characteristics of a poor table structure and the problems it creates. You then learn how to correct the table structure. This methodology will yield important dividends: you will know how to design a good table structure and how to repair a poor one. You will discover not only that data anomalies can be eliminated through normaliza- tion, but that a properly normalized set of table structures is actually less complicated to use than an unnormalized set. In addition, you will learn that the normalized set of table structures more faithfully reflects an organization’s real operations. Data Files and Available Formats CH06_ConstructCo P P P P CH06_Service P P P P CH06_Eval P P P P Data Files Available on cengagebrain.com MS Access Oracle MS SQL My SQL MS Access Oracle MS SQL My SQL Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 202 Part 2 Design Concepts 6-1 Database Tables and Normalization Having good relational database software is not enough to avoid the data redundancy discussed in Chapter 1, Database Systems. If the database tables are treated as though they are files in a file system, the relational database management system (RDBMS) never has a chance to demonstrate its superior data-handling capabilities. The table is the basic building block of database design. Consequently, the table’s structure is of great interest. Ideally, the database design process explored in Chapter 4, Entity Relationship (ER) Modeling, yields good table structures. Yet, it is possible to create poor table structures even in a good database design. How do you recognize a poor table structure, and how do you produce a good table? The answer to both ques- tions involves normalization. Normalization is a process for evaluating and correcting table structures to minimize data redundancies, thereby reducing the likelihood of data anomalies. The normalization process involves assigning attributes to tables based on the concept of determination you learned in Chapter 3, The Relational Database Model. Normalization works through a series of stages called normal forms. The first three stages are described as first normal form (1NF), second normal form (2NF), and third normal form (3NF). From a structural point of view, 2NF is better than 1NF, and 3NF is better than 2NF. For most purposes in business database design, 3NF is as high as you need to go in the normalization process. However, you will discover that properly designed 3NF structures also meet the requirements of fourth normal form (4NF). Although normalization is a very important ingredient in database design, you should not assume that the highest level of normalization is always the most desirable. Gener- ally, the higher the normal form, the more relational join operations you need to produce a specified output. Also, more resources are required by the database system to respond to end-user queries. A successful design must also consider end-user demand for fast performance. Therefore, you will occasionally need to denormalize some portions of a database design to meet performance requirements. Denormalization produces a lower normal form; that is, a 3NF will be converted to a 2NF through denormalization. How- ever, the price you pay for increased performance through denormalization is greater data redundancy. normalization A process that assigns attributes to entities so that data redundancies are reduced or eliminated. denormalization A process by which a table is changed from a higher-level normal form to a lower-level normal form, usually to increase processing speed. Denormalization potentially yields data anomalies. prime attribute A key attribute; that is, an attribute that is part of a key or is the whole key. See also key attributes. key attributes The attributes that form a primary key. See also prime attribute. nonprime attribute An attribute that is not part of a key. nonkey attribute See nonprime attribute. Note Although the word table is used throughout this chapter, formally, normalization is con- cerned with relations. In Chapter 3 you learned that the terms table and relation are fre- quently used interchangeably. In fact, you can say that a table is the implementation view of a logical relation that meets some specific conditions. (See Table 3.1.) However, being more rigorous, the mathematical relation does not allow duplicate tuples; whereas they could exist in tables (see Section 6-5). Also, in normalization terminology, any attribute that is at least part of a key is known as a prime attribute instead of the more common term key attribute, which was introduced earlier. Conversely, a nonprime attribute, or a nonkey attribute, is not part of any candidate key. 6-2 The Need For Normalization Normalization is typically used in conjunction with the entity relationship modeling that you learned in the previous chapters. Database designers commonly use normaliza- tion in two situations. When designing a new database structure based on the business requirements of the end users, the database designer will construct a data model using a technique such as Crow’s Foot notation ERDs. After the initial design is complete, Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. the designer can use normalization to analyze the relationships among the attributes within each entity and determine if the structure can be improved through normaliza- tion. Alternatively, database designers are often asked to modify existing data structures that can be in the form of flat files, spreadsheets, or older database structures. Again, by analyzing relationships among the attributes or fields in the data structure, the database designer can use the normalization process to improve the existing data structure and create an appropriate database design. Whether you are designing a new database struc- ture or modifying an existing one, the normalization process is the same. To get a better idea of the normalization process, consider the simplified database activities of a construction company that manages several building projects. Each project has its own project number, name, assigned employees, and so on. Each employee has an employee number, name, and job classification, such as engineer or computer technician. The company charges its clients by billing the hours spent on each contract. The hourly billing rate is dependent on the employee’s position. For example, one hour of computer technician time is billed at a different rate than one hour of engineer time. Periodically, a report is generated that contains the information displayed in Table 6.1. The total charge in Table 6.1 is a derived attribute and is not stored in the table at this point. The easiest short-term way to generate the required report might seem to be a table whose contents correspond to the reporting requirements. (See Figure 6.1.) Note that the data in Figure 6.1 reflects the assignment of employees to projects. Apparently, an employee can be assigned to more than one project. For example, Dar- lene Smithson (EMP_NUM = 112) has been assigned to two projects: Amber Wave and Starflight. Given the structure of the dataset, each project includes only a single occur- rence of any one employee. Therefore, knowing the PROJ_NUM and EMP_NUM values will let you find the job classification and its hourly charge. In addition, you will know the total number of hours each employee worked on each project. (The total charge—a derived attribute whose value can be computed by multiplying the hours billed and the charge per hour—has not been included in Figure 6.1. No structural harm is done if this derived attribute is included.) Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 203 FIGURE 6.1 TABULAR REPRESENTATION OF THE REPORT FORMAT Table name: RPT_FORMAT Database name: Ch06_ConstructCo 204 Part 2 Design Concepts Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. TABLE 6.1 A SAMPLE REPORT LAYOUT PROJECT PROJECT EMPLOYEE NUMBER NAME NUMBER EMPLOYEE NAME JOB CLASS CHARGE/ HOURS TOTAL HOUR BILLED CHARGE 15 Evergreen 103 18 Amber Wave 114 June E. Arbough 101 John G. News Elec. Engineer Database Designer Database Designer Programmer Systems Analyst Subtotal Applications Designer General Support Systems Analyst $ 84.50 23.8 $105.00 19.4 $105.00 35.7 $ 35.75 12.6 $ 96.75 23.8 $ 2,011.10 $ 2,037.00 $ 3,748.50 $ 450.45 $ 2,302.65 $10,549.70 $ 1,183.26 $ 831.71 $ 3,134.70 $ 2,021.80 105 Alice K. Johnson * 106 William Smithfield 102 David H. Senior Annelise Jones 118 James J. Frommer $ 48.10 24.6 $ 18.36 45.3 $ 96.75 32.4 $ 45.95 44.0 104 Anne K. Ramoras * 112 Darlene M. Smithson DSS Analyst 22 Rolling Tide 105 104 Anne K. Ramoras Database Designer Systems Analyst Applications Designer Clerical Support Programmer Subtotal Programmer $105.00 64.7 $96.75 48.4 $48.10 23.6 $26.87 22.0 $35.75 12.8 $ 6,793.50 $ 4,682.70 $ 1,135.16 $ 591.14 $ 457.60 $13,660.10 $ 879.45 $ 4,431.15 $ 5,911.50 $ 1,592.11 $ 2,283.30 $ 559.98 $ 1,902.33 $17,559.82 $48,941.09 Note: A * indicates the project leader. Alice K. Johnson 113 Delbert K. Joenbrood * 111 Geoff B. Wabash 106 William Smithfield 25 Starflight 107 115 Travis B. Bawangi $ 35.75 24.6 $ 96.75 45.8 $105.00 56.3 $ 48.10 33.1 $ 96.75 23.6 $ 18.36 30.5 $ 45.95 41.4 Maria D. Alonzo 101 John G. News * 114 Annelise Jones 108 Ralph B. Washington 118 James J. Frommer 112 Darlene M. Smithson Systems Analyst Database Designer Applications Designer Systems Analyst General Support Subtotal $ 7,171.47 DSS Analyst Subtotal Total Unfortunately, the structure of the dataset in Figure 6.1 does not conform to the requirements discussed in Chapter 3, nor does it handle data very well. Consider the following deficiencies: 1. Theprojectnumber(PROJ_NUM)isapparentlyintendedtobeaprimarykey(PK)or at least a part of a PK, but it contains nulls. Given the preceding discussion, you know that PROJ_NUM + EMP_NUM will define each row. 2. The table entries invite data inconsistencies. For example, the JOB_CLASS value “Elect. Engineer” might be entered as “Elect.Eng.” in some cases, “El. Eng.” in others, and “EE” in still others. 3. The table displays data redundancies that yield the following anomalies: a. Update anomalies. Modifying the JOB_CLASS for employee number 105 requires many potential alterations, one for each EMP_NUM = 105. b. Insertion anomalies. Just to complete a row definition, a new employee must be assigned to a project. If the employee is not yet assigned, a phantom project must be created to complete the employee data entry. c. Deletion anomalies. Suppose that only one employee is associated with a given project. If that employee leaves the company and the employee data is deleted, the project information will also be deleted. To prevent the loss of the project informa- tion, a fictitious employee must be created. In spite of those structural deficiencies, the table structure appears to work; the report is generated with ease. Unfortunately, the report might yield varying results depending on what data anomaly has occurred. For example, if you want to print a report to show the total “hours worked” value by the job classification “Database Designer,” that report will not include data for “DB Design” and “Database Design” data entries. Such reporting anomalies cause a multitude of problems for managers—and cannot be fixed through application programming. Even if careful data-entry auditing can eliminate most of the reporting problems (at a high cost), it is easy to demonstrate that even a simple data entry becomes inefficient. Given the existence of update anomalies, suppose Darlene M. Smithson is assigned to work on the Evergreen project. The data-entry clerk must update the PROJECT file with the following entry: 15 Evergreen 112 Darlene M. Smithson DSS Analyst $45.95 0.0 to match the attributes PROJ_NUM, PROJ_NAME, EMP_NUM, EMP_NAME, JOB_ CLASS, CHG_HOUR, and HOURS. (If Smithson has just been assigned to the project, the total number of hours worked is 0.0.) Note Each time another employee is assigned to a project, some data entries (such as PROJ_NAME, EMP_NAME, and CHG_HOUR) are unnecessarily repeated. Imagine Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 205 Remember that the naming convention makes it easy to see what each attribute stands for and its likely origin. For example, PROJ_NAME uses the prefix PROJ to indicate that the attri- bute is associated with the PROJECT table, while the NAME component is self-documenting as well. However, keep in mind that name length is also an issue, especially in the prefix des- ignation. For that reason, the prefix CHG was used rather than CHARGE. (Given the database’s context, it is not likely that the prefix will be misunderstood.) 206 Part 2 Design Concepts the data-entry chore when 200 or 300 table entries must be made! The entry of the employee number should be sufficient to identify Darlene M. Smithson, her job descrip- tion, and her hourly charge. Because only one person is identified by the number 112, that person’s characteristics (name, job classification, and so on) should not have to be entered each time the main file is updated. Unfortunately, the structure displayed in Figure 6.1 does not make allowances for that possibility. The data redundancy evident in Figure 6.1 leads to wasted data storage space. Even worse, data redundancy produces data anomalies. For example, suppose the data-entry clerk had entered the data as: 15 Evergeen 112 Darla Smithson DCS Analyst $45.95 0.0 At first glance, the data entry appears to be correct. But is Evergeen the same project as Evergreen? And is DCS Analyst supposed to be DSS Analyst? Is Darla Smithson the same person as Darlene M. Smithson? Such confusion is a data integrity problem because the data entry failed to conform to the rule that all copies of redundant data must be identical. The possibility of introducing data integrity problems caused by data redundancy must be considered during database design. The relational database environment is especially well suited to help the designer overcome those problems. 6-3 The Normalization Process In this section, you will learn how to use normalization to produce a set of normalized tables to store the data that will be used to generate the required information. The objec- tive of normalization is to ensure that each table conforms to the concept of well-formed relations—in other words, tables that have the following characteristics: • Each table represents a single subject. For example, a COURSE table will contain only data that directly pertain to courses. Similarly, a STUDENT table will contain only student data. • No data item will be unnecessarily stored in more than one table (in short, tables have minimum controlled redundancy). The reason for this requirement is to ensure that the data is updated in only one place. • All nonprime attributes in a table are dependent on the primary key—the entire primary key and nothing but the primary key. The reason for this requirement is to ensure that the data is uniquely identifiable by a primary key value. • Each table is void of insertion, update, or deletion anomalies, which ensures the integ- rity and consistency of the data. To accomplish the objective, the normalization process takes you through the steps that lead to successively higher normal forms. The most common normal forms and their basic characteristic are listed in Table 6.2. You will learn the details of these normal forms in the indicated sections. The concept of keys is central to the discussion of normalization. Recall from Chap- ter 3 that a candidate key is a minimal (irreducible) superkey. The primary key is the candidate key selected to be the primary means used to identify the rows in the table. Although normalization is typically presented from the perspective of candidate keys, this initial discussion assumes for the sake of simplicity that each table has only one can- didate key; therefore, that candidate key is the primary key. From the data modeler’s point of view, the objective of normalization is to ensure that all tables are at least in third normal form (3NF). Even higher-level normal forms exist. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 207 TABLE 6.2 NORMAL FORMS NORMAL FORM CHARACTERISTIC SECTION First normal form (1NF) Table format, no repeating groups, and PK identified 6-3a Second normal form (2NF) 1NF and no partial dependencies 6-3b Third normal form (3NF) 2NF and no transitive dependencies 6-3c Boyce-Codd normal form (BCNF) Every determinant is a candidate key (special case of 3NF) 6-6a Fourth normal form (4NF) 3NF and no independent multivalued dependencies 6-6b However, normal forms such as the fifth normal form (5NF) and domain-key normal form (DKNF) are not likely to be encountered in a business environment and are mainly of theoretical interest. Such higher normal forms usually increase joins, which slows performance without adding any value in the elimination of data redundancy. Some very specialized applications, such as statistical research, might require normalization beyond the 4NF, but those applications fall outside the scope of most business opera- tions. Because this book focuses on practical applications of database techniques, the higher-level normal forms are not covered. FunctionalDependence Beforeoutliningthenormalizationprocess,itisagoodidea to review the concepts of determination and functional dependence that were covered in detail in Chapter 3. Table 6.3 summarizes the main concepts. TABLE 6.3 FUNCTIONAL DEPENDENCE CONCEPTS CONCEPT DEFINITION Functional dependence The attribute B is fully functionally dependent on the attribute A if each value of A determines one and only one value of B. Example: PROJ_NUM → PROJ_NAME (read as PROJ_NUM functionally determines PROJ_NAME) In this case, the attribute PROJ_NUM is known as the determinant attribute, and the attribute PROJ_NAME is known as the dependent attribute. Functional dependence (generalized definition) Attribute A determines attribute B (that is, B is functionally dependent on A) if all (generalized definition) of the rows in the table that agree in value for attribute A also agree in value for attribute B. Fully functional dependence (composite key) If attribute B is functionally dependent on a composite key A but not on any subset of that composite key, the attribute B is fully functionally dependent on A. It is crucial to understand these concepts because they are used to derive the set of functional dependencies for a given relation. The normalization process works one relation at a time, identifying the dependencies on that relation and normalizing the relation. As you will see in the following sections, normalization starts by identifying the dependencies of a given relation and progressively breaking up the relation (table) into a set of new relations (tables) based on the identified dependencies. Two types of functional dependencies that are of special interest in normalization are partial dependencies and transitive dependencies. A partial dependency exists when there is a functional dependence in which the determinant is only part of the primary key (remember the assumption that there is only one candidate key). For example, if (A, B) → (C, D), B → C, and (A, B) is the primary key, then the functional partial dependency A condition in which an attribute is dependent on only a portion (subset) of the primary key. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 208 Part 2 Design Concepts dependence B → C is a partial dependency because only part of the primary key (B) is needed to determine the value of C. Partial dependencies tend to be straightforward and easy to identify. A transitive dependency exists when there are functional dependencies such that X → Y, Y → Z, and X is the primary key. In that case, the dependency X → Z is a transi- tive dependency because X determines the value of Z via Y. Unlike partial dependencies, transitive dependencies are more difficult to identify among a set of data. Fortunately, there is an effective way to identify transitive dependencies: they occur only when a functional dependence exists among nonprime attributes. In the previous example, the actual transitive dependency is X → Z. However, the dependency Y → Z signals that a transitive dependency exists. Hence, throughout the discussion of the normalization process, the existence of a functional dependence among nonprime attributes will be considered a sign of a transitive dependency. To address the problems related to tran- sitive dependencies, changes to the table structure are made based on the functional dependence that signals the transitive dependency’s existence. Therefore, to simplify the description of normalization, from this point forward the signaling dependency will be called the transitive dependency. 6-3a Conversion To First Normal Form Because the relational model views data as part of a table or a collection of tables in which all key values must be identified, the data depicted in Figure 6.1 might not be stored as shown. Note that Figure 6.1 contains what is known as repeating groups. A repeating group derives its name from the fact that a group of multiple entries of the same type can exist for any single key attribute occurrence. In Figure 6.1, note that each single project number (PROJ_NUM) occurrence can reference a group of related data entries. For example, the Evergreen project (PROJ_NUM = 15) shows five entries at this point—and those entries are related because they each share the PROJ_NUM = 15 char- acteristic. Each time a new record is entered for the Evergreen project, the number of entries in the group grows by one. A relational table must not contain repeating groups. The existence of repeating groups provides evidence that the RPT_FORMAT table in Figure 6.1 fails to meet even the lowest normal form requirements, thus reflecting data redundancies. Normalizing the table structure will reduce the data redundancies. If repeating groups do exist, they must be eliminated by making sure that each row defines a single entity. In addition, the dependencies must be identified to diagnose the normal form. Identi- fication of the normal form lets you know where you are in the normalization process. Normalization starts with a simple three-step procedure. Step 1: Eliminate the Repeating Groups Start by presenting the data in a tabular format, where each cell has a single value and there are no repeating groups. To elimi- nate the repeating groups, eliminate the nulls by making sure that each repeating group attribute contains an appropriate data value. That change converts the table in Figure 6.1 to 1NF in Figure 6.2. Step 2: Identify the Primary Key The layout in Figure 6.2 represents more than a mere cosmetic change. Even a casual observer will note that PROJ_NUM is not an adequate primary key because the project number does not uniquely identify all of the remaining entity (row) attributes. For example, the PROJ_NUM value 15 can identify any one of five employees. To maintain a proper primary key that will uniquely identify any attribute value, the new key must be composed of a combination of PROJ_NUM and EMP_NUM. For example, using the data shown in Figure 6.2, if you know that rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. transitive dependency A condition in which an attribute is dependent on another attribute that is not part of the primary key. repeating group In a relation, a characteristic describing a group of multiple entries of the same type for a single key attribute occurrence. For example, a car can have multiple colors for its top, interior, bottom, trim, and so on. Copyright 2017 Cengage Lea Editorial review has deemed that Chapter 6 Normalization of Database Tables 209 FIGURE6.2 ATABLEINFIRSTNORMALFORM Table name: DATA_ORG_1NF Database name: Ch06_ConstructCo PROJ_NUM = 15 and EMP_NUM = 103, the entries for the attributes PROJ_NAME, EMP_NAME, JOB_CLASS, CHG_HOUR, and HOURS must be Evergreen, June E. Arbough, Elect. Engineer, $84.50, and 23.8, respectively. Step 3: Identify All Dependencies The identification of the PK in Step 2 means that you have already identified the following dependency: PROJ_NUM, EMP_NUM → PROJ_NAME, EMP_NAME, JOB_CLASS, CHG_HOUR, HOURS That is, the PROJ_NAME, EMP_NAME, JOB_CLASS, CHG_HOUR, and HOURS values are all dependent on—they are determined by—the combination of PROJ_NUM and EMP_NUM. There are additional dependencies. For example, the project number identifies (determines) the project name. In other words, the project name is dependent on the project number. You can write that dependency as: PROJ_NUM → PROJ_NAME Also, if you know an employee number, you also know that employee’s name, job classification, and charge per hour. Therefore, you can identify the dependency shown next: EMP_NUM → EMP_NAME, JOB_CLASS, CHG_HOUR In simpler terms, an employee has the following attributes: a number, a name, a job classification, and a charge per hour. However, by further studying the data in Figure 6.2, you can see that knowing the job classification means knowing the charge per hour for that job classification. (Notice that all “System Analyst” or “Programmer” positions have the same charge per hour regardless of the project or employee.) In other words, the charge per hour depends on the job classification, not the employee. Therefore, you can identify one last dependency: JOB_CLASS → CHG_HOUR Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 210 Part 2 Design Concepts This dependency exists between two nonprime attributes; therefore, it is a signal that a transitive dependency exists, and we will refer to it as a transitive dependency. The dependencies you have just examined can also be depicted with the help of the diagram shown in Figure 6.3. Because such a diagram depicts all dependencies found within a given table structure, it is known as a dependency diagram. Dependency diagrams are very helpful in getting a bird’s-eye view of all the relationships among a table’s attributes, and their use makes it less likely that you will overlook an important dependency. FIGURE6.3 FIRSTNORMALFORM(1NF)DEPENDENCYDIAGRAM PROJ_NUM PROJ_NAME EMP_NUM EMP_NAME JOB_CLASS CHG_HOUR HOURS Partial dependency Transitive dependency Partial dependencies 1NF (PROJ_NUM, EMP_NUM, PROJ_NAME, EMP_NAME, JOB_CLASS, CHG_HOURS, HOURS) PARTIAL DEPENDENCIES: (PROJ_NUM PROJ_NAME) (EMP_NUM EMP_NAME, JOB_CLASS, CHG_HOUR) TRANSITIVE DEPENDENCY: (JOB_CLASS CHG_HOUR) As you examine Figure 6.3, note the following features of a dependency diagram: 1. The primary key attributes are bold, underlined, and in a different color. 2. The arrows above the attributes indicate all desirable dependencies—that is, depen- dencies based on the primary key. In this case, note that the entity’s attributes are dependent on the combination of PROJ_NUM and EMP_NUM. 3. The arrows below the dependency diagram indicate less desirable dependencies. Two types of such dependencies exist: a. Partial dependencies. You need to know only the PROJ_NUM to determine the PROJ_NAME; that is, the PROJ_NAME is dependent on only part of the primary key. Also, you need to know only the EMP_NUM to find the EMP_NAME, the JOB_CLASS, and the CHG_HOUR. A dependency based on only a part of a com- posite primary key is a partial dependency. b. Transitive dependencies. Note that CHG_HOUR is dependent on JOB_CLASS. Because neither CHG_HOUR nor JOB_CLASS is a prime attribute—that is, nei- ther attribute is at least part of a key—the condition is a transitive dependency. In other words, a transitive dependency is a dependency of one nonprime attribute on another nonprime attribute. The problem with transitive dependencies is that they still yield data anomalies. Figure 6.3 includes the relational schema for the table in 1NF and a textual notation for each identified dependency. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. dependency diagram A representation of all data dependencies (primary key, partial, or transitive) within a table. Copyright 2017 Cengage Lea Editorial review has deemed that Note All relational tables satisfy the 1NF requirements. The problem with the 1NF table structure shown in Figure 6.3 is that it contains partial dependencies—dependencies based on only a part of the primary key. While partial dependencies are sometimes used for performance reasons, they should be used with caution. Such caution is warranted because a table that contains partial dependencies is still subject to data redundancies, and therefore to various anomalies. The data redundancies occur because every row entry requires duplica- tion of data. For example, if Alice K. Johnson submits her work log, then the user would have to make multiple entries during the course of a day. For each entry, the EMP_NAME, JOB_CLASS, and CHG_HOUR must be entered each time, even though the attribute values are identical for each row entered. Such duplication of effort is very inefficient, and it helps create data anomalies; nothing prevents the user from typing slightly different versions of the employee name, the position, or the hourly pay. For instance, the employee name for EMP_NUM = 102 might be entered as Dave Senior or D. Senior. The project name might also be entered correctly as Evergreen or misspelled as Evergeen. Such data anomalies violate the relational database’s integrity and consis- tency rules. 6-3b Conversion To Second Normal Form Conversion to 2NF occurs only when the 1NF has a composite primary key. If the 1NF has a single-attribute primary key, then the table is automatically in 2NF. The 1NF-to- 2NF conversion is simple. Starting with the 1NF format displayed in Figure 6.3, you take the following steps: Step1:MakeNewTablestoEliminatePartialDependencies Foreachcomponent of the primary key that acts as a determinant in a partial dependency, create a new table with a copy of that component as the primary key. While these components are placed in the new tables, it is important that they also remain in the original table as well. The determinants must remain in the original table because they will be the foreign keys for the relationships needed to relate these new tables to the original table. To construct the revised dependency diagram, write each key component on a separate line and then write the original (composite) key on the last line. For example: PROJ_NUM EMP_NUM PROJ_NUM EMP_NUM Each component will become the key in a new table. In other words, the original table is now divided into three tables (PROJECT, EMPLOYEE, and ASSIGNMENT). first normal form (1NF) The first stage in the normalization process. It describes a relation depicted in tabular format, with no repeating groups and a primary key identified. All nonkey attributes in the relation are dependent on the primary key. Chapter 6 Normalization of Database Tables 211 The term first normal form (1NF) describes the tabular format in which: • All of the key attributes are defined. • There are no repeating groups in the table. In other words, each row/column inter- section contains one and only one value, not a set of values. • All attributes are dependent on the primary key. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 212 Part 2 Design Concepts Step 2: Reassign Corresponding Dependent Attributes Use Figure 6.3 to deter- mine attributes that are dependent in the partial dependencies. The dependencies for the original key components are found by examining the arrows below the dependency diagram shown in Figure 6.3. The attributes that are dependent in a partial dependency are removed from the original table and placed in the new table with the dependency’s determinant. Any attributes that are not dependent in a partial dependency will remain in the original table. In other words, the three tables that result from the conversion to 2NF are given appropriate names (PROJECT, EMPLOYEE, and ASSIGNMENT) and are described by the following relational schemas: PROJECT (PROJ_NUM, PROJ_NAME) EMPLOYEE (EMP_NUM, EMP_NAME, JOB_CLASS, CHG_HOUR) ASSIGNMENT (PROJ_NUM, EMP_NUM, ASSIGN_HOURS) Because the number of hours spent on each project by each employee is dependent on both PROJ_NUM and EMP_NUM in the ASSIGNMENT table, you leave those hours in the ASSIGNMENT table as ASSIGN_HOURS. Notice that the ASSIGNMENT table contains a composite primary key composed of the attributes PROJ_NUM and EMP_NUM. Notice that by leaving the determinants in the original table as well as making them the primary keys of the new tables, primary key/foreign key relation- ships have been created. For example, in the EMPLOYEE table, EMP_NUM is the primary key. In the ASSIGNMENT table, EMP_NUM is part of the composite primary key (PROJ_NUM, EMP_NUM) and is a foreign key relating the EMPLOYEE table to the ASSIGNMENT table. The results of Steps 1 and 2 are displayed in Figure 6.4. At this point, most of the anomalies discussed earlier have been eliminated. For example, if you now want to add, change, or delete a PROJECT record, you need to go only to the PROJECT table and make the change to only one row. Because a partial dependency can exist only when a table’s primary key is composed of several attributes, a table whose primary key consists of only a single attribute is auto- matically in 2NF once it is in 1NF. Figure 6.4 still shows a transitive dependency, which can generate anomalies. For example, if the charge per hour changes for a job classification held by many employees, that change must be made for each of those employees. If you forget to update some of the employee records that are affected by the charge per hour change, different employ- ees with the same job description will generate different hourly charges. second normal form (2NF) The second stage in the normalization process, in which a relation is in 1NF and there are no partial dependencies (dependencies in only part of the primary key). Note A table is in second normal form (2NF) when: • It is in 1NF. and • It includes no partial dependencies; that is, no attribute is dependent on only a portion of the primary key. It is still possible for a table in 2NF to exhibit transitive dependency. That is, the primary key may rely on one or more nonprime attributes to functionally determine other nonprime attributes, as indicated by a functional dependence among the nonprime attributes. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 6-3c Conversion To Third Normal Form The data anomalies created by the database organization shown in Figure 6.4 are easily eliminated by completing the following two steps: Step 1: Make New Tables to Eliminate Transitive Dependencies For every transitive dependency, write a copy of its determinant as a primary key for a new table. A determinant is any attribute whose value determines other values within a row. If you have three different transitive dependencies, you will have three different deter- minants. As with the conversion to 2NF, it is important that the determinant remain in the original table to serve as a foreign key. Figure 6.4 shows only one table that contains a transitive dependency. Therefore, write the determinant for this transitive dependency as: JOB_CLASS Step 2: Reassign Corresponding Dependent Attributes Using Figure 6.4, identify the attributes that are dependent on each determinant identified in Step 1. Place the dependent attributes in the new tables with their determinants and remove them from their original tables. In this example, eliminate CHG_HOUR from the EMPLOYEE table shown in Figure 6.4 to leave the EMPLOYEE table dependency definition as: EMP_NUM → EMP_NAME, JOB_CLASS determinant Any attribute in a specific row whose value directly determines other values in that row. Chapter 6 Normalization of Database Tables 213 FIGURE6.4 SECONDNORMALFORM(2NF)CONVERSIONRESULTS Table name: PROJECT PROJECT (PROJ_NUM, PROJ_NAME) PROJ_NUM PROJ_NAME Table name: EMPLOYEE EMPLOYEE (EMP_NUM, EMP_NAME, JOB_CLASS, CHG_HOUR) TRANSITIVE DEPENDENCY (JOB_CLASS CHG_HOUR) EMP_NUM EMP_NAME JOB_CLASS CHG_HOUR Transitive dependency Table name: ASSIGNMENT ASSIGNMENT (PROJ_NUM, EMP_NUM, ASSIGN_HOURS) PROJ_NUM EMP_NUM ASSIGN_HOURS Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 214 Part 2 Design Concepts Draw a new dependency diagram to show all of the tables you have defined in Steps 1 and 2. Name the table to reflect its contents and function. In this case, JOB seems appro- priate. Check all of the tables to make sure that each table has a determinant and that no table contains inappropriate dependencies. When you have completed these steps, you will see the results in Figure 6.5. FIGURE 6.5 THIRD NORMAL FORM (3NF) CONVERSION RESULTS PROJ_NUM PROJ_NAME Table name: PROJECT PROJECT (PROJ_NUM, PROJ_NAME) JOB_CLASS CHG_HOUR Table name: JOB JOB (JOB_CLASS, CHG_HOUR) EMP_NUM EMP_NAME JOB_CLASS Table name: EMPLOYEE EMPLOYEE (EMP_NUM, EMP_NAME, JOB_CLASS) PROJ_NUM EMP_NUM ASSIGN_HOURS Table name: ASSIGNMENT ASSIGNMENT (PROJ_NUM, EMP_NUM, ASSIGN_HOURS) In other words, after the 3NF conversion has been completed, your database will contain four tables: PROJECT (PROJ_NUM, PROJ_NAME) EMPLOYEE (EMP_NUM, EMP_NAME, JOB_CLASS) JOB (JOB_CLASS, CHG_HOUR) ASSIGNMENT (PROJ_NUM, EMP_NUM, ASSIGN_HOURS) Note that this conversion has eliminated the original EMPLOYEE table’s transitive dependency. The tables are now said to be in third normal form (3NF). third normal form (3NF) A table is in 3NF when it is in 2NF and no nonkey attribute is functionally dependent on another nonkey attribute; that is, it cannot include transitive dependencies. Note A table is in third normal form (3NF) when: • It is in 2NF. and • It contains no transitive dependencies. It is interesting to note the similarities between resolving 2NF and 3NF problems. To convert a table from 1NF to 2NF, it is necessary to remove the partial dependencies. To con- vert a table from 2NF to 3NF, it is necessary to remove the transitive dependencies. No mat- ter whether the “problem” dependency is a partial dependency or a transitive dependency, Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. the solution is the same: create a new table for each problem dependency. The determinant of the problem dependency remains in the original table and is placed as the primary key of the new table. The dependents of the problem dependency are removed from the original table and placed as nonprime attributes in the new table. Be aware, however, that while the technique is the same, it is imperative that 2NF be achieved before moving on to 3NF; be certain to resolve the partial dependencies before resolving the transitive dependencies. Also, recall the assumption that was made at the beginning of the normalization discussion—that each table has only one candidate key, which is the primary key. If a table has multiple candidate keys, then the overall process remains the same, but there are additional considerations. For example, if a table has multiple candidate keys and one of them is a composite key, the table can have partial dependencies based on this composite candidate key, even when the primary key chosen is a single attribute. In those cases, following the process described above, those dependencies would be perceived as transitive dependencies and would not be resolved until 3NF. The simplified process described above will allow the designer to achieve the correct result, but through practice, you should recognize all candidate keys and their dependencies as such, and resolve them appropriately. The existence of multiple candidate keys can also influence the identi- fication of transitive dependencies. Previously, a transitive dependency was defined to exist when one nonprime attribute determined another nonprime attribute. In the presence of multiple candidate keys, the definition of a nonprime attribute as an attribute that is not a part of any candidate key is critical. If the determinant of a functional dependence is not the primary key but is a part of another candidate key, then it is not a nonprime attribute and does not signal the presence of a transitive dependency. 6-4 Improving the Design Now that the table structures have been cleaned up to eliminate the troublesome par- tial and transitive dependencies, you can focus on improving the database’s ability to provide information and on enhancing its operational characteristics. In the next few paragraphs, you will learn about the various types of issues you need to address to produce a good normalized set of tables. Note that for space issues, each section pres- ents just one example—the designer must apply the principle to all remaining tables in the design. Remember that normalization cannot, by itself, be relied on to make good designs. Instead, normalization is valuable because its use helps eliminate data redundancies. EvaluatePKAssignments EachtimeanewemployeeisenteredintotheEMPLOYEE table, a JOB_CLASS value must be entered. Unfortunately, it is too easy to make data-entry errors that lead to referential integrity violations. For example, entering DB Designer instead of Database Designer for the JOB_CLASS attribute in the EMPLOYEE table will trigger such a violation. Therefore, it would be better to add a JOB_CODE attribute to create a unique identifier. The addition of a JOB_CODE attribute produces the following dependency: JOB_CODE → JOB_CLASS, CHG_HOUR If you assume that the JOB_CODE is a proper primary key, this new attribute does produce the following dependency: JOB_CLASS → CHG_HOUR Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 215 216 Part 2 Design Concepts However, this dependency is not a transitive dependency because the determinant is a candidate key. Further, the presence of JOB_CODE greatly decreases the likelihood of referential integrity violations. Note that the new JOB table now has two candidate keys—JOB_CODE and JOB_CLASS. In this case, JOB_CODE is the chosen primary key as well as a surrogate key. A surrogate key, as you should recall, is an artificial PK intro- duced by the designer with the purpose of simplifying the assignment of primary keys to tables. Surrogate keys are usually numeric, they are often generated automatically by the DBMS, they are free of semantic content (they have no special meaning), and they are usually hidden from the end users. Evaluate Naming Conventions It is best to adhere to the naming conventions out- lined in Chapter 2, Data Models. Therefore, CHG_HOUR will be changed to JOB_CHG_ HOUR to indicate its association with the JOB table. In addition, the attribute name JOB_CLASS does not quite describe entries such as Systems Analyst, Database Designer, and so on; the label JOB_DESCRIPTION fits the entries better. Also, you might have noticed that HOURS was changed to ASSIGN_HOURS in the conversion from 1NF to 2NF. That change lets you associate the hours worked with the ASSIGNMENT table. Refine Attribute Atomicity It is generally good practice to pay attention to the ato- micity requirement. An atomic attribute is one that cannot be further subdivided. Such an attribute is said to display atomicity. Clearly, the use of the EMP_NAME in the EMPLOYEE table is not atomic because EMP_NAME can be decomposed into a last name, a first name, and an initial. By improving the degree of atomicity, you also gain querying flexibility. For example, if you use EMP_LNAME, EMP_FNAME, and EMP_INITIAL, you can easily generate phone lists by sorting last names, first names, and initials. Such a task would be very difficult if the name components were within a single attribute. In general, designers prefer to use simple, single-valued attributes, as indicated by the business rules and processing requirements. Identify New Attributes If the EMPLOYEE table were used in a real-world environ- ment, several other attributes would have to be added. For example, year-to-date gross salary payments, Social Security payments, and Medicare payments would be desirable. An employee hire date attribute (EMP_HIREDATE) could be used to track an employee’s job longevity, and it could serve as a basis for awarding bonuses to long-term employees and for other morale-enhancing measures. The same principle must be applied to all other tables in your design. Identify New Relationships According to the original report, the users need to track which employee is acting as the manager of each project. This can be implemented as a relationship between EMPLOYEE and PROJECT. From the original report, it is clear that each project has only one manager. Therefore, the system’s ability to supply detailed information about each project’s manager is ensured by using the EMP_NUM as a for- eign key in PROJECT. That action ensures that you can access the details of each PROJ- ECT’s manager data without producing unnecessary and undesirable data duplication. The designer must take care to place the right attributes in the right tables by using normalization principles. Refine Primary Keys as Required for Data Granularity Granularity refers to the level of detail represented by the values stored in a table’s row. Data stored at its low- est level of granularity is said to be atomic data, as explained earlier. In Figure 6.5, the ASSIGNMENT table in 3NF uses the ASSIGN_HOURS attribute to represent the hours worked by a given employee on a given project. However, are those values recorded at rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. atomic attribute An attribute that cannot be further subdivided to produce meaningful components. For example, a person’s last name attribute cannot be meaningfully subdivided. atomicity Not being able to be divided into smaller units. granularity The level of detail represented by the values stored in a table’s row. Data stored at its lowest level of granularity is said to be atomic data. Copyright 2017 Cengage Lea Editorial review has deemed that their lowest level of granularity? In other words, does ASSIGN_HOURS represent the hourly total, daily total, weekly total, monthly total, or yearly total? Clearly, ASSIGN_ HOURS requires more careful definition. In this case, the relevant question would be as follows: for what time frame—hour, day, week, month, and so on—do you want to record the ASSIGN_HOURS data? For example, assume that the combination of EMP_NUM and PROJ_NUM is an acceptable (composite) primary key in the ASSIGNMENT table. That primary key is useful in representing only the total number of hours an employee worked on a project since its start. Using a surrogate primary key such as ASSIGN_NUM pro- vides lower granularity and yields greater flexibility. For example, assume that the EMP_NUM and PROJ_NUM combination is used as the primary key, and then an employee makes two “hours worked” entries in the ASSIGNMENT table. That action violates the entity integrity requirement. Even if you add the ASSIGN_DATE as part of a composite PK, an entity integrity violation is still generated if any employee makes two or more entries for the same project on the same day. (The employee might have worked on the project for a few hours in the morning and then worked on it again later in the day.) The same data entry yields no problems when ASSIGN_ NUM is used as the primary key. Note Maintain Historical Accuracy Writing the job charge per hour into the ASSIGN- MENT table is crucial to maintaining the historical accuracy of the table’s data. It would be appropriate to name this attribute ASSIGN_CHG_HOUR. Although this attribute would appear to have the same value as JOB_CHG_HOUR, this is true only if the JOB_ CHG_HOUR value remains the same forever. It is reasonable to assume that the job charge per hour will change over time. However, suppose that the charges to each project were calculated and billed by multiplying the hours worked from the ASSIGNMENT table by the charge per hour from the JOB table. Those charges would always show the current charge per hour stored in the JOB table rather than the charge per hour that was in effect at the time of the assignment. Evaluate Using Derived Attributes Finally, you can use a derived attribute in the ASSIGNMENT table to store the actual charge made to a project. That derived attribute, named ASSIGN_CHARGE, is the result of multiplying ASSIGN_HOURS by ASSIGN_ CHG_HOUR. This creates a transitive dependency such that: (ASSIGN_CHARGE + ASSIGN_HOURS) → ASSIGN_CHG_HOUR From a system functionality point of view, such derived attribute values can be cal- culated when they are needed to write reports or invoices. However, storing the derived attribute in the table makes it easy to write the application software to produce the desired results. Also, if many transactions must be reported and/or summarized, the availability Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 217 In an ideal database design, the level of desired granularity would be determined during the conceptual design or while the requirements were being gathered. However, as you have already seen in this chapter, many database designs involve the refinement of exist- ing data requirements, thus triggering design modifications. In a real-world environment, changing granularity requirements might dictate changes in primary key selection, and those changes might ultimately require the use of surrogate keys. 218 Part 2 Design Concepts of the derived attribute will save reporting time. (If the calculation is done at the time of data entry, it will be completed when the end user presses the Enter key, thus speeding up the process.) Review Chapter 4 for a discussion of the implications of storing derived attributes in a database table. The enhancements described in the preceding sections are illustrated in the tables and dependency diagrams shown in Figure 6.6. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE 6.6 THE COMPLETED DATABASE Table name: PROJECT PROJ_NUM PROJ_NAME Table name: PROJECT EMP_NUM Table name: JOB Database name: Ch06_ConstructCo JOB_CODE JOB_DESCRIPTION JOB_CHG_HOUR Table name: JOB Table name: ASSIGNMENT ASSIGN_NUM ASSIGN_DATE PROJ_NUM EMP_NUM ASSIGN_HOURS ASSIGN_CHG_HOUR ASSIGN_CHARGE Table name: ASSIGNMENT Chapter 6 Normalization of Database Tables 219 FIGURE 6.6 THE COMPLETED DATABASE (CONTINUED) Table name: EMPLOYEE Database name: Ch06_ConstructCo EMP_NUM EMP_LNAME EMP_FNAME EMP_INITIAL EMP_HIREDATE JOB_CODE Table name: EMPLOYEE Figure 6.6 is a vast improvement over the original database design. If the application soft- ware is designed properly, the most active table (ASSIGNMENT) requires the entry of only the PROJ_NUM, EMP_NUM, and ASSIGN_HOURS values. The values for the attributes ASSIGN_NUM and ASSIGN_DATE can be generated by the application. For example, the ASSIGN_NUM can be created by using a counter, and the ASSIGN_DATE can be the system date read by the application and automatically entered into the ASSIGNMENT table. In addi- tion, the application software can automatically insert the correct ASSIGN_CHG_HOUR value by writing the appropriate JOB table’s JOB_CHG_HOUR value into the ASSIGN- MENT table. (The JOB and ASSIGNMENT tables are related through the JOB_CODE attri- bute.) If the JOB table’s JOB_CHG_HOUR value changes, the next insertion of that value into the ASSIGNMENT table will reflect the change automatically. The table structure thus minimizes the need for human intervention. In fact, if the system requires the employees to enter their own work hours, they can scan their EMP_NUM into the ASSIGNMENT table by using a magnetic card reader that enters their identity. Thus, the ASSIGNMENT table’s structure can set the stage for maintaining some desired level of security. 6-5 Surrogate Key Considerations Although this design meets the vital entity and referential integrity requirements, the designer must still address some concerns. For example, a composite primary key might become too cumbersome to use as the number of attributes grows. (It becomes difficult to create a suitable foreign key when the related table uses a composite primary key. In addition, a composite primary key makes it more difficult to write search routines.) Or, a primary key attribute might simply have too much descriptive content to be usable— which is why the JOB_CODE attribute was added to the JOB table to serve as its primary key. When the primary key is considered to be unsuitable for some reason, designers use surrogate keys, as discussed in the previous chapter. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 220 Part 2 Design Concepts At the implementation level, a surrogate key is a system-defined attribute generally created and managed via the DBMS. Usually, a system-defined surrogate key is numeric, and its value is automatically incremented for each new row. For example, Microsoft Access uses an AutoNumber data type, Microsoft SQL Server uses an identity column, and Oracle uses a sequence object. Recall from Section 6-4 that the JOB_CODE attribute was designated to be the JOB table’s primary key. However, remember that the JOB_CODE attribute does not prevent duplicate entries, as shown in the JOB table in Table 6.4. Clearly, the data entries in Table 6.4 are inappropriate because they duplicate existing records—yet there has been no violation of either entity integrity or referential integrity. This problem of multiple duplicate records was created when the JOB_CODE attribute was added as the PK. (When the JOB_DESCRIPTION was initially designated to be the PK, the DBMS would ensure unique values for all job description entries when it was asked to enforce entity integrity. However, that option created the problems that caused the use of the JOB_CODE attribute in the first place!) In any case, if JOB_CODE is to be the surrogate PK, you still must ensure the existence of unique values in the JOB_ DESCRIPTION through the use of a unique index. Note that all of the remaining tables (PROJECT, ASSIGNMENT, and EMPLOYEE) are subject to the same limitations. For example, if you use the EMP_NUM attribute in the EMPLOYEE table as the PK, you can make multiple entries for the same employee. To avoid that problem, you might create a unique index for EMP_LNAME, EMP_FNAME, and EMP_INITIAL, but how would you then deal with two employees named Joe B. Smith? In that case, you might use another (preferably externally defined) attribute to serve as the basis for a unique index. It is worth repeating that database design often involves trade-offs and the exercise of professional judgment. In a real-world environment, you must strike a balance between design integrity and flexibility. For example, you might design the ASSIGNMENT table to use a unique index on PROJ_NUM, EMP_NUM, and ASSIGN_DATE if you want to limit an employee to only one ASSIGN_HOURS entry per date. That limitation would ensure that employees could not enter the same hours multiple times for any given date. Unfortunately, that limitation is likely to be undesirable from a managerial point of view. After all, if an employee works several different times on a project during any given day, it must be possible to make multiple entries for that same employee and the same project during that day. In that case, the best solution might be to add a new externally defined attribute—such as a stub, voucher, or ticket number—to ensure uniqueness. In any case, frequent data audits would be appropriate. 6-6 Higher-Level Normal Forms Tables in 3NF will perform suitably in business transactional databases. However, higher normal forms are sometimes useful. In this section, you will learn about a special case of 3NF, known as Boyce-Codd normal form, and about fourth normal form (4NF). Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. TABLE 6.4 DUPLICATE ENTRIES IN THE JOB TABLE JOB_CODE JOB_DESCRIPTION JOB_CHG_HOUR 511 Programmer $35.75 512 Programmer $35.75 6-6a The Boyce-Codd Normal Form A table is in Boyce-Codd normal form (BCNF) when every determinant in the table is a candidate key. (Recall from Chapter 3 that a candidate key has the same characteristics as a primary key, but for some reason, it was not chosen to be the primary key.) Clearly, when a table contains only one candidate key, the 3NF and the BCNF are equivalent. In other words, BCNF can be violated only when the table contains more than one candi- date key. In the previous normal form examples, tables with only one candidate key were used to simplify the explanations. Remember, however, that multiple candidate keys are always possible, and normalization rules focus on candidate keys, not just the primary key. Consider the table structure shown in Figure 6.7. The CLASS table has two candidate keys: • CLASS_CODE • CRS_CODE + CLASS_SECTION The table is in 1NF because the key attributes are defined and all nonkey attributes are determined by the key. This is true for both candidate keys. Both candidate keys have been identified, and all of the other attributes can be determined by either candidate key. The table is in 2NF because it is in 1NF and there are no partial dependencies on either candidate key. Since CLASS_CODE is a single attribute candidate key, the issue of par- tial dependencies doesn’t apply. However, the composite candidate key of CRS_CODE + CLASS_SECTION could potentially have a partial dependency so 2NF must be evalu- ated for that candidate key. In this case, there are no partial dependencies involving the composite key. Finally, the table is in 3NF because there are no transitive dependencies. Remember, because CRS_CODE + CLASS_SECTION is a candidate key, the fact that this composite can determine the CLASS_TIME and ROOM_CODE is not a transitive dependency. A transitive dependency exists when a nonkey attribute can determine another nonkey attribute, and CRS_CODE + CLASS_SECTION is a key. Note A table is in Boyce-Codd normal form (BCNF) when every determinant in the table is a candidate key. Most designers consider the BCNF to be a special case of the 3NF. In fact, if the tech- niques shown in this chapter are used, most tables conform to the BCNF requirements once the 3NF is reached. So, how can a table be in 3NF and not be in BCNF? To answer that question, you must keep in mind that a transitive dependency exists when one non- prime attribute is dependent on another nonprime attribute. Boyce-Codd normal form (BCNF) A special type of third normal form (3NF) in which every determinant is a candidate key. A table in BCNF must be in 3NF. See also determinant. Chapter 6 Normalization of Database Tables 221 FIGURE 6.7 TABLES WITH MULTIPLE CANDIDATE KEYS Table name: CLASS CLASS_CODE CRS_CODE CLASS_SECTION CLASS_TIME ROOM_CODE Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 222 Part 2 Design Concepts In other words, a table is in 3NF when it is in 2NF and there are no transitive depen- dencies, but what about a case in which one key attribute is the determinant of another key attribute? That condition does not violate 3NF, yet it fails to meet the BCNF requirements (see Figure 6.8) because BCNF requires that every determinant in the table be a candidate key. Note these functional dependencies in Figure 6.8: A + B → C, D A + C → B, D C→B Notice that this structure has two candidate keys: (A + B) and (A + C). The table structure shown in Figure 6.8 has no partial dependencies, nor does it contain transitive dependencies. (The condition C → B indicates that one key attribute determines part of the primary key—and that dependency is not transitive or partial because the dependent is a prime attribute!) Thus, the table structure in Figure 6.8 meets the 3NF requirements, although the condition C → B causes the table to fail to meet the BCNF requirements. To convert the table structure in Figure 6.8 into table structures that are in 3NF and in BCNF, first change the primary key to A + C. This change is appropriate because the dependency C → B means that C is effectively a superset of B. At this point, the table is in 1NF because it contains a partial dependency, C → B. Next, follow the standard decomposition procedures to produce the results shown in Figure 6.9. To see how this procedure can be applied to an actual problem, examine the sample data in Table 6.5. Table 6.5 reflects the following conditions: • Each CLASS_CODE identifies a class uniquely. This condition illustrates the case in which a course might generate many classes. For example, a course labeled INFS 420 might be taught in two classes (sections), each identified by a unique code to facilitate registration. Thus, the CLASS_CODE 32456 might identify INFS 420, class section 1, while the CLASS_CODE 32457 might identify INFS 420, class section 2. Or, the CLASS_CODE 28458 might identify QM 362, class section 5. • A student can take many classes. Note, for example, that student 125 has taken both 21334 and 32456, earning the grades A and C, respectively. • A staff member can teach many classes, but each class is taught by only one staff member. Note that staff member 20 teaches the classes identified as 32456 and 28458. The structure shown in Table 6.5 is reflected in Panel A of Figure 6.10: STU_ID + STAFF_ID → CLASS_CODE, ENROLL_GRADE CLASS_CODE → STAFF_ID Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE6.8 ATABLETHATISIN3NFBUTNOTINBCNF ABCD Chapter 6 Normalization of Database Tables 223 FIGURE6.9 DECOMPOSITIONTOBCNF 3NF, but not BCNF A B C D 1NF A C B D Partial dependency ACD CB 3NF and BCNF 3NF and BCNF TABLE 6.5 SAMPLE DATA FOR A BCNF CONVERSION STU_ID STAFF_ID CLASS_CODE ENROLL_GRADE 125 25 21334 A 125 20 32456 C 135 20 28458 B 144 25 27563 C 144 20 32456 B Panel A of Figure 6.10 shows a structure that is clearly in 3NF, but the table repre- sented by this structure has a major problem because it is trying to describe two things: staff assignments to classes and student enrollment information. Such a dual-purpose table structure will cause anomalies. For example, if a different staff member is assigned to teach class 32456, two rows will require updates, thus producing an update anomaly. Also, if student 135 drops class 28458, information about who taught that class is lost, thus producing a deletion anomaly. The solution to the problem is to decompose the table structure, following the procedure outlined earlier. The decomposition of Panel B shown in Figure 6.10 yields two table structures that conform to both 3NF and BCNF requirements. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 224 Part 2 Design Concepts FIGURE6.10 ANOTHERBCNFDECOMPOSITION Panel A: 3NF, but not BCNF STU_ID STAFF_ID CLASS_CODE ENROLL_GRADE Panel B: 3NF and BCNF STU_ID CLASS_CODE ENROLL_GRADE CLASS_CODE STAFF_ID Remember that a table is in BCNF when every determinant in that table is a candi- date key. Therefore, when a table contains only one candidate key, 3NF and BCNF are equivalent. 6-6b Fourth Normal Form (4NF) You might encounter poorly designed databases, or you might be asked to convert spreadsheets into a database format in which multiple multivalued attributes exist. For example, consider the possibility that an employee can have multiple assignments and can also be involved in multiple service organizations. Suppose employee 10123 vol- unteers for the Red Cross and United Way. In addition, the same employee might be assigned to work on three projects: 1, 3, and 4. Figure 6.11 illustrates how that set of facts can be recorded in very different ways. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE 6.11 TABLES WITH MULTIVALUED DEPENDENCIES Database name: Ch06_Service Table name: VOLUNTEER_V1 Table name: VOLUNTEER_V2 Table name: VOLUNTEER_V3 There is a problem with the tables in Figure 6.11. The attributes ORG_CODE and ASSIGN_NUM each may have many different values. In normalization ter- minology, this situation is referred to as a multivalued dependency, which occurs when one key determines multiple values of two other attributes and those attributes are independent of each other. (One employee can have many service entries and many assignment entries. Therefore, one EMP_NUM can determine multiple val- ues of ORG_CODE and multiple values of ASSIGN_NUM; however, ORG_CODE and ASSIGN_NUM are independent of each other.) The presence of a multivalued dependency means that if table versions 1 and 2 are implemented, the tables are likely to contain quite a few null values; in fact, the tables do not even have a via- ble candidate key. (The EMP_NUM values are not unique, so they cannot be PKs. No combination of the attributes in table versions 1 and 2 can be used to create a PK because some of them contain nulls.) Such a condition is not desirable, espe- cially when there are thousands of employees, many of whom may have multiple job assignments and many service activities. Version 3 at least has a PK, but it is com- posed of all the attributes in the table. In fact, version 3 meets 3NF requirements, yet it contains many redundancies that are clearly undesirable. The solution is to eliminate the problems caused by the multivalued dependency. You do this by creating new tables for the components of the multivalued dependency. In this example, the multivalued dependency is resolved and eliminated by creating the ASSIGNMENT and SERVICE_V1 tables depicted in Figure 6.12. Those tables are said to be in 4NF. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 225 FIGURE6.12 ASETOFTABLESIN4NF Table name: PROJECT Table name: ASSIGNMENT The relational diagram Database name: CH06_Service Table name: EMPLOYEE Table name: ORGANIZATION Table name: SERVICE_V1 226 Part 2 Design Concepts Note If you follow the proper design procedures illustrated in this book, you should not encounter the problem shown in Figure 6.11. Specifically, the discussion of 4NF is largely academic if you make sure that your tables conform to the following two rules: 1. All attributes must be dependent on the primary key, but they must be independent of each other. 2. No row may contain two or more multivalued facts about an entity. 6-7 Normalization and Database Design The tables shown in Figure 6.6 illustrate how normalization procedures can be used to produce good tables from poor ones. You will likely have ample opportunity to put this skill into practice when you begin to work with real-world databases. Normalization should be part of the design process. Therefore, make sure that proposed entities meet the required normal form before the table structures are created. Keep in mind that if you follow the design procedures discussed in Chapters 3 and 4, the likelihood of data anomalies will be small. However, even the best database designers are known to make occasional mistakes that come to light during normalization checks. Also, many of the real-world databases you encounter will have been improperly designed or burdened with anomalies if they were improperly modified over the course of time. That means you might be asked to redesign and modify existing databases that are, in effect, anomaly traps. Therefore, you should be aware of good design principles and procedures as well as normalization procedures. First, an ERD is created through an iterative process. You begin by identifying rele- vant entities, their attributes, and their relationships. Then you use the results to identify additional entities and attributes. The ERD provides the big picture, or macro view, of an organization’s data requirements and operations. Second, normalization focuses on the characteristics of specific entities; that is, nor- malization represents a micro view of the entities within the ERD. Also, as you learned in the previous sections of this chapter, the normalization process might yield additional entities and attributes to be incorporated into the ERD. Therefore, it is difficult to sep- arate normalization from ER modeling; the two techniques are used in an iterative and incremental process. To understand the proper role of normalization in the design process, you should reexamine the operations of the contracting company whose tables were normalized in the preceding sections. Those operations can be summarized by using the following business rules: • The company manages many projects. • Each project requires the services of many employees. • An employee may be assigned to several different projects. • Some employees are not assigned to a project and perform duties not specifically related to a project. Some employees are part of a labor pool, to be shared by all proj- ect teams. For example, the company’s executive secretary would not be assigned to any one particular project. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. A table is in fourth normal form (4NF) when it is in 3NF and has no multivalued dependencies. fourth normal form (4NF) A table is in 4NF if it is in 3NF and contains no multiple independent sets of multivalued dependencies. Copyright 2017 Cengage Lea Editorial review has deemed that Chapter 6 Normalization of Database Tables 227 • Each employee has a single primary job classification, which determines the hourly billing rate. • Many employees can have the same job classification. For example, the company employs more than one electrical engineer. Given that simple description of the company’s operations, two entities and their attributes are initially defined: • PROJECT (PROJ_NUM, PROJ_NAME) • EMPLOYEE (EMP_NUM, EMP_LNAME, EMP_FNAME, EMP_INITIAL, JOB_ DESCRIPTION, JOB_CHG_HOUR) Those two entities constitute the initial ERD shown in Figure 6.13. After creating the initial ERD shown in Figure 6.13, the normal forms are defined: • PROJECT is in 3NF and needs no modification at this point. • EMPLOYEE requires additional scrutiny. The JOB_DESCRIPTION attribute defines job classifications such as Systems Analyst, Database Designer, and Programmer. In turn, those classifications determine the billing rate, JOB_CHG_HOUR. Therefore, EMPLOYEE contains a transitive dependency. The removal of EMPLOYEE’s transitive dependency yields three entities: • PROJECT (PROJ_NUM, PROJ_NAME) • EMPLOYEE (EMP_NUM, EMP_LNAME, EMP_FNAME, EMP_INITIAL, JOB_CODE) • JOB (JOB_CODE, JOB_DESCRIPTION, JOB_CHG_HOUR) Because the normalization process yields an additional entity (JOB), the initial ERD is modified as shown in Figure 6.14. To represent the M:N relationship between EMPLOYEE and PROJECT, you might think that two 1:M relationships could be used—an employee can be assigned to many projects, and each project can have many employees assigned to it. (See Figure 6.15.) Unfortunately, that representation yields a design that cannot be correctly implemented. Because the M:N relationship between EMPLOYEE and PROJECT cannot be imple- mented, the ERD in Figure 6.15 must be modified to include the ASSIGNMENT entity to track the assignment of employees to projects, thus yielding the ERD shown in Fig- ure 6.16. The ASSIGNMENT entity in Figure 6.16 uses the primary keys from the enti- ties PROJECT and EMPLOYEE to serve as its foreign keys. However, note that in this implementation, the ASSIGNMENT entity’s surrogate primary key is ASSIGN_NUM, to avoid the use of a composite primary key. Therefore, the “enters” relationship between EMPLOYEE and ASSIGNMENT and the “requires” relationship between PROJECT and ASSIGNMENT are shown as weak or nonidentifying. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE6.13 INITIALCONTRACTINGCOMPANYERD 228 Part 2 Design Concepts FIGURE6.14 MODIFIEDCONTRACTINGCOMPANYERD FIGURE6.15 INCORRECTM:NRELATIONSHIPREPRESENTATION In Figure 6.16, the ASSIGN_HOURS attribute is assigned to the composite entity named ASSIGNMENT. Because you will likely need detailed information about each project’s manager, the creation of a “manages” relationship is useful. The “manages” relationship is implemented through the foreign key in PROJECT. Finally, some addi- tional attributes may be created to improve the system’s ability to generate additional information. For example, you may want to include the date the employee was hired (EMP_HIREDATE) to keep track of worker longevity. Based on this last modification, the model should include four entities and their attributes: PROJECT (PROJ_NUM, PROJ_NAME, EMP_NUM) EMPLOYEE (EMP_NUM, EMP_LNAME, EMP_FNAME, EMP_INITIAL, EMP_HIREDATE, JOB_CODE) JOB (JOB_CODE, JOB_DESCRIPTION, JOB_CHG_HOUR) ASSIGNMENT (ASSIGN_NUM, ASSIGN_DATE, PROJ_NUM, EMP_NUM, ASSIGN_HOURS, ASSIGN_CHG_HOUR, ASSIGN_CHARGE) Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. The design process is now on the right track. The ERD represents the operations accurately, and the entities now reflect their conformance to 3NF. The combination of normalization and ER modeling yields a useful ERD, whose entities may now be trans- lated into appropriate table structures. In Figure 6.15, note that PROJECT is optional to EMPLOYEE in the “manages” relationship. This optionality exists because not all employees manage projects. The final database contents are shown in Figure 6.17. 6-8 Denormalization It is important to remember that the optimal relational database implementation requires that all tables be at least in third normal form (3NF). A good relational DBMS excels at manag- ing normalized relations—that is, relations void of any unnecessary redundancies that might cause data anomalies. Although the creation of normalized relations is an important database design goal, it is only one of many such goals. Good database design also considers processing (or reporting) requirements and processing speed. The problem with normalization is that as tables are decomposed to conform to normalization requirements, the number of database tables expands. Therefore, in order to generate information, data must be put together from various tables. Joining a large number of tables takes additional input/output (I/O) operations and processing logic, thereby reducing system speed. Most relational database systems are able to handle joins very efficiently. However, rare and occasional circumstances may allow some degree of denormalization so processing speed can be increased. Keep in mind that the advantage of higher processing speed must be carefully weighed against the disadvantage of data anomalies. On the other hand, some anomalies are of only theoretical interest. For example, should people in a real-world database environ- ment worry that a ZIP_CODE determines CITY in a CUSTOMER table whose primary key is the customer number? Is it really practical to produce a separate table for ZIP (ZIP_CODE, CITY) to eliminate a transitive dependency from the CUSTOMER table? (Perhaps your answer to that question changes if you are in the business of producing mailing lists.) As explained earlier, the problem with denormalized relations and redundant data is that data integrity could be compromised due to the possibility of insert, update, and Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 229 FIGURE6.16 FINALCONTRACTINGCOMPANYERD 230 Part 2 Design Concepts FIGURE 6.17 THE IMPLEMENTED DATABASE Table name: EMPLOYEE Database name: Ch06_ConstructCo Table name: JOB Table name: ASSIGNMENT Table name: PROJECT deletion anomalies. The advice is simple: use common sense during the normalization process. Furthermore, the database design process could, in some cases, introduce some small degree of redundant data in the model, as seen in the previous example. This, in effect, creates “denormalized” relations. Table 6.6 shows some common examples of data redundancy that are generally found in database implementations. A more comprehensive example of the need for denormalization due to reporting requirements is the case of a faculty evaluation report in which each row lists the scores obtained during the last four semesters taught. (See Figure 6.18.) Although this report seems simple enough, the problem is that the data is stored in a normalized table in which each row represents a different score for a given faculty member in a given semester. (See Figure 6.19.) The difficulty of transposing multirow data to multicolumn data is compounded by the fact that the last four semesters taught are not necessarily the same for all faculty members. Some might have taken sabbaticals, some might have had research appoint- ments, some might be new faculty with only two semesters on the job, and so on. To generate this report, the two tables in Figure 6.18 were used. The EVALDATA table is Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 231 TABLE 6.6 COMMON DENORMALIZATION EXAMPLES CASE EXAMPLE RATIONALE AND CONTROLS Redundant data Storing ZIP and CITY attributes in the AGENT table when ZIP determines CITY (see Figure 2.2) Avoid extra join operations Program can validate city (drop-down box) based on the zip code Derived data Storing STU_HRS and STU_CLASS (student classification) when STU_HRS determines STU_CLASS (see Figure 3.28) Avoid extra join operations Program can validate classification (lookup) based on the student hours Preaggregated data (also derived data) Storing the student grade point average (STU_GPA) aggregate value in the STUDENT table when this can be calculated from the ENROLL and COURSE tables (see Figure 3.28) Avoid extra join operations Program computes the GPA every time a grade is entered or updated STU_GPA can be updated only via administrative routine Information requirements Using a temporary denormalized table to hold report data; this is required when creating a tabular report in which the columns represent data that are stored in the table as rows (see Figures 6.17 and 6.18) Impossible to generate the data required by the report using plain SQL No need to maintain table Temporary table is deleted once report is done Processing speed is not an issue FIGURE 6.18 THE FACULTY EVALUATION REPORT the master data table containing the evaluation scores for each faculty member for each semester taught; this table is normalized. The FACHIST table contains the last four data points—that is, evaluation score and semester—for each faculty member. The FACHIST table is a temporary denormalized table created from the EVALDATA table via a series of queries. (The FACHIST table is the basis for the report shown in Figure 6.18.) As shown in the faculty evaluation report, the conflicts between design efficiency, information requirements, and performance are often resolved through compromises that may include denormalization. In this case, and assuming there is enough storage space, the designer’s choices could be narrowed down to: • • Store the data in a permanent denormalized table. This is not the recommended solu- tion because the denormalized table is subject to data anomalies (insert, update, and delete). This solution is viable only if performance is an issue. Create a temporary denormalized table from the permanent normalized table(s). The denormalized table exists only as long as it takes to generate the report; it disappears after the report is produced. Therefore, there are no data anomaly problems. This solution is practical only if performance is not an issue and there are no other viable processing options. As shown, normalization purity is often difficult to sustain in the modern database environ- ment. You will learn in Chapter 13, Business Intelligence and Data Warehouses, that lower Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 232 Part 2 Design Concepts FIGURE 6.19 THE EVALDATA AND FACHIST TABLES Table name: EVALDATA Table name: FACHIST Database name: Ch06_EVAL Denormalized Repeating Group Normalized normalization forms occur (and are even required) in specialized databases known as data warehouses. Such specialized databases reflect the ever-growing demand for greater scope and depth in the data on which decision support systems increasingly rely. You will discover that the data warehouse routinely uses 2NF structures in its complex, multilevel, multisource data environment. In short, although normalization is very important, especially in the so-called production database environment, 2NF is no longer disregarded as it once was. Although 2NF tables cannot always be avoided, the problem of working with tables that contain partial and/or transitive dependencies in a production database environment should not be minimized. Aside from the possibility of troublesome data anomalies being created, unnormalized tables in a production database tend to suffer from these defects: • Data updates are less efficient because programs that read and update tables must deal with larger tables. • Indexing is more cumbersome. It is simply not practical to build all of the indexes required for the many attributes that might be located in a single unnormalized table. • Unnormalized tables yield no simple strategies for creating virtual tables known as views. You will learn how to create and use views in Chapter 8, Advanced SQL. Remember that good design cannot be created in the application programs that use a database. Also keep in mind that unnormalized database tables often lead to various data redundancy disasters in production databases, such as the problems examined thus far. In other words, use denormalization cautiously and make sure that you can explain why the unnormalized tables are a better choice in certain situations than their normalized counterparts. 6-9 Data-Modeling Checklist In the chapters of Part 2, you have learned how data modeling translates a specific real-world environment into a data model that represents the real-world data, users, processes, and interactions. The modeling techniques you have learned thus far give you the tools needed to produce successful database designs. However, just as any good pilot uses a checklist to ensure that all is in order for a successful flight, the data-modeling checklist shown in Table 6.7 will help ensure that you perform data-modeling tasks successfully based on the concepts and tools you have learned in this text. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Note Chapter 6 Normalization of Database Tables 233 You can also find this data-modeling checklist on the inside front cover of this book for easy reference. TABLE 6.7 DATA-MODELING CHECKLIST BUSINESS RULES • Properly document and verify all business rules with the end users. • Ensure that all business rules are written precisely, clearly, and simply. The business rules must help identify entities, attributes, relationships, and constraints. • Identify the source of all business rules, and ensure that each business rule is justified, dated, and signed off by an approving authority. DATA MODELING Naming conventions: All names should be limited in length (database-dependent size). • Entity names: • Should be nouns that are familiar to business and should be short and meaningful • Should document abbreviations, synonyms, and aliases for each entity • Should be unique within the model • For composite entities, may include a combination of abbreviated names of the entities linked through the composite entity • Attribute names: • Should be unique within the entity • Should use the entity abbreviation as a prefix • Should be descriptive of the characteristic • Should use suffixes such as _ID, _NUM, or _CODE for the PK attribute • Should not be a reserved word • Should not contain spaces or special characters such as @, !, or & • Relationship names: • Should be active or passive verbs that clearly indicate the nature of the relationship Entities: • Each entity should represent a single subject. • Each entity should represent a set of distinguishable entity instances. • All entities should be in 3NF or higher. Any entities below 3NF should be justified. • The granularity of the entity instance should be clearly defined. • The PK should be clearly defined and support the selected data granularity. Attributes: • Should be simple and single-valued (atomic data) • Should document default values, constraints, synonyms, and aliases • Derived attributes should be clearly identified and include source(s) • Should not be redundant unless this is required for transaction accuracy, performance, or maintaining a history • Nonkey attributes must be fully dependent on the PK attribute Relationships: • Should clearly identify relationship participants • Should clearly define participation, connectivity, and document cardinality ER model: • Should be validated against expected processes: inserts, updates, and deletions • Should evaluate where, when, and how to maintain a history • Should not contain redundant relationships except as required (see attributes) • Should minimize data redundancy to ensure single-place updates • Should conform to the minimal data rule: All that is needed is there, and all that is there is needed. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 234 Part 2 Design Concepts Summary • Normalization is a technique used to design tables in which data redundancies are minimized. The first three normal forms (1NF, 2NF, and 3NF) are the most com- mon. From a structural point of view, higher normal forms are better than lower normal forms because higher normal forms yield relatively fewer data redundancies in the database. Almost all business designs use 3NF as the ideal normal form. A special, more restricted 3NF known as Boyce-Codd normal form, or BCNF, is also used. • A table is in 1NF when all key attributes are defined and all remaining attributes are dependent on the primary key. However, a table in 1NF can still contain both par- tial and transitive dependencies. A partial dependency is one in which an attribute is functionally dependent on only a part of a multiattribute primary key. A transi- tive dependency is one in which an attribute is functionally dependent on another nonkey attribute. A table with a single-attribute primary key cannot exhibit partial dependencies. • A table is in 2NF when it is in 1NF and contains no partial dependencies. Therefore, a 1NF table is automatically in 2NF when its primary key is based on only a single attribute. A table in 2NF may still contain transitive dependencies. • A table is in 3NF when it is in 2NF and contains no transitive dependencies. Given that definition, the Boyce-Codd normal form (BCNF) is merely a special 3NF case in which all determinant keys are candidate keys. When a table has only a single candi- date key, a 3NF table is automatically in BCNF. • A table that is not in 3NF may be split into new tables until all of the tables meet the 3NF requirements. • Normalization is an important part—but only a part—of the design process. As entities and attributes are defined during the ER modeling process, subject each entity (set) to normalization checks and form new entities (sets) as required. Incorporate the normalized entities into the ERD and continue the iterative ER process until all entities and their attributes are defined and all equivalent tables are in 3NF. • A table in 3NF might contain multivalued dependencies that produce either numer- ous null values or redundant data. Therefore, it might be necessary to convert a 3NF table to the fourth normal form (4NF) by splitting the table to remove the multivalued dependencies. Thus, a table is in 4NF when it is in 3NF and contains no multivalued dependencies. • The larger the number of tables, the more additional I/O operations and processing logic you need to join them. Therefore, tables are sometimes denormalized to yield less I/O in order to increase processing speed. Unfortunately, with larger tables, you pay for the increased processing speed by making the data updates less efficient, by making indexing more cumbersome, and by introducing data redundancies that are likely to yield data anomalies. In the design of production databases, use denormal- ization sparingly and cautiously. • The data-modeling checklist provides a way for the designer to check that the ERD meets a set of minimum requirements. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 1. What is normalization? 2. When is a table in 1NF? 3. When is a table in 2NF? 4. When is a table in 3NF? 5. When is a table in BCNF? 6. Given the dependency diagram shown in Figure Q6.6, answer Items 6a−6c. a. Identify and discuss each of the indicated dependencies. b. Create a database whose tables are at least in 2NF, showing the dependency dia- grams for each table. c. Create a database whose tables are at least in 3NF, showing the dependency dia- grams for each table. 7. The dependency diagram in Figure Q6.7 indicates that authors are paid royalties for each book they write for a publisher. The amount of the royalty can vary by author, by book, and by edition of the book. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 235 FIGUREQ6.6 DEPENDENCYDIAGRAMFORQUESTION6 C1 C2 C3 C4 C5 Key Term atomic attribute atomicity Boyce-Codd normal form (BCNF) denormalization dependency diagram determinant first normal form (1NF) fourth normal form (4NF) granularity key attribute nonkey attribute nonprime attribute normalization partial dependency prime attribute repeating group second normal form (2NF) third normal form (3NF) transitive dependency Online Content Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com. Review Questions 236 Part 2 Design Concepts FIGURE Q6.7 BOOK ROYALTY DEPENDENCY DIAGRAM ISBN BookTitle Author_Num LastName Publisher Royalty Edition a. Based on the dependency diagram, create a database whose tables are at least in 2NF, showing the dependency diagram for each table. b. Create a database whose tables are at least in 3NF, showing the dependency diagram for each table. 8. The dependency diagram in Figure Q6.8 indicates that a patient can receive many prescriptions for one or more medicines over time. Based on the dependency dia- gram, create a database whose tables are in at least 2NF, showing the dependency diagram for each table. 9. What is a partial dependency? With what normal form is it associated? 10. What three data anomalies are likely to be the result of data redundancy? How can such anomalies be eliminated? 11. Define and discuss the concept of transitive dependency. 12. What is a surrogate key, and when should you use one? 13. Whyisatablewhoseprimarykeyconsistsofasingleattributeautomaticallyin2NF when it is in 1NF? 14. Howwouldyoudescribeaconditioninwhichoneattributeisdependentonanother attribute when neither attribute is part of the primary key? 15. Supposesomeonetellsyouthatanattributethatispartofacompositeprimarykey is also a candidate key. How would you respond to that statement? 16. A table is in normal form when it is in and there are no transitive dependencies. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE Q6.8 PRESCRIPTION DEPENDENCY DIAGRAM MedName PatientID Date RefillsAllowed PatientName Dosage ShelfLife 1. Using the descriptions of the attributes given in the figure, convert the ERD shown in Figure P6.1 into a dependency diagram that is in at least 3NF. 2. Using the descriptions of the attributes given in the figure, convert the ERD shown in Figure P6.2 into a dependency diagram that is in at least 3NF. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 237 FIGUREP6.1 APPOINTMENTERDFORPROBLEM1 FIGUREP6.2 PRESENTATIONERDFORPROBLEM2 Problems 238 Part 2 Design Concepts 3. Using the INVOICE table structure shown in Table P6.3, do the following: TABLE P6.3 ATTRIBUTE NAME SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE INV_NUM 211347 211347 211347 211348 211349 PROD_NUM AA-E3422QW QD-300932X RU-995748G AA-E3422QW GH-778345P SALE_DATE 15-Jan-2016 15-Jan-2016 15-Jan-2016 15-Jan-2016 16-Jan-2016 PROD_LABEL Rotary sander 0.25-in. drill bit Band saw Rotary sander Power drill VEND_CODE 211 211 309 211 157 VEND_NAME NeverFail, Inc. NeverFail, Inc. BeGood, Inc. NeverFail, Inc. ToughGo, Inc. QUANT_SOLD 1 8 1 2 1 PROD_PRICE $49.95 $3.45 $39.99 $49.95 $87.75 Note a. Write the relational schema, draw its dependency diagram, and identify all dependencies, including all partial and transitive dependencies. You can assume that the table does not contain repeating groups and that an invoice number ref- erences more than one product. (Hint: This table uses a composite primary key.) b. Remove all partial dependencies, write the relational schema, and draw the new dependency diagrams. Identify the normal forms for each table structure you created. You can assume that any given product is supplied by a single vendor, but a vendor can supply many products. Therefore, it is proper to conclude that the following dependency exists: PROD_NUM → PROD_LABEL, PROD_PRICE, VEND_CODE, VEND_NAME (Hint: Your actions should produce three dependency diagrams.) c. Remove all transitive dependencies, write the relational schema, and draw the new dependency diagrams. Also identify the normal forms for each table struc- ture you created. d. Draw the Crow’s Foot ERD. 4. Using the STUDENT table structure shown in Table P6.4, do the following: a. Write the relational schema and draw its dependency diagram. Identify all dependencies, including all transitive dependencies. b. Write the relational schema and draw the dependency diagram to meet the 3NF requirements to the greatest practical extent possible. If you believe that practical considerations dictate using a 2NF structure, explain why your decision to retain 2NF is appropriate. If necessary, add or modify attributes to create appropriate determinants and to adhere to the naming conventions. c. Using the results of Problem 4, draw the Crow’s Foot ERD. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 239 TABLE P6.4 ATTRIBUTE NAME SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE STU_NUM 211343 200128 199876 198648 223456 STU_LNAME Stephanos Smith Jones Ortiz McKulski STU_MAJOR Accounting Accounting Marketing Marketing Statistics DEPT_CODE ACCT ACCT MKTG MKTG MATH DEPT_NAME Accounting Accounting Marketing Marketing Mathematics DEPT_PHONE 4356 4356 4378 4378 3420 COLLEGE_NAME Business Admin Business Admin Business Admin Business Admin Arts & Sciences ADVISOR_LNAME Grastrand Grastrand Gentry Tillery Chen ADVISOR_OFFICE T201 T201 T228 T356 J331 ADVISOR_BLDG Torre Building Torre Building Torre Building Torre Building Jones Building ADVISOR_PHONE 2115 2115 2123 2159 3209 STU_GPA 3.87 2.78 2.31 3.45 3.58 STU_HOURS 75 45 117 113 87 STU_CLASS Junior Sophomore Senior Senior Junior Note 5. To keep track of office furniture, computers, printers, and other office equipment, the FOUNDIT Company uses the table structure shown in Table P6.5. Although the completed student hours (STU_HOURS) do determine the student classifi- cation (STU_CLASS), this dependency is not as obvious as you might initially assume it to be. For example, a student is considered a junior if the student has completed between 61 and 90 credit hours. TABLE P6.5 ATTRIBUTE NAME SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE ITEM_ID 231134-678 342245-225 254668-449 ITEM_LABEL HP DeskJet 895Cse HP Toner DT Scanner ROOM_NUMBER 325 325 123 BLDG_CODE NTC NTC CSF BLDG_NAME Nottooclear Nottooclear Canseefar BLDG_MANAGER I. B. Rightonit I. B. Rightonit May B. Next a. Given that information, write the relational schema and draw the dependency diagram. Make sure that you label the transitive and/or partial dependencies. b. Write the relational schema and create a set of dependency diagrams that meet 3NF requirements. Rename attributes to meet the naming conventions, and cre- ate new entities and attributes as necessary. c. Draw the Crow’s Foot ERD. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 240 Part 2 Design Concepts 6. The table structure shown in Table P6.6 contains many unsatisfactory components and characteristics. For example, there are several multivalued attributes, naming conventions are violated, and some attributes are not atomic. TABLE P6.6 EMP_NUM 1003 1018 1019 1023 EMP_LNAME Willaker Smith McGuire McGuire EMP_EDUCATION BBA, MBA BBA BS, MS, Ph.D. JOB_CLASS SLS SLS JNT DBA EMP_DEPENDENTS Gerald (spouse), Mary (daughter), John (son) JoAnne (spouse) George (spouse) Jill (daughter) DEPT_CODE MKTG MKTG SVC INFS DEPT_NAME Marketing Marketing General Service Info. Systems DEPT_MANAGER Jill H. Martin Jill H. Martin Hank B. Jones Carlos G. Ortez EMP_TITLE Sales Agent Sales Agent Janitor DB Admin EMP_DOB 23-Dec-1968 28-Mar-1979 18-May-1982 20-Jul-1959 EMP_HIRE_DATE 14-Oct-1997 15-Jan-2006 21-Apr-2003 15-Jul-1999 EMP_TRAINING L1, L2 L1 L1 L1, L3, L8, L15 EMP_BASE_SALARY $38,255.00 $30,500.00 $19,750.00 $127,900.00 EMP_COMMISSION_RATE 0.015 0.010 a. Given the structure shown in Table P6.6, write the relational schema and draw its dependency diagram. Label all transitive and/or partial dependencies. b. Draw the dependency diagrams that are in 3NF. (Hint: You might have to create a few new attributes. Also make sure that the new dependency diagrams contain attributes that meet proper design criteria; i.e., make sure there are no multival- ued attributes, that the naming conventions are met, and so on.) c. Draw the relational diagram. d. Draw the Crow’s Foot ERD. 7. Suppose you are given the following business rules to form the basis for a database design. The database must enable the manager of a company dinner club to mail invitations to the club’s members, to plan the meals, to keep track of who attends the dinners, and so on. • Each dinner serves many members, and each member may attend many dinners. • A member receives many invitations, and each invitation is mailed to many members. • A dinner is based on a single entree, but an entree may be used as the basis for many dinners. For example, a dinner may be composed of a fish entree, rice, and corn, or the dinner may be composed of a fish entree, a baked potato, and string beans. Because the manager is not a database expert, the first attempt at creating the data- base uses the structure shown in Table P6.7. a. Given the table structure illustrated in Table P6.7, write the relational schema and draw its dependency diagram. Label all transitive and/or partial dependen- cies. (Hint: This structure uses a composite primary key.) Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 241 TABLE P6.7 ATTRIBUTE NAME SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE MEMBER_NUM 214 235 214 MEMBER_NAME Alice B. VanderVoort Gerald M. Gallega Alice B. VanderVoort MEMBER_ADDRESS 325 Meadow Park 123 Rose Court 325 Meadow Park MEMBER_CITY Murkywater Highlight Murkywater MEMBER_ZIPCODE 12345 12349 12345 INVITE_NUM 8 9 10 INVITE_DATE 23-Feb-2016 12-Mar-2016 23-Feb-2016 ACCEPT_DATE 27-Feb-2016 15-Mar-2016 27-Feb-2016 DINNER_DATE 15-Mar-2016 17-Mar-2016 15-Mar-2016 DINNER_ATTENDED Yes Yes No DINNER_CODE DI5 DI5 DI2 DINNER_DESCRIPTION Glowing Sea Delight Glowing Sea Delight Ranch Superb ENTREE_CODE EN3 EN3 EN5 ENTREE_DESCRIPTION Stuffed crab Stuffed crab Marinated steak DESSERT_CODE DE8 DE5 DE2 DESSERT_DESCRIPTION Chocolate mousse with raspberry sauce Cherries jubilee Apple pie with honey crust b. Break up the dependency diagram you drew in Problem 7a to produce depen- dency diagrams that are in 3NF, and write the relational schema. (Hint: You might have to create a few new attributes. Also, make sure that the new dependency diagrams contain attributes that meet proper design criteria; i.e., make sure there are no multivalued attributes, that the naming conventions are met, and so on.) c. Using the results of Problem 7b, draw the Crow’s Foot ERD. 8. Use the dependency diagram shown in Figure P6.8 to work the following problems. a. Break up the dependency diagram shown in Figure P6.8 to create two new dependency diagrams: one in 3NF and one in 2NF. b. Modify the dependency diagrams you created in Problem 8a to produce a set of dependency diagrams that are in 3NF. (Hint: One of your dependency diagrams should be in 3NF but not in BCNF.) c. Modify the dependency diagrams you created in Problem 8b to produce a collec- tion of dependency diagrams that are in 3NF and BCNF. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGUREP6.8 INITIALDEPENDENCYDIAGRAMFORPROBLEM8 ABCDEFG 242 Part 2 Design Concepts 9. Suppose you have been given the table structure and data shown in Table P6.9, which was imported from an Excel spreadsheet. The data reflects that a professor can have multiple advisees, can serve on multiple committees, and can edit more than one journal. TABLE P6.9 ATTRIBUTE NAME SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE EMP_NUM 123 104 118 PROF_RANK Professor Asst. Professor Assoc. Professor Assoc. Professor EMP_NAME Ghee Rankin Ortega Smith DEPT_CODE CIS CHEM CIS ENG DEPT_NAME Computer Info. Systems Chemistry Computer Info. Systems English PROF_OFFICE KDD-567 BLF-119 KDD-562 PRT-345 ADVISEE 1215, 2312, 3233, 2218, 2098 3102, 2782, 3311, 2008, 2876, 2222, 3745, 1783, 2378 2134, 2789, 3456, 2002, 2046, 2018, 2764 2873, 2765, 2238, 2901, 2308 COMMITTEE_CODE PROMO, TRAF, APPL, DEV DEV SPR, TRAF PROMO, SPR, DEV JOURNAL_CODE JMIS, QED, JMGT JCIS, JMGT Given the information in Table P6.9: a. Draw the dependency diagram. b. Identify the multivalued dependencies. c. Create the dependency diagrams to yield a set of table structures in 3NF. d. Eliminate the multivalued dependencies by converting the affected table struc- tures to 4NF. e. Draw the Crow’s Foot ERD to reflect the dependency diagrams you drew in Problem 9c. (Note: You might have to create additional attributes to define the proper PKs and FKs. Make sure that all of your attributes conform to the naming conventions.) 10. Themanagerofaconsultingfirmhasaskedyoutoevaluateadatabasethatcontains the table structure shown in Table P6.10. Table P6.10 was created to enable the manager to match clients with consultants. The objective is to match a client within a given region with a consultant in that region and to make sure that the client’s need for specific consulting services is prop- erly matched to the consultant’s expertise. For example, if the client needs help with database design and is located in the Southeast, the objective is to make a match with a consultant who is located in the Southeast and whose expertise is in database design. (Although the consulting company manager tries to match consultant and client locations to minimize travel expense, it is not always possible to do so.) The following basic business rules are maintained: • Each client is located in one region. • A region can contain many clients. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 6 Normalization of Database Tables 243 • Each contract might require the services of many consultants. • Each consultant can work on many contracts. • A client can sign more than one contract, but each contract is signed by only one client. • Each contract might cover multiple consulting classifications. (For example, a contract may list consulting services in database design and networking.) TABLE P6.10 ATTRIBUTE NAME SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE CLIENT_NUM 298 289 289 CLIENT_NAME Marianne R. Brown James D. Smith James D. Smith CLIENT_REGION Midwest Southeast Southeast CONTRACT_DATE 10-Feb-2016 15-Feb-2016 12-Mar-2016 CONTRACT_NUMBER 5841 5842 5843 CONTRACT_AMOUNT $2,985,000.00 $670,300.00 $1,250,000.00 CONSULT_CLASS_1 Database Administration Internet Services Database Design CONSULT_CLASS_2 Web Applications Database Administration CONSULT_CLASS_3 Network Installation CONSULT_CLASS_4 CONSULTANT_NUM_1 29 34 25 CONSULTANT_NAME_1 Rachel G. Carson Gerald K. Ricardo Angela M. Jamison CONSULTANT_REGION_1 Midwest Southeast Southeast CONSULTANT_NUM_2 56 38 34 CONSULTANT_NAME_2 Karl M. Spenser Anne T. Dimarco Gerald K. Ricardo CONSULTANT_REGION_2 Midwest Southeast Southeast CONSULTANT_NUM_3 22 45 CONSULTANT_NAME_3 Julian H. Donatello Geraldo J. Rivera CONSULTANT_REGION_3 Midwest Southeast CONSULTANT_NUM_4 18 CONSULTANT_NAME_4 Donald Chen CONSULTANT_REGION_4 West • Each consultant is located in one region. • A region can contain many consultants. • Each consultant has one or more areas of expertise (class). For example, a con- sultant might be classified as an expert in both database design and networking. • Each area of expertise (class) can have many consultants. For example, the con- sulting company might employ many consultants who are networking experts. a. Given this brief description of the requirements and the business rules, write the relational schema and draw the dependency diagram for the preceding (and very poor) table structure. Label all transitive and/or partial dependencies. b. Break up the dependency diagram you drew in Problem 10a to produce depen- dency diagrams that are in 3NF and write the relational schema. (Hint: You might Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 244 Part 2 Design Concepts have to create a few new attributes. Also make sure that the new dependency dia- grams contain attributes that meet proper design criteria; that is, make sure there are no multivalued attributes, that the naming conventions are met, and so on.) c. Using the results of Problem 10b, draw the Crow’s Foot ERD. 11. Given the sample records in the CHARTER table shown in Table P6.11, do the following: a. Write the relational schema and draw the dependency diagram for the table structure. Make sure that you label all dependencies. CHAR_PAX indicates the number of passengers carried. The CHAR_MILES entry is based on round-trip miles, including pickup points. (Hint: Look at the data values to determine the nature of the relationships. For example, note that employee Melton has flown two charter trips as pilot and one trip as copilot.) b. Decompose the dependency diagram you drew to solve Problem 11a to create table structures that are in 3NF and write the relational schema. c. Draw the Crow’s Foot ERD to reflect the properly decomposed dependency dia- grams you created in Problem 11b. Make sure the ERD yields a database that can track all of the data shown in Problem 11. Show all entities, relationships, connectivities, optionalities, and cardinalities. TABLE P6.11 ATTRIBUTE NAME SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE SAMPLE VALUE CHAR_TRIP 10232 10233 10234 10235 CHAR_DATE 15-Jan-2016 15-Jan-2016 16-Jan-2016 17-Jan-2016 CHAR_CITY STL MIA TYS ATL CHAR_MILES 580 1,290 524 768 CUST_NUM 784 231 544 784 CUST_LNAME Brown Hanson Bryana Brown CHAR_PAX 5 12 2 5 CHAR_CARGO 235 lbs. 18,940 lbs. 348 lbs. 155 lbs. PILOT Melton Chen Henderson Melton COPILOT Henderson Melton FLT_ENGINEER O’Shaski LOAD_MASTER Benkasi AC_NUMBER 1234Q 3456Y 1234Q 2256W MODEL_CODE PA31-350 CV-580 PA31-350 PA31-350 MODEL_SEATS 10 38 10 10 MODEL_CHG_MILE $2.79 $23.36 $2.79 $2.79 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. PART 3 Advanced Design and Implementation 7 Introduction to Structured Query Language (SQL) 8 Advanced SQL 9 Database Design Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 7 Introduction to Structured Query Language (SQL) In this chapter, you will learn: • The basic commands and functions of SQL • How to use SQL for data administration (to create tables and indexes) • How to use SQL for data manipulation (to add, modify, delete, and retrieve data) • How to use SQL to query a database for useful information Preview In this chapter, you will learn the basics of Structured Query Language (SQL). SQL, which is pronounced S-Q-L or sequel, is composed of commands that enable users to create database and table structures, perform various types of data manipulation and data administration, and query the database to extract useful information. All relational DBMS software supports SQL, and many software vendors have developed extensions to the basic SQL command set. Although it is quite useful and powerful, SQL is not meant to stand alone in the appli- cations arena. Data entry with SQL is possible but awkward, as are data corrections and additions. SQL itself does not create menus, special report forms, overlays, pop-ups, or other features that end users usually expect. Instead, those features are available as vendor-supplied enhancements. SQL focuses on data definition (creating tables and indexes) and data manipulation (adding, modifying, deleting, and retrieving data). This chapter covers these basic functions. In spite of its limitations, SQL is a powerful tool for extracting information and managing data. Data Files and Available Formats CH07_SaleCo P P P P CH07_ConstructCo P P P P CH07_LargeCo P P P P Ch07_Fact P P P P Data Files Available on cengagebrain.com MS Access Oracle MS SQL My SQL MS Access Oracle MS SQL My SQL Note Although you can use the MS Access databases and SQL script files for creating the tables and loading the data supplied online, it is strongly suggested that you create your own database structures so you can practice the SQL commands illustrated in this chapter. How you connect to your database depends on how the software was installed on your computer. Follow the instructions provided by your instructor or school. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 7-1 Introduction to SQL Ideally, a database language allows you to create database and table structures, perform basic data management chores (add, delete, and modify), and perform complex que- ries designed to transform the raw data into useful information. Moreover, a database language must perform such basic functions with minimal user effort, and its com- mand structure and syntax must be easy to learn. Finally, it must be portable; that is, it must conform to some basic standard so a person does not have to relearn the basics when moving from one RDBMS to another. SQL meets those ideal database language requirements well. SQL functions fit into two broad categories: • It is a data definition language (DDL). SQL includes commands to create database objects such as tables, indexes, and views, as well as commands to define access rights to those database objects. Some common data definition commands you will learn are listed in Table 7.1. • It is a data manipulation language (DML). SQL includes commands to insert, update, delete, and retrieve data within the database tables. The data manipulation commands you will learn in this chapter are listed in Table 7.2. TABLE 7.1 SQL DATA DEFINITION COMMANDS COMMAND OR OPTION DESCRIPTION Chapter 7 Introduction to Structured Query Language (SQL) 247 CREATE SCHEMA AUTHORIZATION Creates a database schema CREATE TABLE Creates a new table in the user’s database schema NOT NULL Ensures that a column will not have null values UNIQUE Ensures that a column will not have duplicate values PRIMARY KEY Defines a primary key for a table FOREIGN KEY Defines a foreign key for a table DEFAULT Defines a default value for a column (when no value is given) CHECK Validates data in an attribute CREATE INDEX Creates an index for a table CREATE VIEW Creates a dynamic subset of rows and columns from one or more tables (see Chapter 8, Advanced SQL) ALTER TABLE Modifies a table’s definition (adds, modifies, or deletes attributes or constraints) CREATE TABLE AS Creates a new table based on a query in the user’s database schema DROP TABLE Permanently deletes a table (and its data) DROP INDEX Permanently deletes an index DROP VIEW Permanently deletes a view SQL is relatively easy to learn. Its basic command set has a vocabulary of fewer than 100 words. Better yet, SQL is a nonprocedural language: you merely command what is to be done; you do not have to worry about how. For example, a single command creates the complex table structures required to store and manipulate data successfully; end users and programmers do not need to know the physical data storage format or the complex activities that take place when a SQL command is executed. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 248 Part 3 Advanced Design and Implementation TABLE 7.2 SQL DATA MANIPULATION COMMANDS COMMAND OR OPTION INSERT SELECT WHERE GROUP BY HAVING ORDER BY UPDATE DELETE COMMIT ROLLBACK Comparison operators =, <, >, <=, >=, <>, != Logical operators AND/OR/NOT
Special operators
BETWEEN IS NULL LIKE
IN
EXISTS
DISTINCT
Aggregate functions COUNT
MIN
MAX
SUM
AVG
DESCRIPTION
Inserts row(s) into a table
Selects attributes from rows in one or more tables or views Restricts the selection of rows based on a conditional expression Groups the selected rows based on one or more attributes Restricts the selection of grouped rows based on a condition Orders the selected rows based on one or more attributes Modifies an attribute’s values in one or more table’s rows Deletes one or more rows from a table
Permanently saves data changes
Restores data to its original values
Used in conditional expressions
Used in conditional expressions
Used in conditional expressions
Checks whether an attribute value is within a range
Checks whether an attribute value is null
Checks whether an attribute value matches a given string pattern Checks whether an attribute value matches any value within a value list Checks whether a subquery returns any rows
Limits values to unique values
Used with SELECT to return mathematical summaries on columns Returns the number of rows with non-null values for a given column Returns the minimum attribute value found in a given column
Returns the maximum attribute value found in a given column
Returns the sum of all values for a given column
Returns the average of all values for a given column
The American National Standards Institute (ANSI) prescribes a standard SQL. The ANSI SQL standards are also accepted by the International Organization for Standard- ization (ISO), a consortium composed of national standards bodies of more than 150 countries. Although adherence to the ANSI/ISO SQL standard is usually required in commercial and government contract database specifications, many RDBMS vendors add their own special enhancements. Consequently, it is seldom possible to move a SQL- based application from one RDBMS to another without making some changes.
However, even though there are several different SQL “dialects,” their differences are minor. Whether you use Oracle, Microsoft SQL Server, MySQL, IBM’s DB2, Microsoft Access, or any other well-established RDBMS, a software manual should be sufficient to get you up to speed if you know the material presented in this chapter.
At the heart of SQL is the query. In Chapter 1, Database Systems, you learned that a query is a spur-of-the-moment question. Actually, in the SQL environment, the word query covers both questions and actions. Most SQL queries are used to answer questions
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such as these: “What products currently held in inventory are priced over $100, and what is the quantity on hand for each of those products?” or “How many employees have been hired since January 1, 2016, by each of the company’s departments?” However, many SQL queries are used to perform actions such as adding or deleting table rows or chang- ing attribute values within tables. Still other SQL queries create new tables or indexes. In short, for a DBMS, a query is simply a SQL statement that must be executed. However, before you can use SQL to query a database, you must define the database environment for SQL with its data definition commands.
7-2 Data Definition Commands
Before you examine the SQL syntax for creating and defining tables and other elements, first examine a simple database model and the database tables that form the basis for the many SQL examples you will explore in this chapter.
7-2a The Database Model
A simple database composed of the following tables is used to illustrate the SQL commands in this chapter: CUSTOMER, INVOICE, LINE, PRODUCT, and VENDOR. This database model is shown in Figure 7.1.
The database model in Figure 7.1 reflects the following business rules:
• A customer may generate many invoices. Each invoice is generated by one customer.
• An invoice contains one or more invoice lines. Each invoice line is associated with one invoice.
• Each invoice line references one product. A product may be found in many invoice lines. (You can sell more than one hammer to more than one customer.)
Online Content
The database model in Figure 7.1 is implemented in the Microsoft Access Ch07_SaleCo database, which is available at www.cengagebrain.com. (This database contains a few additional tables that are not reflected in Figure 7.1. These tables are used for discussion purposes only.)
Chapter 7 Introduction to Structured Query Language (SQL) 249
FIGURE 7.1 THE DATABASE MODEL
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250 Part 3
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• A vendor may supply many products. Some vendors do not yet supply products. For example, a vendor list may include potential vendors.
• If a product is vendor-supplied, it is supplied by only a single vendor.
• Some products are not supplied by a vendor. For example, some products may be
produced in-house or bought on the open market.
As you can see in Figure 7.1, the database model contains many tables. However, to illustrate the initial set of data definition commands, the focus of attention will be the PRODUCT and VENDOR tables. You will have the opportunity to use the remaining tables later in this chapter and in the Problems section.
To give you a point of reference for understanding the effect of the SQL queries, the con- tents of the PRODUCT and VENDOR tables are listed in Figure 7.2. In the tables, note the following features, which correspond to the business rules reflected in the ERD shown in Figure 7.1:
• The VENDOR table contains vendors who are not referenced in the PRODUCT table. Database designers note that possibility by saying that PRODUCT is optional to VENDOR; a vendor may exist without a reference to a product. You examined such optional relationships in detail in Chapter 4, Entity Relationship (ER) Modeling.
• Existing V_CODE values in the PRODUCT table must (and do) have a match in the VENDOR table to ensure referential integrity.
• A few products are supplied factory-direct, a few are made in-house, and a few may have been bought in a warehouse sale. In other words, a product is not necessarily supplied by a vendor. Therefore, VENDOR is optional to PRODUCT.
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FIGURE 7.2 THE VENDOR AND PRODUCT TABLES
Table name: VENDOR Database name: Ch07_SaleCo
Table name: PRODUCT

A few of the conditions just described were made for the sake of illustrating specific SQL features. For example, null V_CODE values were used in the PRODUCT table to illustrate how you can track such nulls using SQL.
7-2b Creating The Database
Before you can use a new RDBMS, you must complete two tasks: create the database structure and create the tables that will hold the end-user data. To complete the first task, the RDBMS creates the physical files that will hold the database. When you create a new database, the RDBMS automatically creates the data dictionary tables in which to store the metadata and creates a default database administrator. Creating the physical files that will hold the database means interacting with the operating system and the file systems supported by the operating system. Therefore, creating the database structure is the one feature that tends to differ substantially from one RDBMS to another. However, it is relatively easy to create a database structure, regardless of which RDBMS you use.
If you use Microsoft Access, creating the database is simple: start Access, click the FILE tab, click New in the left pane, and then click Blank desktop database in the right pane. Specify the folder in which you want to store the database, and then name the database. However, if you work in a database environment typically used by larger orga- nizations, you will probably use an enterprise RDBMS such as Oracle, MS SQL Server, MySQL, or DB2. Given their security requirements and greater complexity, creating a database with these products is a more elaborate process. (See Appendix N, Creating a New Database Using Oracle 11g, for specific instructions to create a database structure in Oracle.)
With the exception of creating the database, most RDBMS vendors use SQL that devi- ates little from the ANSI standard SQL. For example, most RDBMSs require each SQL command to end with a semicolon. However, some SQL implementations do not use a semicolon. Important syntax differences among implementations will be highlighted in the Note boxes in this chapter.
If you are using an enterprise RDBMS, you must be authenticated by the RDBMS before you can start creating tables. Authentication is the process the DBMS uses to verify that only registered users access the database. To be authenticated, you must log on to the RDBMS using a user ID and a password created by the database administrator. In an enterprise RDBMS, every user ID is associated with a database schema.
7-2c The Database Schema
In the SQL environment, a schema is a logical group of database objects—such as tables and indexes—that are related to each other. Usually, the schema belongs to a single user or application. A single database can hold multiple schemas that belong to different users or applications. Schemas are useful in that they group tables by owner (or function) and enforce a first level of security by allowing each user to see only the tables that belong to that user.
ANSI SQL standards define a command to create a database schema: CREATE SCHEMA AUTHORIZATION {creator};
Therefore, if the creator is JONES, the following command is used: CREATE SCHEMA AUTHORIZATION JONES;
Most enterprise RDBMSs support that command. However, the command is seldom used directly—that is, from the command line. (When a user is created,
authentication
The process through which a DBMS verifies that only registered users can access the database.
schema
A logical grouping of database objects, such as tables, indexes, views, and queries, that are related to each other. Usually, a schema belongs to a single user or application.
Chapter 7 Introduction to Structured Query Language (SQL) 251
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252 Part 3
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Note
the DBMS automatically assigns a schema to that user.) When the DBMS is used, the CREATE SCHEMA AUTHORIZATION command must be issued by the user who owns the schema. That is, if you log on as JONES, you can only use CREATE SCHEMA AUTHORIZATION JONES.
For most RDBMSs, the CREATE SCHEMA AUTHORIZATION command is optional, which is why this chapter focuses on the ANSI SQL commands required to create and manipulate tables.
7-2d DataTypes
In the data dictionary in Table 7.3, note the data types selected. Keep in mind that data-
type selection is usually dictated by the nature and intended use of the data. For example:
• P_PRICE clearly requires some kind of numeric data type; defining it as a character field is not acceptable.
• Just as clearly, a vendor name is an obvious candidate for a character data type. For example, VARCHAR(35) fits well because vendor names are variable-length character strings, and in this case, such strings may be up to 35 characters long.
• At first glance, it might seem logical to select a numeric data type for V_AREACODE because it contains only digits. However, adding and subtracting area codes does not yield meaningful results. Therefore, selecting a character data type is more appro- priate. This is true for many common attributes found in business data models. For example, even though zip codes contain all digits, they must be defined as character data because some zip codes begin with the digit zero (0), and a numeric data type would cause the leading zero to be dropped.
• U.S. state abbreviations are always two characters, so CHAR(2) is a logical choice.
• Selecting P_INDATE to be a (Julian) DATE field rather than a character field is desir- able because Julian dates allow you to make simple date comparisons and perform date arithmetic. For instance, if you have used DATE fields, you can determine the number of days between dates.
If you use DATE fields, you can also determine a future date using a simple command. For example, you can determine the date that is 60 days from a given P_INDATE by using P_INDATE + 60 in most DBMSs. MySQL requires a function for adding dates. For example, the AddDate() function used in “AddDate(P_INDATE, 60)” determines the date that is 60 days from the P_INDATE. Or, you can use the RDBMS’s system date— SYSDATE in Oracle, SYSDATE() or NOW() in MySQL, GETDATE() in MS SQL Server, and Date() in Access—to answer questions such as “What will be the date 60 days from today?” For example, you might use SYSDATE + 60 in Oracle, AddDate(SYSDATE(), 60) in MySQL, GETDATE() + 60 in MS SQL Server, or Date() + 60 in Access.
Oracle uses DATE data types to store complete dates, that is, a date and time. Access uses Date/Time as the data type to store these types of values. MySQL and MS SQL Server use the DATE data type to store only dates without a time component. Stor- ing a complete date with time component in MySQL or MS SQL Server requires the DATETIME data type.
Date arithmetic capability is particularly useful in billing. Perhaps you want your sys- tem to start charging interest on a customer balance 60 days after the invoice is gener- ated. Such simple date arithmetic would be impossible if you used a character data type.
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Chapter 7 Introduction to Structured Query Language (SQL) 253
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TABLE 7.3
DATA DICTIONARY FOR THE CH07_SALECO DATABASE
TABLE NAME
ATTRIBUTE NAME
CONTENTS
TYPE
FORMAT RANGE
REQUIRED PK OR FK
FK REFERENCED TABLE
PRODUCT
P_CODE P_DESCRIPT
Y PK
VENDOR
V_CODE V_NAME V_CONTACT V_AREACODE V_PHONE V_STATE V_ORDER
Vendor code Vendor name Contact person Area code Phone number State
INTEGER VARCHAR(35) VARCHAR(25) CHAR(3) CHAR(8) CHAR(2) CHAR(1)
##### 1000–9999
FK
PK
CHAR VARCHAR NUMBER
= Foreign key
= Primary key
= Fixed-length character data, 1 to 255 characters
= Variable-length character data, 1 to 2,000 characters. VARCHAR is automatically converted to VARCHAR2 in Oracle.
= Numeric data. NUMBER(9,2) is used to specify numbers that have two decimal places and are up to nine digits long, including the decimal places.
P_INDATE P_QOH P_MIN P_PRICE P_DISCOUNT V_CODE
Stocking date Units available Minimum units Product price Discount rate Vendor code
DATE SMALLINT SMALLINT NUMBER(8,2) NUMBER(5,2) INTEGER
DD-MON-YYYY NA Y #### 0–9999 Y #### 0–9999 Y ####.## 0.00–9999.00 Y 0.## 0.00–0.20 Y ### 100–999
FK
VENDOR
Product code
VARCHAR(10) VARCHAR(35)
XXXXXXXXXX NA
Xxxxxxxxxxxx NA Y
Product description
Previous order
Xxxxxxxxxxxxxx NA Y 999 NA Y 999–9999 NA Y XX NA Y X Y or N Y
Some RDBMSs permit the use of a MONEY or a CURRENCY data type.
= Numeric data. DBMSs that do not support the NUMBER data type typically use NUMERIC instead. = Integer values only. INT is automatically converted to NUMBER in Oracle.
NUMERIC
INT
SMALLINT = Small integer values only. SMALLINT is automatically converted to NUMBER in Oracle.
DATE formats vary. Commonly accepted formats are DD-MON-YYYY, DD-MON-YY, MM/DD/YYYY, and MM/DD/YY.
*Not all the ranges shown here will be illustrated in this chapter. However, you can use these constraints to practice writing your own.
Y PK Xxxxxxxxxxxxxx NA Y

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TABLE 7.4
Data-type selection sometimes requires professional judgment. For example, you must make a decision about the V_CODE’s data type as follows:
• If you want the computer to generate new vendor codes by adding 1 to the largest recorded vendor code, you must classify V_CODE as a numeric attribute. (You can- not perform mathematical procedures on character data.) The designation INTEGER will ensure that only the counting numbers (integers) can be used. Most SQL imple- mentations also permit the use of SMALLINT for integer values up to six digits.
• If you do not want to perform mathematical procedures based on V_CODE, you should classify it as a character attribute, even though it is composed entirely of num- bers. When there is no need to perform mathematical procedures on the attribute, store it as a character attribute.
The first option is used to demonstrate the SQL procedures in this chapter.
When you define the attribute’s data type, you must pay close attention to the expected use of the attributes for sorting and data-retrieval purposes. For example, in a real estate application, an attribute that represents the numbers of bathrooms in a home (H_BATH_NUM) could be assigned the CHAR(3) data type because the application will probably not do any addition, multiplication, or division with the number of bathrooms. Based on the CHAR(3) data-type definition, valid H_BATH_NUM values would be ‘2’,’1′,’2.5′,’10’. However, this data-type deci- sion creates potential problems. For example, if an application sorts the homes by number of bathrooms, a query would “see” the value ’10’ as less than ‘2’, which is clearly incorrect. So, you must consider the expected use of the data to properly define the attribute data type.
The data dictionary in Table 7.3 contains only a few of the data types supported by SQL. For teaching purposes, the selection of data types is limited to ensure that almost any RDBMS can be used to implement the examples. If your RDBMS is fully compliant with ANSI SQL, it will support many more data types than those shown in Table 7.4. Also, many RDBMSs support data types beyond the ones specified in ANSI SQL.
SOME COMMON SQL DATA TYPES
DATA TYPE FORMAT COMMENTS
Numeric
NUMBER(L,D) or NUMERIC(L,D)
The declaration NUMBER(7,2) or NUMERIC(7,2) indicates that numbers will be stored with two decimal places and may be up to seven digits long, including the sign and the decimal place (for example, 12.32 or −134.99).
INTEGER
May be abbreviated as INT. Integers are (whole) counting numbers, so they cannot be used if you want to store numbers that require decimal places.
SMALLINT
Like INTEGER but limited to integer values up to six digits. If your integer values are relatively small, use SMALLINT instead of INT.
DECIMAL(L,D)
Like the NUMBER specification, but the storage length is a minimum specification. That is, greater lengths are acceptable, but smaller ones are not. DECIMAL(9,2), DECIMAL(9), and DECIMAL are all acceptable.
Character
CHAR(L)
Fixed-length character data for up to 255 characters. If you store strings that are
not as long as the CHAR parameter value, the remaining spaces are left unused. Therefore, if you specify CHAR(25), strings such as Smith and Katzenjammer are each stored as 25 characters. However, a U.S. area code is always three digits long, so CHAR(3) would be appropriate if you wanted to store such codes.
VARCHAR(L) or VARCHAR2(L)
Variable-length character data. The designation VARCHAR2(25) or VARCHAR(25) will let you store characters up to 25 characters long. However, unlike CHAR, VARCHAR will not leave unused spaces. Oracle automatically converts VARCHAR to VARCHAR2.
Date
DATE
Stores dates in the Julian date format.
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In addition to the data types shown in Table 7.4, SQL supports several other data types, including TIME, TIMESTAMP, REAL, DOUBLE, and FLOAT, and intervals, such as INTERVAL DAY TO HOUR. Many RDBMSs have also expanded the list to include other types of data, such as LOGICAL, CURRENCY, and AutoNumber (Access). How- ever, because this chapter is designed to introduce the basics of SQL, the discussion is limited to the data types summarized in Table 7.4.
7-2e Creating Table Structures
Now you are ready to implement the PRODUCT and VENDOR table structures with the
help of SQL, using the CREATE TABLE syntax shown next. CREATE TABLE tablename (
Online Content
All the SQL commands used in this chapter are located in script files at www.cengagebrain.com. You can copy and paste the SQL commands into your SQL program. Script files are provided for Oracle, MS SQL Server, and MySQL users.
column1 column2 PRIMARY KEY FOREIGN KEY CONSTRAINT
data type data type (column1 (column1 constraint ] );
[constraint] [,
[constraint] ] [,
[, column2]) ] [,
[, column2]) REFERENCES tablename] [,
To make the SQL code more readable, most SQL programmers use one line per col- umn (attribute) definition. In addition, spaces are used to line up the attribute character- istics and constraints. Finally, both table and attribute names are fully capitalized. Those conventions are used in the following examples that create VENDOR and PRODUCT tables and subsequent tables throughout the book.
Note
Chapter 7 Introduction to Structured Query Language (SQL) 255
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CREATE TABLE
A SQL command that creates a table’s structures using the characteristics and attributes given.
SQL Syntax
Syntax notation for SQL commands used in this book:
CAPITALS
italics
{a | b | ..} [……] Tablename Column data type constraint condition columnlist tablelist conditionlist expression
Required SQL command keywords
A parameter provided by the end user (generally required)
A mandatory parameter; use one option from the list separated by |
An optional parameter—anything inside square brackets is optional
The name of a table
The name of an attribute in a table
A valid data-type definition
A valid constraint definition
A valid conditional expression (evaluates to true or false)
One or more column names or expressions separated by commas
One or more table names separated by commas
One or more conditional expressions separated by logical operators
A simple value (such as 76 or Married) or a formula (such as P_PRICE − 10)

256 Part 3
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Note
CREATE TABLE VENDOR (
V_CODE
V_NAME
V_CONTACT V_AREACODE
V_PHONE
V_STATE
V_ORDER
PRIMARY KEY (V_CODE));
NOT NULL NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL,
UNIQUE,
INTEGER VARCHAR(35) VARCHAR(25) CHAR(3) CHAR(8) CHAR(2) CHAR(1)
• Because the PRODUCT table contains a foreign key that references the VENDOR table, create the VENDOR table first. (In fact, the “M” side of a relationship always references the “1” side. Therefore, in a 1:M relationship, you must always create the table for the “1” side first.)
• If your RDBMS does not support the VARCHAR2 and FCHAR format, use CHAR.
• Oracle accepts the VARCHAR data type and automatically converts it to VARCHAR2.
• If your RDBMS does not support SINT or SMALLINT, use INTEGER or INT. If INTEGER is not supported, use NUMBER (Oracle or Access) or NUMERIC (MS SQL Server or MySQL).
• If you use Access, you can use the NUMBER data type, but you cannot use the number delimiters at the SQL level. For example, using NUMBER(8,2) to indicate numbers with up to eight digits with two digits to the right of the decimal place is fine in Oracle, but you cannot use it in Access—you must use NUMBER without the delimiters.
• If your RDBMS does not support primary and foreign key designations or the UNIQUE specification, delete them from the SQL code shown here.
• If you use the PRIMARY KEY designation in Oracle, you do not need the NOT NULL and UNIQUE specifications.
• The ON UPDATE CASCADE clause is part of the ANSI standard, but it may not be supported by your RDBMS. In that case, delete the ON UPDATE CASCADE clause.
CREATE TABLE PRODUCT (
P_CODE
P_DESCRIPT
P_INDATE
P_QOH
P_MIN
P_PRICE
P_DISCOUNT
V_CODE
PRIMARY KEY (P_CODE),
FOREIGN KEY (V_CODE) REFERENCES VENDOR ON UPDATE CASCADE);
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VARCHAR(10) VARCHAR(35) DATE SMALLINT SMALLINT NUMBER(8,2) NUMBER(5,2) INTEGER,
NOT NULL NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL,
UNIQUE,

Note
Chapter 7 Introduction to Structured Query Language (SQL) 257
Note to MySQL Users
MySQL was originally designed to handle very rapid retrieval of data. To improve retrieval speed, the developers sacrificed many features that ensure data integrity. As MySQL has become more robust, many of those features, such as referential integrity, have been added. To provide developers with options for database behavior, MySQL still supports “nontransaction-safe” tables that do not enable some of the features for data integrity, as well as “transaction-safe” tables that do. MySQL storage engines allow the developer to specify which type of tables to use. MySQL defaults to the MyISAM storage engine, which produces nontransaction-safe tables. The InnoDB storage engine produces transaction-safe tables. The storage engine is specified at the end of the CREATE TABLE command as shown below:
CREATE TABLE PRODUCT (
P_CODE
P_DESCRIPT
P_INDATE
P_QOH
P_MIN
P_PRICE
P_DISCOUNT
V_CODE
PRIMARY KEY (P_CODE),
FOREIGN KEY (V_CODE) REFERENCES VENDOR (V_CODE) ON UPDATE CASCADE); ;
Transaction-safe tables provide improved support for data integrity, implementation of database transactions and transaction logs (as discussed in Chapter 10, Transaction Management and Concurrency Control), and improved backup and recovery options.
VARCHAR(10) VARCHAR(35) DATE SMALLINT SMALLINT NUMBER(8,2) NUMBER(5,2) INTEGER,
NOT NULL NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL,
UNIQUE,
As you examine the preceding SQL table-creating command sequences, note the following features:
• The NOT NULL specifications for the attributes ensure that a data entry will be made. When it is crucial to have the data available, the NOT NULL specification will not allow the end user to leave the attribute empty (with no data entry at all). Because this specification is made at the table level and stored in the data dictionary, appli- cation programs can use this information to create the data dictionary validation automatically.
• The UNIQUE specification creates a unique index in the respective attribute. Use it to avoid having duplicated values in a column.
• The primary key attributes contain both a NOT NULL and UNIQUE specification, which enforce the entity integrity requirements. If the NOT NULL and UNIQUE specifications are not supported, use PRIMARY KEY without the specifications. (For example, if you designate the PK in MS Access, the NOT NULL and UNIQUE specifications are automatically assumed and are not spelled out.)
• The entire table definition is enclosed in parentheses. A comma is used to separate each table element definition (attributes, primary key, and foreign key).
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258 Part 3 Note
Advanced Design and Implementation
If you are working with a composite primary key, all of the primary key’s attributes are contained within the parentheses and are separated with commas. For example, the LINE table in Figure 7.1 has a primary key that consists of the two attributes INV_NUMBER and LINE_NUMBER. Therefore, you would define the primary key by typing the following:
PRIMARY KEY (INV_NUMBER, LINE_NUMBER),
The order of the primary key components is important because the indexing starts with the first mentioned attribute, then proceeds with the next attribute, and so on. In this example, the line numbers would be ordered within each of the invoice numbers:
INV_NUMBER LINE_NUMBER 1001 1
1001 2
1002 1
1003 1
1003 2
reserved words
Words used by a system that cannot be used for any other purpose. For example, in Oracle SQL, the word INITIAL cannot be used to name tables or columns.
Note
Note
• The ON UPDATE CASCADE specification ensures that if you make a change in any VENDOR’s V_CODE that change is automatically applied to all foreign key references throughout the system to ensure that referential integrity is maintained. (Although the ON UPDATE CASCADE clause is part of the ANSI standard, some RDBMSs, such as Oracle, do not support it. If your RDBMS does not support the clause, delete it from the code shown here.)
• An RDBMS automatically enforces referential integrity for foreign keys. That is, you cannot have an invalid entry in the foreign key column; at the same time, you cannot delete a vendor row as long as a product row references that vendor.
• The command sequence ends with a semicolon. (Remember that your RDBMS may require you to omit the semicolon.)
Note About Column Names
Do not use mathematical symbols such as +, −, and / in your column names; instead, use an underscore to separate words, if necessary. For example, PER-NUM might generate an error message, but PER_NUM is acceptable. Also, do not use reserved words. Reserved words are words used by SQL to perform specific functions. For example, in some RDBMSs, the column name INITIAL will generate the message “invalid column name.”
Note to Oracle Users
When you press Enter after typing each line, a line number is automatically generated as long as you do not type a semicolon before pressing Enter. For example, Oracle’s execution of the CREATE TABLE command will look like the following:
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Chapter 7 Introduction to Structured Query Language (SQL) 259
CREATE TABLE PRODUCT (
2 P_CODE
3 CONSTRAINT
4 P_DESCRIPT
5 P_INDATE
6 P_QOH
7 P_MIN
8 P_PRICE
9 P_DISCOUNT
10 V_CODE
11 CONSTRAINT
12 FOREIGN KEY
13 ;
VARCHAR2(10) PRODUCT_P_CODE_PK VARCHAR2(35)
DATE
NUMBER
NUMBER
NUMBER(8,2)
NUMBER(5,2)
NUMBER, PRODUCT_V_CODE_FK V_CODE REFERENCES VENDOR
PRIMARY KEY, NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL, NOT NULL,
In the preceding SQL command sequence, note the following:
• The attribute definition for P_CODE starts in line 2 and ends with a comma at the end of line 3.
• The CONSTRAINT clause (line 3) allows you to define and name a constraint in Oracle. You can name the constraint to meet your own naming conventions. In this case, the constraint was named PRODUCT_P_CODE_PK.
• Examples of constraints are NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, and CHECK. Additional details about constraints are explained as follows.
• To define a PRIMARY KEY constraint, you could also use the following syntax: P_CODE VARCHAR2(10) PRIMARY KEY.
• In this case, Oracle would automatically name the constraint.
• Lines 11 and 12 define a FOREIGN KEY constraint named PRODUCT_V_CODE_FK for the attribute V_CODE. The CONSTRAINT clause is generally used at the end of the CREATE TABLE command sequence.
• If you do not name the constraints yourself, Oracle will automatically assign a name. Unfor- tunately, the Oracle-assigned name makes sense only to Oracle, so you will have a difficult time deciphering it later. You should assign a name that makes sense to human beings!
7-2f SQL Constraints
In Chapter 3, The Relational Database Model, you learned that adherence to rules for entity integrity and referential integrity is crucial in a relational database environment. Fortunately, most SQL implementations support both integrity rules. Entity integrity is enforced automatically when the primary key is specified in the CREATE TABLE com- mand sequence. For example, you can create the VENDOR table structure and set the stage for the enforcement of entity integrity rules by using the following:
PRIMARY KEY (V_CODE)
In the PRODUCT table’s CREATE TABLE sequence, note that referential integrity has been enforced by specifying the following in the PRODUCT table:
FOREIGN KEY (V_CODE) REFERENCES VENDOR ON UPDATE CASCADE
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260 Part 3
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Online
Content
For a more detailed dis- cussion of the options for using the ON DELETE and ON UPDATE clauses, see Appendix D, Con- verting the ER Model into a Database Structure, Section D.2, General Rules Governing Relationships Among Tables. Appendix D is available at www. cengagebrain.com.
Note
The foreign key constraint definition ensures that:
• You cannot delete a vendor from the VENDOR table if at least one product row references that vendor. This is the default behavior for the treatment of foreign keys.
• On the other hand, if a change is made in an existing VENDOR table’s V_CODE, that change must be reflected automatically in any PRODUCT table V_CODE reference (ON UPDATE CASCADE). That restriction makes it impossible for a V_CODE value to exist in the PRODUCT table if it points to a nonexistent VENDOR table V_CODE value. In other words, the ON UPDATE CASCADE specification ensures the preservation of referential integrity. (Oracle does not support ON UPDATE CASCADE.)
In general, ANSI SQL permits the use of ON DELETE and ON UPDATE clauses to cover CASCADE, SET NULL, or SET DEFAULT.
Note about Referential Constraint Actions
The support for the referential constraint’s actions varies from product to product. For example:
• MySQL requires the InnoDB storage engine to enforce referential integrity.
• MS Access, SQL Server, MySQL, and Oracle support ON DELETE CASCADE.
• MS Access, MySQL, and SQL Server support ON UPDATE CASCADE.
• Oracle does not support ON UPDATE CASCADE.
• Oracle and MySQL support SET NULL.
• MS Access and SQL Server do not support SET NULL.
• Refer to your product manuals for additional information on referential constraints.
While MS Access does not support ON DELETE CASCADE or ON UPDATE CASCADE at the SQL command-line level, it does support them through the relationship window inter- face. In fact, whenever you try to establish a relationship between two tables in Access, the relationship window interface will automatically pop up.
Besides the PRIMARY KEY and FOREIGN KEY constraints, the ANSI SQL standard also defines the following constraints:
• The NOT NULL constraint ensures that a column does not accept nulls.
• The UNIQUE constraint ensures that all values in a column are unique.
• The DEFAULT constraint assigns a value to an attribute when a new row is added to a table. The end user may, of course, enter a value other than the default value.
• The CHECK constraint is used to validate data when an attribute value is entered. The CHECK constraint does precisely what its name suggests: it checks to see that a specified condition exists. Examples of such constraints include the following:
– The minimum order value must be at least 10.
– The date must be after April 15, 2016.
If the CHECK constraint is met for the specified attribute (that is, the condition is true), the data is accepted for that attribute. If the condition is found to be false, an error message is generated and the data is not accepted.
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Chapter 7 Introduction to Structured Query Language (SQL) 261 Note that the CREATE TABLE command lets you define constraints in two different
places:
• When you create the column definition (known as a column constraint)
• When you use the CONSTRAINT keyword (known as a table constraint)
A column constraint applies to just one column; a table constraint may apply to many col- umns. Those constraints are supported at varying levels of compliance by enterprise RDBMSs. In this chapter, Oracle is used to illustrate SQL constraints. For example, note that the following SQL command sequence uses the DEFAULT and CHECK constraints to define
the table named CUSTOMER. CREATE TABLE CUSTOMER (
CUS_CODE CUS_LNAME CUS_FNAME CUS_INITIAL CUS_AREACODE
CUS_PHONE
CUS_BALANCE
CONSTRAINT CUS_UI1 UNIQUE (CUS_LNAME, CUS_FNAME));
In this case, the CUS_AREACODE attribute is assigned a default value of ‘615’. There- fore, if a new CUSTOMER table row is added and the end user makes no entry for the area code, the ‘615’ value will be recorded. Also, the CHECK condition restricts the val- ues for the customer’s area code to 615, 713, and 931; any other values will be rejected.
It is important to note that the DEFAULT value applies only when new rows are added to a table, and then only when no value is entered for the customer’s area code. (The default value is not used when the table is modified.) In contrast, the CHECK condition is vali- dated whether a customer row is added or modified. However, while the CHECK condition may include any valid expression, it applies only to the attributes in the table being checked. If you want to check for conditions that include attributes in other tables, you must use triggers. (See Chapter 8, Advanced SQL.) Finally, the last line of the CREATE TABLE com- mand sequence creates a unique index constraint (named CUS_UI1) on the customer’s last name and first name. The index will prevent the entry of two customers with the same last name and first name. (This index merely illustrates the process. Clearly, it should be possible to have more than one person named John Smith in the CUSTOMER table.)
Note
NUMBER VARCHAR(15) VARCHAR(15) CHAR(1), CHAR(3)
PRIMARY KEY, NOT NULL, NOT NULL,
DEFAULT ‘615’
NOT NULL
CHAR(8) NUMBER(9,2)
NOT NULL, DEFAULT 0.00,
CHECK(CUS_AREACODE IN (‘615′,’713′,’931′)),
Note to MS Access and MySQL Users
MS Access does not accept the DEFAULT or CHECK constraints. However, MS Access will accept the CONSTRAINT CUS_UI1 UNIQUE (CUS_LNAME, CUS_FNAME) line and create the unique index.
MySQL will allow CHECK constraints in the table definition for compatibility, but it does not enforce them. MySQL does allow DEFAULT constraints, but the DEFAULT value cannot be a function. Therefore, it is not possible to set the default value for a date field to be the current date using SYSDATE() or NOW() because they are both functions.
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262 Part 3
Advanced Design and Implementation
In the following SQL command to create the INVOICE table, the DEFAULT con- straint assigns a default date to a new invoice, and the CHECK constraint validates that the invoice date is greater than January 1, 2016.
CREATE TABLE INVOICE (
INV_NUMBER
CUS_CODE
INV_DATE
NUMBER NUMBER DATE
PRIMARY KEY,
NOT NULL REFERENCES CUSTOMER(CUS_CODE), DEFAULT SYSDATE NOT NULL,
CONSTRAINT INV_CK1 CHECK (INV_DATE > TO_DATE(’01-JAN-2016’,
‘DD-MON-YYYY’)));
In this case, notice the following:
• The CUS_CODE attribute definition contains REFERENCES CUSTOMER (CUS_ CODE) to indicate that the CUS_CODE is a foreign key. This is another way to define a foreign key.
• The DEFAULT constraint uses the SYSDATE special function. This function always returns today’s date.
• The invoice date (INV_DATE) attribute is automatically given today’s date (returned by SYSDATE) when a new row is added and no value is given for the attribute.
• A CHECK constraint is used to validate that the invoice date is greater than ‘January 1, 2016′. When comparing a date to a manually entered date in a CHECK clause, Oracle requires the use of the TO_DATE function. The TO_DATE function takes two parameters: the literal date and the date format used.
The final SQL command sequence creates the LINE table. The LINE table has a com- posite primary key (INV_NUMBER, LINE_NUMBER) and uses a UNIQUE constraint in INV_NUMBER and P_CODE to ensure that the same product is not ordered twice in the same invoice.
CREATE TABLE LINE (
INV_NUMBER
LINE_NUMBER
P_CODE
LINE_UNITS
LINE_PRICE
PRIMARY KEY (INV_NUMBER, LINE_NUMBER),
FOREIGN KEY (INV_NUMBER) REFERENCES INVOICE ON DELETE CASCADE, FOREIGN KEY (P_CODE) REFERENCES PRODUCT(P_CODE),
CONSTRAINT LINE_UI1 UNIQUE(INV_NUMBER, P_CODE));
In the creation of the LINE table, note that a UNIQUE constraint is added to prevent the duplication of an invoice line. A UNIQUE constraint is enforced through the creation of a unique index. Also note that the ON DELETE CASCADE foreign key enforces referential integrity. The use of ON DELETE CASCADE is recommended for weak entities to ensure that the deletion of a row in the strong entity automatically triggers the deletion of the cor- responding rows in the dependent weak entity. In that case, the deletion of an INVOICE row will automatically delete all of the LINE rows related to the invoice. In the following section, you will learn more about indexes and how to use SQL commands to create them.
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NUMBER NUMBER(2,0) VARCHAR(10) NUMBER(9,2) NUMBER(9,2)
NOT NULL, NOT NULL, NOT NULL, DEFAULT 0.00 DEFAULT 0.00
NOT NULL, NOT NULL,

Chapter 7 Introduction to Structured Query Language (SQL) 263
7-2g SQL Indexes
You learned in Chapter 3 that indexes can be used to improve the efficiency of searches and to avoid duplicate column values. In the previous section, you saw how to declare unique indexes on selected attributes when the table is created. In fact, when you declare a primary key, the DBMS automatically creates a unique index. Even with this feature, you often need additional indexes. The ability to create indexes quickly and efficiently is important. Using the CREATE INDEX command, SQL indexes can be created on the basis of any selected attribute. The syntax is:
CREATE [UNIQUE]INDEX indexname ON tablename(column1 [, column2])
For example, based on the attribute P_INDATE stored in the PRODUCT table, the
following command creates an index named P_INDATEX: CREATE INDEX P_INDATEX ON PRODUCT(P_INDATE);
SQL does not let you write over an existing index without warning you first, thus pre- serving the index structure within the data dictionary. Using the UNIQUE index qual- ifier, you can even create an index that prevents you from using a value that has been used before. Such a feature is especially useful when the index attribute is a candidate key whose values must not be duplicated:
CREATE UNIQUE INDEX P_CODEX ON PRODUCT(P_CODE);
If you now try to enter a duplicate P_CODE value, SQL produces the error mes- sage “duplicate value in index.” Many RDBMSs, including Access, automatically create a unique index on the PK attribute(s) when you declare the PK.
A common practice is to create an index on any field that is used as a search key, in comparison operations in a conditional expression, or when you want to list rows in a specific order. For example, if you want to create a report of all products by vendor, it would be useful to create an index on the V_CODE attribute in the PRODUCT table. Remember that a vendor can supply many products. Therefore, you should not create a UNIQUE index in this case. Better yet, to make the search as efficient as possible, using a composite index is recommended.
Unique composite indexes are often used to prevent data duplication. For example, consider the case illustrated in Table 7.5, in which required employee test scores are stored. (An employee can take a test only once on a given date.) Given the structure of Table 7.5, the PK is EMP_NUM + TEST_NUM. The third test entry for employee 111 meets entity integrity requirements—the combination 111,3 is unique—yet the WEA test entry is clearly duplicated.
TABLE 7.5
A DUPLICATED TEST RECORD
EMP_NUM TEST_NUM TEST_CODE TEST_DATE TEST_SCORE
110
1
WEA
15-Jan-2016
93
110
2
WEA
12-Jan-2016
87
111
1
HAZ
14-Dec-2015
91
111
2
WEA
18-Feb-2016
95
111
3
WEA
18-Feb-2016
95
112
1
CHEM
17-Aug-2015
91
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CREATE INDEX
A SQL command that creates indexes on the basis of a selected attribute or attributes.

264 Part 3
Advanced Design and Implementation
Such duplication could have been avoided through the use of a unique composite index, using the attributes EMP_NUM, TEST_CODE, and TEST_DATE:
CREATE UNIQUE INDEX EMP_TESTDEX ON TEST(EMP_NUM, TEST_CODE, TEST_DATE);
By default, all indexes produce results that are listed in ascending order, but you can create an index that yields output in descending order. For example, if you routinely print a report that lists all products ordered by price from highest to lowest, you could create an index named PROD_PRICEX by typing:
CREATE INDEX PROD_PRICEX ON PRODUCT(P_PRICE DESC); To delete an index, use the DROP INDEX command:
DROP INDEX indexname
For example, if you want to eliminate the PROD_PRICEX index, type:
DROP INDEX PROD_PRICEX;
After creating the tables and some indexes, you are ready to start entering data. The following sections use two tables (VENDOR and PRODUCT) to demonstrate most of the data manipulation commands.
7-3 Data Manipulation Commands
In this section, you will learn how to use the basic SQL data manipulation commands
INSERT, SELECT, COMMIT, UPDATE, ROLLBACK, and DELETE.
7-3a Adding Table Rows
SQL requires the use of the INSERT command to enter data into a table. The INSERT
command’s basic syntax looks like this:
INSERT INTO tablename VALUES (value1, value2, …, valuen)
Because the PRODUCT table uses its V_CODE to reference the VENDOR table’s V_CODE, an integrity violation will occur if the VENDOR table V_CODE values do not yet exist. Therefore, you need to enter the VENDOR rows before the PRODUCT rows. Given the VENDOR table structure defined earlier and the sample VENDOR data shown in Figure 7.2, you would enter the first two data rows as follows:
INSERT INTO VENDOR
VALUES (21225,’Bryson, Inc.’,’Smithson’,’615′,’223-3234′,’TN’,’Y’);
INSERT INTO VENDOR
VALUES (21226,’Superloo, Inc.’,’Flushing’,’904′,’215-8995′,’FL’,’N’);
and so on, until all of the VENDOR table records have been entered.
(To see the contents of the VENDOR table, use the SELECT * FROM VENDOR;
command.)
The PRODUCT table rows would be entered in the same fashion, using the PROD-
UCT data shown in Figure 7.2. For example, the first two data rows would be entered as follows, pressing Enter at the end of each line:
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DROP INDEX
A SQL command used to delete database objects such as tables, views, indexes, and users.
INSERT
A SQL command that allows the insertion of one or more data rows into a table using a subquery.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 7 Introduction to Structured Query Language (SQL) 265
INSERT INTO PRODUCT
VALUES (’11QER/31′,’Power painter, 15 psi., 3-nozzle’,’03-Nov-15′,8,5,109.99,0.00,25595);
INSERT INTO PRODUCT
VALUES (’13-Q2/P2′,’7.25-in. pwr. saw blade’,’13-Dec-15′,32,15,14.99, 0.05, 21344);
(To see the contents of the PRODUCT table, use the SELECT * FROM PRODUCT; command.)
Note
In the preceding data-entry lines, observe that:
• The row contents are entered between parentheses. Note that the first character after VALUES is a parenthesis and that the last character in the command sequence is also a parenthesis.
• Character (string) and date values must be entered between apostrophes ( ’ ).
• Numerical entries are not enclosed in apostrophes.
• Attribute entries are separated by commas.
• A value is required for each column in the table.
This version of the INSERT command adds one table row at a time.
Inserting Rows with Null Attributes Thus far, you have entered rows in which all of the attribute values are specified. But what do you do if a product does not have a vendor or if you do not yet know the vendor code? In those cases, you would want to leave the vendor code null. To enter a null, use the following syntax:
INSERT INTO PRODUCT
VALUES (‘BRT-345′,’Titanium drill bit’,’18-Oct-15′, 75, 10, 4.50, 0.06, NULL);
Incidentally, note that the NULL entry is accepted only because the V_CODE attribute is optional—the NOT NULL declaration was not used in the CREATE TABLE statement for this attribute.
Inserting Rows with Optional Attributes Sometimes, more than one attribute is optional. Rather than declaring each attribute as NULL in the INSERT command, you can indicate just the attributes that have required values. You do that by listing the attribute names inside parentheses after the table name. For the purpose of this exam- ple, assume that the only required attributes for the PRODUCT table are P_CODE and P_DESCRIPT:
INSERT INTO PRODUCT(P_CODE, P_DESCRIPT) VALUES (‘BRT-345′,’Titanium drill bit’);
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Date entry is a function of the date format expected by the DBMS. For example, March 25, 2016, might be shown as 25-Mar-2016 in Access and Oracle, 2016-03-25 in MySQL, or it might be displayed in other presentation formats in another RDBMS. MS Access requires the use of # delimiters when performing any computations or comparisons based on date attributes, as in P_INDATE >= #25-Mar-16#. Date data and the functions for manipulating it in various DBMS products is discussed in more detail in Chapter 8.

266 Part 3 Note
Advanced Design and Implementation
When inserting rows interactively, omitting the attribute list in the INSERT command is accept- able if the programmer intends to provide a value for each attribute. However, if an INSERT com- mand is embedded inside a program for later use, the attribute list should always be used, even if the programmer provides a value for every attribute. The reason is that the structure of the database table may change over time. The programs that are created today become the leg- acy systems of tomorrow. These applications may be expected to have a very long, useful life. If the structure of the table changes over time as new business requirements develop, an INSERT without an attribute list may inadvertently insert data into the wrong columns if the order of the columns in the table changes, or the INSERT command may generate an error because the command does not provide enough values if new columns are subsequently added to the table.
COMMIT
The SQL command that permanently saves data changes to a database.
Note
7-3b Saving Table Changes
Any changes made to the table contents are not saved on disk until you close the data- base, close the program you are using, or use the COMMIT command. If the database is open and a power outage or some other interruption occurs before you issue the COMMIT command, your changes will be lost and only the original table contents will be retained. The syntax for the COMMIT command is:
COMMIT [WORK]
The COMMIT command permanently saves all changes—such as rows added, attri- butes modified, and rows deleted—made to any table in the database. Therefore, if you intend to make your changes to the PRODUCT table permanent, it is a good idea to save those changes by using the following command:
COMMIT;
Note to MS Access and MySQL Users
MS Access does not support the COMMIT command because it automatically saves changes after the execution of each SQL command. By default, MySQL also automatically commits changes with each command. However, if START TRANSACTION or BEGIN is placed at the beginning of a series of commands, MySQL will delay committing the commands until the COMMIT or ROLLBACK command is issued.
However, the COMMIT command’s purpose is not just to save changes. In fact, the ultimate purpose of the COMMIT and ROLLBACK commands (see Section 7-3e) is to ensure database update integrity in transaction management. (You will see how such issues are addressed in Chapter 10, Transaction Management and Concurrency Control.)
7-3c Listing Table Rows
The SELECT command is used to list the contents of a table. The syntax of the SELECT
command is as follows:
SELECT columnlist FROM tablename
The SELECT clause of the query specifies the columns to be retrieved as a column list. The columnlist represents one or more attributes, separated by commas. You could use the asterisk ( * ) as a wildcard character to list all attributes. A wildcard character
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SELECT
A SQL command that yields the values of all rows or a subset of rows in a table. The SELECT statement is used to retrieve data from tables.
wildcard character
A symbol that can be used as a general substitute for: (1) all columns in a table (*) when used in an attribute list of a SELECTstatement or, (2) zero or more characters in a SQL LIKE clause condition ( % and _ ).
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Chapter 7 Introduction to Structured Query Language (SQL) 267 is a symbol that can be used as a general substitute for other characters or commands.
For example, to list all attributes and all rows of the PRODUCT table, use the following: SELECT * FROM PRODUCT;
The FROM clause of the query specifies the table or tables from which the data is to be retrieved. Figure 7.3 shows the output generated by that command. (Figure 7.3 shows all of the rows in the PRODUCT table that serve as the basis for subsequent discussions. If you entered only the PRODUCT table’s first two records, as shown in the preceding section, the output of the preceding SELECT command would show only the rows you entered. Don’t worry about the difference between your SELECT output and the output shown in Figure 7.3. When you complete the work in this section, you will have created and populated your VENDOR and PRODUCT tables with the correct rows for use in future sections.)
Note
Note
FIGURE 7.3 THE CONTENTS OF THE PRODUCT TABLE
Your listing might not be in the order shown in Figure 7.3. The listings shown in the figure are the result of system-controlled primary-key-based index operations. You will learn later how to control the output so that it conforms to the order you have specified.
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Note to Oracle Users
Some SQL implementations (such as Oracle’s) cut the attribute labels to fit the width of the column. However, Oracle lets you set the width of the display column to show the complete attribute name. You can also change the display format, regardless of how the data is stored in the table. For example, if you want to display dollar symbols and commas in the P_PRICE output, you can declare:
COLUMN P_PRICE FORMAT $99,999.99
to change the output 12347.67 to $12,347.67.
In the same manner, to display only the first 12 characters of the P_DESCRIPT attribute, use the following:
COLUMN P_DESCRIPT FORMAT A12 TRUNCATE
FROM
A SQL clause that specifies the table or tables from which data is to be retrieved.

268 Part 3
Advanced Design and Implementation
Note
Although SQL commands can be grouped together on a single line, complex com- mand sequences are best shown on separate lines, with space between the SQL command and the command’s components. Using that formatting convention makes it much easier to see the components of the SQL statements, which in turn makes it easy to trace the SQL logic and make corrections if necessary. The number of spaces used in the indention is up to you. For example, note the following format for a more complex statement:
SELECT P_CODE, P_DESCRIPT, P_INDATE, P_QOH, P_MIN, P_PRICE, P_DISCOUNT, V_CODE
FROM PRODUCT;
When you run a SELECT command on a table, the RDBMS returns a set of one or more rows that have the same characteristics as a relational table. In addition, the SELECT command lists all rows from the table you specified in the FROM clause. This is a very important characteristic of SQL commands. By default, most SQL data manipulation com- mands operate over an entire table (or relation), which is why SQL commands are said to be set-oriented commands. A SQL set-oriented command works over a set of rows. The set may include one or more columns and zero or more rows from one or more tables.
Just as with INSERT commands, omitting the column list by specifying “ * ” for all columns is acceptable when querying the database interactively. However, if the SELECT query is embedded in a program for later use, the column list should always be included even if every column in the table is being included in the result because the structure of the table might change over time. In real-world business applications, SELECT * commands embed- ded in programs are often considered bugs waiting to happen.
7-3d Updating Table Rows
Use the UPDATE command to modify data in a table. The syntax for this command is as
follows:
UPDATE SET [WHERE
tablename
columnname = expression [, columnname = expression] conditionlist ];
For example, if you want to change P_INDATE from December 13, 2015, to January 18, 2016, in the second row of the PRODUCT table (see Figure 7.3), use the primary key (13-Q2/P2) to locate the correct row. Therefore, type:
UPDATE SET WHERE
PRODUCT
P_INDATE = ’18-JAN-2016′ P_CODE = ’13-Q2/P2′;
If more than one attribute is to be updated in the row, separate the corrections with commas:
UPDATE SET WHERE
PRODUCT
P_INDATE = ’18-JAN-2016′, P_PRICE = 17.99, P_MIN = 10 P_CODE = ’13-Q2/P2′;
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UPDATE
A SQL command that allows attribute values to be changed in one or more rows of a table.
Copyright 2017 Cengage Lea Editorial review has deemed that

What would have happened if the previous UPDATE command had not included the WHERE condition? The P_INDATE, P_PRICE, and P_MIN values would have been changed in all rows of the PRODUCT table. Remember, the UPDATE command is a set-oriented operator. Therefore, if you do not specify a WHERE condition, the UPDATE command will apply the changes to all rows in the specified table.
Confirm the correction(s) by using the following SELECT command to check the PRODUCT table’s listing:
SELECT * FROM PRODUCT;
7-3e Restoring Table Contents
If you have not yet used the COMMIT command to store the changes permanently in the database, you can restore the database to its previous condition with the ROLLBACK command. ROLLBACK undoes any changes since the last COMMIT command and brings all of the data back to the values that existed before the changes were made. To restore the data to its “prechange” condition, type:
ROLLBACK;
and then press Enter. Use the SELECT statement again to verify that the ROLLBACK restored the data to its original values.
COMMIT and ROLLBACK work only with data manipulation commands that add, modify, or delete table rows. For example, assume that you perform these actions:
1. CREATE a table called SALES.
2. INSERT 10 rows in the SALES table.
3. UPDATE two rows in the SALES table. 4. Execute the ROLLBACK command.
Will the SALES table be removed by the ROLLBACK command? No, the ROLLBACK command will undo only the results of the INSERT and UPDATE commands. All data definition commands (CREATE TABLE) are automatically committed to the data dictionary and cannot be rolled back. The COMMIT and ROLLBACK commands are examined in greater detail in Chapter 10.
Note
Some RDBMSs, such as Oracle, automatically COMMIT data changes when issuing data definition commands. For example, if you had used the CREATE INDEX command after updating the two rows in the previous example, all previous changes would have been committed automatically; doing a ROLLBACK afterward would not have undone anything. Check your RDBMS manual to understand these subtle differences.
7-3f Deleting Table Rows
It is easy to delete a table row using the DELETE statement. The syntax is:
DELETE FROM tablename [WHERE conditionlist ];
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Chapter 7 Introduction to Structured Query Language (SQL) 269
Note to MS Access Users
MS Access does not support the ROLLBACK command.
ROLLBACK
A SQL command that restores the database table contents to the condition that existed after the last COMMIT statement.
DELETE
A SQL command that allows data rows to be deleted from a table.
e eBook and/or eChapter(s). uent rights restrictions require it.

270 Part 3
Advanced Design and Implementation
Note
For example, if you want to delete the product you added earlier whose code (P_CODE) is ‘BRT-345’, use the following command:
DELETE FROM PRODUCT
WHERE P_CODE = ‘BRT-345’;
In this example, the primary key value lets SQL find the exact record to be deleted from the PRODUCT table. However, deletions are not limited to a primary key match; any attribute may be used. For example, in your PRODUCT table, you will see several products for which the P_MIN attribute is equal to 5. Use the following command to delete all rows from the PRODUCT table for which the P_MIN is equal to 5:
DELETE FROM PRODUCT WHERE P_MIN = 5;
Check the PRODUCT table’s contents again to verify that all products with P_MIN equal to 5 have been deleted.
Finally, remember that DELETE is a set-oriented command, and that the WHERE condition is optional. Therefore, if you do not specify a WHERE condition, all rows from the specified table will be deleted!
Note to MySQL Users
By default MySQL is set for “safe mode” for updates and deletes. This means that users can- not update or delete rows from a table unless the UPDATE or DELETE command includes a WHERE clause that provides a value for the primary key. To disable safe mode temporarily, set the sql_safe_updates variable to 0. Safe mode can be re-enabled by setting the variable back to 1. For example, to complete the DELETE command shown above, the following sequence could be used:
SET SQL_SAFE_UPDATES = 0;
DELETE FROM PRODUCT WHERE P_MIN = 5; SET SQL_SAFE_UPDATES = 1;
To permanently disable safe mode, uncheck the safe mode option in MySQL Workbench under the Edit → Preferences window.
7-3g Inserting Table Rows with a Select Subquery
You learned in Section 7-3a how to use the INSERT statement to add rows to a table. In that section, you added rows one at a time. In this section, you will learn how to add multiple rows to a table, using another table as the source of the data. The syntax for the INSERT statement is:
INSERT INTO tablename SELECT columnlist FROM tablename;
In this case, the INSERT statement uses a SELECT subquery. A subquery, also known as a nested query or an inner query, is a query that is embedded (or nested) inside another query. The inner query is always executed first by the RDBMS. Given
arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
subquery
A query that is embedded (or nested) inside another query. Also known as a nested query or an inner query.
nested query
In SQL, a query that is embedded in another query. See subquery.
inner query
A query that is embedded or nested inside another query. Also known as a nested query or a subquery.
Copyright 2017 Cengage Le Editorial review has deemed that

the previous SQL statement, the INSERT portion represents the outer query, and the SELECT portion represents the subquery. You can nest queries (place queries inside que- ries) many levels deep. In every case, the output of the inner query is used as the input for the outer (higher-level) query. In Chapter 8 you will learn more about the various types of subqueries.
The values returned by the SELECT subquery should match the attributes and data types of the table in the INSERT statement. If the table into which you are inserting rows has one date attribute, one number attribute, and one character attribute, the SELECT subquery should return one or more rows in which the first column has date values, the second column has number values, and the third column has character values.
7-4 SELECT Queries
In this section, you will learn how to fine-tune the SELECT command by adding restric- tions to the search criteria. When coupled with appropriate search conditions, SELECT is an incredibly powerful tool that enables you to transform data into information. For example, in the following sections, you will learn how to create queries that can answer questions such as these: “What products were supplied by a particular vendor?”, “Which products are priced below $10?”, and “How many products supplied by a given vendor were sold between January 5, 2016, and March 20, 2016?”
7-4a Selecting Rows with Conditional Restrictions
You can select partial table contents by placing restrictions on the rows to be included in the output. Use the WHERE clause to add conditional restrictions to the SELECT state- ment that limit the rows returned by the query. The following syntax enables you to specify which rows to select:
Online Content
Before you execute the commands in the follow- ing sections, you must do the following:
• If you are using Oracle, MySQL, or MS SQL Server, run the respective sqlintrod- binit.sql script file at www.cengagebrain. com to create all tables and load the data in the database.
• If you are using Access, copy the original Ch07_SaleCo. mdb file from www. cengagebrain.com.
The SELECT statement retrieves all rows that match the specified condition(s)— also known as the conditional criteria—you specified in the WHERE clause. The con- ditionlist in the WHERE clause of the SELECT statement is represented by one or more conditional expressions, separated by logical operators. The WHERE clause is optional. If no rows match the specified criteria in the WHERE clause, you see a blank screen or a message that tells you no rows were retrieved. For example, consider the following query:
SELECT FROM WHERE
P_DESCRIPT, P_INDATE, P_PRICE, V_CODE PRODUCT
V_CODE = 21344;
This query returns the description, date, and price of products with a vendor code of 21344, as shown in Figure 7.4.
MS Access users can use the Access QBE (query by example) query generator. Although the Access QBE generates its own “native” version of SQL, you can also elect to type standard SQL in the Access SQL window, as shown at the bottom of Figure 7.5. The figure shows the Access QBE screen, the SQL window’s QBE-generated SQL, and the listing of the modified SQL.
WHERE
A SQL clause that adds conditional restrictions to a SELECT statement that limit the rows returned by the query.
Chapter 7 Introduction to Structured Query Language (SQL) 271
SELECT FROM [WHERE
columnlist tablelist conditionlist ];
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272 Part 3
Advanced Design and Implementation
FIGURE7.4 SELECTEDPRODUCTTABLEATTRIBUTESFOR VENDOR CODE 21344
FIGURE 7.5 THE MICROSOFT ACCESS QBE AND ITS SQL
Microsoft Access-generated SQL User-entered SQL
Query view options
Note
Note to MS Access Users
The MS Access QBE interface automatically designates the data source by using the table name as a prefix. You will discover later that the table name prefix is used to avoid ambi- guity when the same column name appears in multiple tables. For example, both the VENDOR and PRODUCT tables contain the V_CODE attribute. Therefore, if both tables are used (as they would be in a join), the source of the V_CODE attribute must be specified.
Numerous conditional restrictions can be placed on the selected table contents. For example, the comparison operators shown in Table 7.6 can be used to restrict output.
The following example uses the “not equal to” operator:
SELECT FROM WHERE
P_DESCRIPT, P_QOH, P_PRICE, V_CODE PRODUCT
V_CODE <> 21344;
The output, shown in Figure 7.6, lists all of the rows for which the vendor code is not 21344.
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TABLE 7.6
COMPARISON OPERATORS
SYMBOL MEANING
Note that, in Figure 7.6, rows with nulls in the V_CODE column (see Figure 7.3) are not included in the SELECT command’s output.
FIGURE7.6 PRODUCTTABLEATTRIBUTESFORVENDORCODESOTHER THAN 21344
The following command sequence:
Chapter 7 Introduction to Structured Query Language (SQL) 273
=
Equal to
< Less than <= Less than or equal to >
Greater than
>=
Greater than or equal to
<> or !=
Not equal to
SELECT FROM WHERE
P_DESCRIPT, P_QOH, P_MIN, P_PRICE PRODUCT
P_PRICE <= 10; yields the output shown in Figure 7.7. FIGURE7.7 SELECTEDPRODUCTTABLEATTRIBUTESWITHAP_PRICE RESTRICTION Using Comparison Operators on Character Attributes Because computers identify all characters by their numeric American Standard Code for Information Interchange (ASCII) codes, comparison operators may even be used to place restrictions on character-based attributes. Therefore, the command: Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 274 Part 3 Advanced Design and Implementation SELECT FROM WHERE P_CODE, P_DESCRIPT, P_QOH, P_MIN, P_PRICE PRODUCT P_CODE < '1558-QW1'; would be correct and would yield a list of all rows in which the P_CODE is alphabetically less than 1558-QW1. (Because the ASCII code value for the letter B is greater than the value of the letter A, it follows that A is less than B.) Therefore, the output will be gener- ated as shown in Figure 7.8. FIGURE7.8 SELECTEDPRODUCTTABLEATTRIBUTES:THEASCIICODE EFFECT String (character) comparisons are made from left to right. This left-to-right compari- son is especially useful when attributes such as names are to be compared. For example, the string “Ardmore” would be judged greater than the string “Aarenson” but less than the string “Brown”; such results may be used to generate alphabetical listings like those in a phone direc- tory. If the characters 0−9 are stored as strings, the same left-to-right string comparisons can lead to apparent anomalies. For example, the ASCII code for the character “5” is greater than the ASCII code for the character “4,” as expected. Yet, the same “5” will also be judged greater than the string “44” because the first character in the string “44” is less than the string “5.” For that reason, you may get some unexpected results from comparisons when dates or other numbers are stored in character format. For example, the left-to-right ASCII character com- parison would force the conclusion that the date “01/01/2016” occurred before “12/31/2015.” Because the leftmost character “0” in “01/01/2016” is less than the leftmost character “1” in “12/31/2015,” “01/01/2016” is less than “12/31/2015.” Naturally, if date strings are stored in a yyyy/mm/dd format, the comparisons will yield appropriate results, but this is a nonstandard date presentation. Therefore, all current RDBMSs support date data types; you should use them. In addition, using date data types gives you the benefit of date arithmetic. Using Comparison Operators on Dates Date procedures are often more soft- ware-specific than other SQL procedures. For example, the query to list all of the rows in which the inventory stock dates occur on or after January 20, 2016, looks like this: SELECT FROM WHERE P_DESCRIPT, P_QOH, P_MIN, P_PRICE, P_INDATE PRODUCT P_INDATE >= ’20-Jan-2016′;
Remember that MS Access users must use the # delimiters for dates. For example, you would use #20-Jan-16# in the preceding WHERE clause. The date-restricted out- put is shown in Figure 7.9. In MySQL, the expected date format is yyyy-mm-dd, so the WHERE clause would be written as:
WHERE P_INDATE >= ‘2016-01-20’
Using Computed Columns and Column Aliases Suppose that you want to deter- mine the total value of each of the products currently held in inventory. Logically, that determination requires the multiplication of each product’s quantity on hand by its current price. You can accomplish this task with the following command:
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Chapter 7 Introduction to Structured Query Language (SQL) 275 SELECT P_DESCRIPT, P_QOH, P_PRICE, P_QOH * P_PRICE
FROM PRODUCT;
Entering the SQL command in Access generates the output shown in Figure 7.10.
SQL accepts any valid expressions (or formulas) in the computed columns. Such for- mulas can contain any valid mathematical operators and functions that are applied to attributes in any of the tables specified in the FROM clause of the SELECT statement. Note also that Access automatically adds an Expr label to all computed columns. (The first computed column would be labeled Expr1; the second, Expr2; and so on.) Oracle uses the actual formula text as the label for the computed column.
To make the output more readable, the SQL standard permits the use of aliases for any column in a SELECT statement. An alias is an alternate name given to a column or table in any SQL statement.
For example, you can rewrite the previous SQL statement as follows:
SELECT P_DESCRIPT, P_QOH, P_PRICE, P_QOH * P_PRICE AS TOTVALUE
FROM PRODUCT;
The output of the command is shown in Figure 7.11.
You could also use a computed column, an alias, and date arithmetic in a single query. For example, assume that you want to get a list of out-of-warranty products that have been stored more than 90 days. In that case, the P_INDATE is at least 90 days less than the current (system) date. The MS Access version of this query is:
FIGURE7.9 SELECTEDPRODUCTTABLEATTRIBUTES:DATERESTRICTION
FIGURE7.10 SELECTSTATEMENTWITHACOMPUTEDCOLUMN
SELECT FROM WHERE
P_CODE, P_INDATE, DATE() – 90 AS CUTDATE PRODUCT
P_INDATE <= DATE() - 90; alias An alternative name for a column or table in a SQL statement. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 276 Part 3 Advanced Design and Implementation FIGURE7.11 SELECTSTATEMENTWITHACOMPUTEDCOLUMNANDAN ALIAS The Oracle version of the same query is shown here: TABLE 7.7 SELECT FROM WHERE P_CODE, P_INDATE, SYSDATE - 90 AS CUTDATE PRODUCT P_INDATE <= SYSDATE - 90; Note that DATE() and SYSDATE are special functions that return the current date in MS Access and Oracle, respectively. You can use the DATE() and SYSDATE functions anywhere a date literal is expected, such as in the value list of an INSERT statement, in an UPDATE statement when changing the value of a date attribute, or in a SELECT statement, as shown here. Of course, the previous query output would change based on the current date. Suppose that a manager wants a list of all products, the dates they were received, and the warranty expiration date (90 days from receiving the product). To generate that list, type: SELECT P_CODE, P_INDATE, P_INDATE + 90 AS EXPDATE FROM PRODUCT; Note that you can use all arithmetic operators with date attributes as well as with numeric attributes. 7-4b Arithmetic Operators: The Rule of Precedence As you saw in the previous example, you can use arithmetic operators with table attributes in a column list or in a conditional expression. In fact, SQL commands are often used in conjunction with the arithmetic operators shown in Table 7.7. THE ARITHMETIC OPERATORS OPERATOR DESCRIPTION Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. + Add - Subtract * Multiply / Divide ^ Raise to the power of (some applications use ** instead of ^) Do not confuse the multiplication symbol ( * ) with the wildcard symbol used by some SQL implementations, such as MS Access. The wildcard symbol is used only in string compari- sons, while the multiplication symbol is used in conjunction with mathematical procedures. As you perform mathematical operations on attributes, remember the mathematical rules of precedence. As the name suggests, the rules of precedence are the rules that establish the order in which computations are completed. For example, note the order of the following computational sequence: 1. Perform operations within parentheses. 2. Perform power operations. 3. Perform multiplications and divisions. 4. Perform additions and subtractions. Theapplicationoftherulesofprecedencewilltellyouthat8+2*5=8+10=18,but(8+2) *5=10*5=50.Similarly,4+5^2*3=4+25*3=79,but(4+5)^2*3=81*3=243,while theoperationexpressedby(4+5^2)*3yieldstheanswer(4+25)*3=29*3=87. 7-4c Logical Operators: AND, OR, and NOT In the real world, a search of data normally involves multiple conditions. For exam- ple, when you are buying a new house, you look for a certain area, a certain number of bedrooms, bathrooms, stories, and so on. In the same way, SQL allows you to include multiple conditions in a query through the use of logical operators. The logical operators are AND, OR, and NOT. For example, if you want a list of the table contents for either the V_CODE = 21344 or the V_CODE = 24288, you can use the OR logical operator, as in the following command sequence: SELECT FROM WHERE P_DESCRIPT, P_INDATE, P_PRICE, V_CODE PRODUCT V_CODE = 21344 OR V_CODE = 24288; rules of precedence Basic algebraic rules that specify the order in which operations are performed. For example, operations within parentheses are executed first, so in the equation 2 + (3 × 5), the multiplication portion is calculated first, making the correct answer 17. OR The SQL logical operator used to link multiple conditional expressions in a WHERE or HAVING clause. It requires only one of the conditional expressions to be true. AND The SQL logical operator used to link multiple conditional expressions in a WHERE or HAVING clause. It requires that all conditional expressions evaluate to true. This command generates the six rows shown in Figure 7.12 that match the logical restriction. Chapter 7 Introduction to Structured Query Language (SQL) 277 FIGURE7.12 SELECTEDPRODUCTTABLEATTRIBUTES:THELOGICALOR The logical operator AND has the same SQL syntax requirement as OR. The following command generates a list of all rows for which P_PRICE is less than $50 and for which P_INDATE is a date occurring after January 15, 2016: SELECT P_DESCRIPT, P_INDATE, P_PRICE, V_CODE FROM PRODUCT WHERE P_PRICE < 50 AND P_INDATE > ’15-Jan-2016′;
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

278 Part 3
Advanced Design and Implementation
This command produces the output shown in Figure 7.13.
You can combine the logical OR with the logical AND to place further restrictions on the output. For example, suppose that you want a table listing for the following conditions:
• The P_INDATE is after January 15, 2016, and the P_PRICE is less than $50.
• Or the V_CODE is 24288.
The required listing can be produced by using the following:
SELECT P_DESCRIPT, P_INDATE, P_PRICE, V_CODE FROM PRODUCT
WHERE (P_PRICE < 50 AND P_INDATE > ’15-Jan-2016′) OR V_CODE = 24288;
Note the use of parentheses to combine logical restrictions. Where you place the parentheses depends on how you want the logical restrictions to be executed. Conditions listed within parentheses are always executed first. The preceding query yields the output shown in Figure 7.14.
FIGURE7.14 SELECTEDPRODUCTTABLEATTRIBUTES:THELOGICALAND AND OR
Note that the three rows with the V_CODE = 24288 are included regardless of the P_INDATE and P_PRICE entries for those rows.
The use of the logical operators OR and AND can become quite complex when numerous restrictions are placed on the query. In fact, a specialty field in mathematics known as Boolean algebra is dedicated to the use of logical operators.
The logical operator NOT is used to negate the result of a conditional expression. That is, in SQL, all conditional expressions evaluate to true or false. If an expression is true, the row is selected; if an expression is false, the row is not selected. The NOT logical opera- tor is typically used to find the rows that do not match a certain condition. For example, if you want to see a listing of all rows for which the vendor code is not 21344, use the following command sequence:
FIGURE7.13 SELECTEDPRODUCTTABLEATTRIBUTES:THELOGICALAND
SELECT FROM WHERE
*
PRODUCT
NOT (V_CODE = 21344);
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Boolean algebra
A branch of mathematics that uses the logical operators OR, AND, and NOT.
NOT
A SQL logical operator that negates a given predicate.
Copyright 2017 Cengage Lea Editorial review has deemed that

Note that the condition is enclosed in parentheses; that practice is optional, but it is highly recommended for clarity. The logical operator NOT can be combined with AND and OR.
Note
7-4d Special Operators
ANSI-standard SQL allows the use of special operators in conjunction with the WHERE
clause. These special operators include:
BETWEEN: Used to check whether an attribute value is within a range
IS NULL: Used to check whether an attribute value is null
LIKE: Used to check whether an attribute value matches a given string pattern
IN: Used to check whether an attribute value matches any value within a value list EXISTS: Used to check whether a subquery returns any rows
The BETWEEN Special Operator If you use software that implements a standard SQL, the operator BETWEEN may be used to check whether an attribute value is within a range of values. For example, if you want to see a listing for all products whose prices are between $50 and $100, use the following command sequence:
SELECT *
FROM PRODUCT
WHERE P_PRICE BETWEEN 50.00 AND 100.00;
Note
BETWEEN
In SQL, a special comparison operator used to check whether a value is within a range of specified values.
IS NULL
In SQL, a comparison operator used to check whether an attribute has a value.
LIKE
In SQL, a comparison operator used to check whether an attribute’s text value matches a specified string pattern.
IN
In SQL, a comparison operator used to check whether a value is among a list of specified values.
EXISTS
In SQL, a comparison operator that checks whether a subquery returns any rows.
Chapter 7 Introduction to Structured Query Language (SQL) 279
If your SQL version does not support the logical NOT, you can generate the required output by using the following condition:
WHERE V_CODE <> 21344
If your version of SQL does not support <>, use:
WHERE V_CODE != 21344
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Note to Oracle Users
When using the BETWEEN special operator, always specify the lower-range value first. The WHERE clause of the command above is interpreted as:
WHERE P_PRICE >= 50 AND P_PRICE <= 100 If you list the higher-range value first, the DBMS will return an empty result set because the WHERE clause will be interpreted as: WHERE P_PRICE >= 100 and P_PRICE <= 50 Clearly, no product can have a price that is both greater than 100 and simultaneously less than 50, Therefore, no rows can possibly match the criteria. 280 Part 3 Advanced Design and Implementation Note Note that SQL uses a special operator to test for nulls. Why? Couldn’t you just enter a condition such as “V_CODE = NULL”? No. Technically, NULL is not a “value” the way the number 0 or the blank space is; instead, a NULL is a special property of an attribute that represents the absence of any value. The LIKE Special Operator The LIKE special operator is used in conjunction with wildcards to find patterns within string attributes. Standard SQL allows you to use the percent sign ( % ) and underscore ( _ ) wildcard characters to make matches when the entire string is not known: • % means any and all following or preceding characters are eligible. For example: 'J%' includes Johnson, Jones, Jernigan, July, and J-231Q. 'Jo%' includes Johnson and Jones. '%n' includes Johnson and Jernigan. • _ means any one character may be substituted for the underscore. For example: '_23-456-6789' includes 123-456-6789, 223-456-6789, and 323-456-6789. '_23-_56-678_' includes 123-156-6781, 123-256-6782, and 823-956-6788. '_o_es' includes Jones, Cones, Cokes, totes, and roles. For example, the following query would find all VENDOR rows for contacts whose last names begin with Smith. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. If your DBMS does not support BETWEEN, you can use: SELECT FROM WHERE * PRODUCT P_PRICE => 50.00 AND P_PRICE <= 100.00; The IS NULL Special Operator Standard SQL allows the use of IS NULL to check for a null attribute value. For example, suppose that you want to list all products that do not have a vendor assigned (V_CODE is null). Such a null entry could be found by using the following command sequence: SELECT FROM WHERE P_CODE, P_DESCRIPT, V_CODE PRODUCT V_CODE IS NULL; Similarly, if you want to check a null date entry, the command sequence is: SELECT FROM WHERE P_CODE, P_DESCRIPT, P_INDATE PRODUCT P_INDATE IS NULL; Some RDBMSs, such as Microsoft Access, use the wildcard characters * and ? instead of % and _. SELECT FROM WHERE V_NAME, V_CONTACT, V_AREACODE, V_PHONE VENDOR V_CONTACT LIKE 'Smith%'; If you check the original VENDOR data in Figure 7.2 again, you’ll see that this SQL query yields three records: two Smiths and one Smithson. Keep in mind that most SQL implementations yield case-sensitive searches. For exam- ple, Oracle will not yield a result that includes Jones if you use the wildcard search delim- iter 'jo%' in a search for last names; Jones begins with a capital J, and your wildcard search starts with a lowercase j. On the other hand, MS Access searches are not case sensitive. For example, suppose that you typed the following query in Oracle: SELECT FROM WHERE V_NAME, V_CONTACT, V_AREACODE, V_PHONE VENDOR V_CONTACT LIKE 'SMITH%'; No rows will be returned because character-based queries may be case sensitive. That is, an uppercase character has a different ASCII code than a lowercase character, causing SMITH, Smith, and smith to be evaluated as different (unequal) entries. Because the table contains no vendor whose last name begins with SMITH (all uppercase), the 'SMITH%' used in the query cannot be matched. Matches can be made only when the query entry is written exactly like the table entry. Some RDBMSs, such as Microsoft Access, automatically make the necessary con- versions to eliminate case sensitivity. Others, such as Oracle, provide a special UPPER function to convert both table and query character entries to uppercase. (The conversion is done in the computer’s memory only; the conversion has no effect on how the value is actually stored in the table.) So, if you want to avoid a no-match result based on case sensitivity, and if your RDBMS allows the use of the UPPER function, you can generate the same results by using the following query: SELECT FROM WHERE V_NAME, V_CONTACT, V_AREACODE, V_PHONE VENDOR UPPER(V_CONTACT) LIKE 'SMITH%'; The preceding query produces a list that includes all rows containing a last name that begins with Smith, regardless of uppercase or lowercase letter combinations such as Smith, smith, and SMITH. The logical operators may be used in conjunction with the special operators. For instance, the following query: SELECT FROM WHERE V_NAME, V_CONTACT, V_AREACODE, V_PHONE VENDOR V_CONTACT NOT LIKE 'Smith%'; will yield an output of all vendors whose names do not start with Smith. Suppose that you do not know whether a person’s name is spelled Johnson or Johnsen. The wildcard character _ lets you find a match for either spelling. The proper search would be instituted by the following query: SELECT FROM WHERE * VENDOR V_CONTACT LIKE 'Johns_n'; Chapter 7 Introduction to Structured Query Language (SQL) 281 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 282 Part 3 Advanced Design and Implementation Thus, the wildcards allow you to make matches when only approximate spellings are known. Wildcard characters may be used in combinations. For example, the wildcard search based on the string '_l%' can yield the strings “Al”, “Alton”, “Elgin”, “Blakeston”, “blank”, “bloated”, and “eligible”. The IN Special Operator Many queries that would require the use of the logical OR can be more easily handled with the help of the special operator IN. For example, the following query: SELECT * FROM PRODUCT WHERE V_CODE = 21344 OR V_CODE = 24288; can be handled more efficiently with: SELECT FROM WHERE * PRODUCT V_CODE IN (21344, 24288); Note that the IN operator uses a value list. All of the values in the list must be of the same data type. Each of the values in the value list is compared to the attribute—in this case, V_CODE. If the V_CODE value matches any of the values in the list, the row is selected. In this example, the rows selected will be only those in which the V_CODE is either 21344 or 24288. If the attribute used is of a character data type, the list values must be enclosed in single quotation marks. For instance, if the V_CODE had been defined as CHAR(5) when the table was created, the preceding query would have read: SELECT FROM WHERE * PRODUCT V_CODE IN ('21344', '24288'); The IN operator is especially valuable when it is used in conjunction with subqueries. For example, suppose that you want to list the V_CODE and V_NAME of only those vendors who provide products. In that case, you could use a subquery within the IN operator to automatically generate the value list. The query would be: SELECT FROM WHERE V_CODE, V_NAME VENDOR V_CODE IN (SELECT V_CODE FROM PRODUCT); The preceding query will be executed in two steps: 1. TheinnerqueryorsubquerywillgeneratealistofV_CODEvaluesfromthePRODUCT tables. Those V_CODE values represent the vendors who supply products. 2. The IN operator will compare the values generated by the subquery to the V_CODE values in the VENDOR table, and will select only the rows with matching values— that is, the vendors who provide products. The IN special operator will receive additional attention in Chapter 8, where you will learn more about subqueries. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. The EXISTS Special Operator The EXISTS special operator can be used whenever there is a requirement to execute a command based on the result of another query. That is, if a subquery returns any rows, run the main query; otherwise, do not. For example, the following query will list all vendors, but only if there are products to order: SELECT FROM WHERE * VENDOR EXISTS (SELECT * FROM PRODUCT WHERE P_QOH <= P_MIN); The EXISTS special operator is used in the following example to list all vendors, but only if there are products with the quantity on hand, and less than double the minimum quantity: SELECT FROM WHERE * VENDOR EXISTS (SELECT * FROM PRODUCT WHERE P_QOH < P_MIN * 2); The EXISTS special operator will receive additional attention in Chapter 8, where you will learn more about subqueries. 7-5 Additional Data Definition Commands In this section, you will learn how to change table structures by changing attribute char- acteristics and by adding columns. Then you will learn how to make advanced data updates to the new columns. Finally, you will learn how to copy tables or parts of tables and how to delete tables. All changes in the table structure are made by using the ALTER TABLE command fol- lowed by a keyword that produces the specific change you want to make. Three options are available: ADD, MODIFY, and DROP. You use ADD to add a column, MODIFY to change column characteristics, and DROP to delete a column from a table. Most RDBMSs do not allow you to delete a column unless the column does not contain any values; otherwise, such an action might delete crucial data used by other tables. The basic syntax to add or modify columns is: ALTER TABLE tablename {ADD | MODIFY} ( columnname datatype [ {ADD | MODIFY} columnname datatype] ); The ALTER TABLE command can also be used to add table constraints. In those cases, the syntax would be: ALTER TABLE tablename ADD constraint [ ADD constraint ]; where constraint refers to a constraint definition similar to those you learned in Section 7-2f. You could also use the ALTER TABLE command to remove a column or table constraint. The syntax would be as follows: ALTER TABLE tablename DROP {PRIMARY KEY | COLUMN columnname | CONSTRAINT constraintname }; ALTER TABLE The SQL command used to make changes to table structure. When the command is followed by a keyword (ADD or MODIFY), it adds a column or changes column characteristics. Chapter 7 Introduction to Structured Query Language (SQL) 283 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 284 Part 3 Advanced Design and Implementation Note Notice that when removing a constraint, you need to specify it by name, which is one reason you should always name constraints in your CREATE TABLE or ALTER TABLE statement. 7-5a Changing a Column’s Data Type Using the ALTER syntax, the integer V_CODE in the PRODUCT table can be changed to a character V_CODE by using the following command: ALTER TABLE PRODUCT MODIFY (V_CODE CHAR(5)); Some RDBMSs, such as Oracle, do not let you change data types unless the column to be changed is empty. For example, if you want to change the V_CODE field from the cur- rent number definition to a character definition, the preceding command will yield an error message because the V_CODE column already contains data. The error message is easily explained. Remember that the V_CODE in PRODUCT references the V_CODE in VENDOR. If you change the V_CODE data type, the data types do not match, and there is a referential integrity violation, which triggers the error message. If the V_CODE column does not contain data, the preceding command sequence will alter the table structure as expected (if the foreign key reference was not specified during the creation of the PRODUCT table). 7-5b Changing a Column’s Data Characteristics If the column to be changed already contains data, you can make changes in the column’s characteristics if those changes do not alter the data type. For example, if you want to increase the width of the P_PRICE column to nine digits, use the following command: ALTER TABLE PRODUCT MODIFY (P_PRICE DECIMAL(9,2)); If you now list the table contents, you can see that the column width of P_PRICE has increased by one digit. Some DBMSs impose limitations on when it is possible to change attribute charac- teristics. For example, Oracle lets you increase (but not decrease) the size of a column because an attribute modification will affect the integrity of the data in the database. In fact, some attribute changes can be made only when there is no data in any rows for the affected attribute. 7-5c Adding a Column You can alter an existing table by adding one or more columns. In the following example, you add the column named P_SALECODE to the PRODUCT table. (This column will be used later to determine whether goods that have been in inventory for a certain length of time should be placed on special sale.) Suppose that you expect the P_SALECODE entries to be 1, 2, or 3. Because no arith- metic will be performed with the P_SALECODE, the P_SALECODE will be classified Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. as a single-character attribute. Note the inclusion of all required information in the following ALTER command: ALTER TABLE PRODUCT ADD (P_SALECODE CHAR(1)); Online Content If you are using the MS Access databases provided at www.cengagebrain. com, you can track each of the updates in the following sections. For example, look at the cop- ies of the PRODUCT table in the Ch07_SaleCo data- base, one named PROD- UCT_2 and one named PRODUCT_3. Each of the two copies includes the new P_SALECODE col- umn. If you want to see the cumulative effect of all UPDATE commands, you can continue using the PRODUCT table with the P_SALECODE mod- ification and all of the changes you will make in the following sections. (You might even want to use both options, first to examine the individual effects of the update que- ries and then to examine the cumulative effects.) UPDATE SET WHERE PRODUCT P_SALECODE = '2' P_CODE = '1546-QQ2'; Subsequent data can be entered the same way, defining each entry location by its primary key (P_CODE) and its column location (P_SALECODE). For example, if you want to enter the P_SALECODE value '1' for the P_CODE values '2232/QWE' and '2232/ QTY', you use: UPDATE SET WHERE PRODUCT P_SALECODE = '1' P_CODE IN ('2232/QWE', '2232/QTY'); If your RDBMS does not support IN, use the following command: UPDATE SET WHERE PRODUCT P_SALECODE = '1' P_CODE = '2232/QWE' OR P_CODE = '2232/QTY'; Chapter 7 Introduction to Structured Query Language (SQL) 285 When adding a column, be careful not to include the NOT NULL clause for the new column. Doing so will cause an error message; if you add a new column to a table that already has rows, the existing rows will default to a value of null for the new column. Therefore, it is not possible to add the NOT NULL clause for this new column. (Of course, you can add the NOT NULL clause to the table structure after all the data for the new column has been entered and the column no longer contains nulls.) 7-5d Dropping a Column Occasionally, you might want to modify a table by deleting a column. Suppose that you want to delete the V_ORDER attribute from the VENDOR table. You would use the following command: ALTER TABLE VENDOR DROP COLUMN V_ORDER; Again, some RDBMSs impose restrictions on attribute deletion. For example, you may not drop attributes that are involved in foreign key relationships, nor may you delete an attribute if it is the only one in a table. 7-5e Advanced Data Updates To make changes to data in the columns of existing rows, use the UPDATE command. Do not confuse the INSERT and UPDATE commands: INSERT creates new rows in the table, while UPDATE changes rows that already exist. For example, to enter the P_SALE- CODE value '2' in the fourth row, use the UPDATE command together with the primary key P_CODE '1546-QQ2'. Enter the value by using the following command sequence: Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 286 Part 3 Advanced Design and Implementation You can check the results of your efforts by using the following commands: SELECT P_CODE, P_DESCRIPT, P_INDATE, P_PRICE, P_SALECODE FROM PRODUCT; Although the UPDATE sequences just shown allow you to enter values into specified table cells, the process is very cumbersome. Fortunately, if a relationship can be estab- lished between the entries and the existing columns, the relationship can be used to assign values to their appropriate slots. For example, suppose that you want to place sales codes into the table based on the P_INDATE using the following schedule: P_INDATE P_SALECODE before December 25, 2015 2 between January 16, 2016 and February 10, 2016 1 Using the PRODUCT table, the following two command sequences make the appropriate assignments: UPDATE SET WHERE UPDATE SET WHERE PRODUCT P_SALECODE = '2' P_INDATE < '25-Dec-2015'; PRODUCT P_SALECODE = '1' P_INDATE >= ’16-Jan-2016′ AND P_INDATE <='10-Feb-2016'; To check the results of those two command sequences, use: SELECT P_CODE, P_DESCRIPT, P_INDATE, P_PRICE, P_SALECODE FROM PRODUCT; If you have made all of the updates shown in this section using Oracle, your PROD- UCT table should look like Figure 7.15. Make sure that you issue a COMMIT statement to save these changes. The arithmetic operators are particularly useful in data updates. For example, if the quantity on hand in your PRODUCT table has dropped below the minimum desirable value, you will order more of the product. Suppose, for example, that you have ordered 20 units of product 2232/QWE. When the 20 units arrive, you will want to add them to inventory using the following commands: UPDATE SET WHERE PRODUCT P_QOH = P_QOH + 20 P_CODE = ’2232/QWE’; If you want to add 10 percent to the price for all products that have current prices below $50, you can use: UPDATE SET WHERE PRODUCT P_PRICE = P_PRICE * 1.10 P_PRICE < 50.00; If you are using Oracle, issue a ROLLBACK command to undo the changes made by the last two UPDATE statements. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 7 Introduction to Structured Query Language (SQL) 287 FIGURE 7.15 THE CUMULATIVE EFFECT OF THE MULTIPLE UPDATES IN THE PRODUCT TABLE Note 7-5f Copying Parts of Tables As you will discover in later chapters on database design, sometimes it is necessary to break up a table structure into several component parts (or smaller tables). Fortunately, SQL allows you to copy the contents of selected table columns so that the data need not be re-entered manually into the newly created table(s). For example, if you want to copy P_CODE, P_DESCRIPT, P_PRICE, and V_CODE from the PRODUCT table to a new table named PART, you create the PART table structure first, as follows: If you fail to roll back the changes of the preceding UPDATE queries, the output of the subsequent queries will not match the results shown in the figures. Therefore: • If you are using Oracle, use the ROLLBACK command to restore the database to its previous state. • If you are using Access, copy the original Ch07_SaleCo.mdb file from www.cengage brain.com. CREATE TABLE PART( PART_CODE CHAR(8), CHAR(35), DECIMAL(8,2), INTEGER, PART_DESCRIPT PART_PRICE V_CODE PRIMARY KEY (PART_CODE)); Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 288 Part 3 Advanced Design and Implementation Note that the PART column names need not be identical to those of the original table and that the new table need not have the same number of columns as the original table. In this case, the first column in the PART table is PART_CODE, rather than the original P_CODE in the PRODUCT table. Also, the PART table contains only four columns rather than the eight columns in the PRODUCT table. However, column characteristics must match; you cannot copy a character-based attribute into a numeric structure, and vice versa. Next, you need to add the rows to the new PART table, using the PRODUCT table rows and the INSERT command you learned in Section 7–3g. The syntax is: INSERT INTO SELECT FROM target_tablename[(target_columnlist)] source_columnlist source_tablename; Note that the target column list is required if the source column list does not match all of the attribute names and characteristics of the target table (including the order of the columns). Otherwise, you do not need to specify the target column list. In this example, you must specify the target column list in the following INSERT command because the column names of the target table are different: INSERT INTO PART (PART_CODE, PART_DESCRIPT, PART_PRICE, V_CODE) SELECT P_CODE, P_DESCRIPT, P_PRICE, V_CODE FROM PRODUCT; The contents of the PART table can now be examined by using the following query to generate the new PART table’s contents, shown in Figure 7.16: SELECT * FROM PART; SQL provides another way to rapidly create a new table based on selected columns and rows of an existing table. In this case, the new table will copy the attribute names, data characteristics, and rows of the original table. The Oracle version of the command is: CREATE TABLE PART AS SELECT P_CODE AS PART_CODE, P_DESCRIPT AS PART_DESCRIPT, P_PRICE AS PART_PRICE, V_CODE FROM PRODUCT; If the PART table already exists, Oracle will not let you overwrite the existing table. To run this command, you must first delete the existing PART table. (See Section 7-5h.) Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE7.16 PARTTABLEATTRIBUTESCOPIEDFROMTHEPRODUCTTABLE The MS Access version of this command is: SELECT P_CODE AS PART_CODE, P_DESCRIPT AS PART_DESCRIPT, P_PRICE AS PART_PRICE, V_CODE INTO PART FROM PRODUCT; If the PART table already exists, MS Access will ask if you want to delete the existing table and continue with the creation of the new PART table. The SQL command just shown creates a new PART table with PART_CODE, PART_ DESCRIPT, PART_PRICE, and V_CODE columns. In addition, all of the data rows for the selected columns will be copied automatically. However, note that no entity integrity (primary key) or referential integrity (foreign key) rules are automatically applied to the new table. In the next section, you will learn how to define the PK to enforce entity integ- rity and the FK to enforce referential integrity. 7-5g Adding Primary and Foreign Key Designations When you create a new table based on another table, the new table does not include integrity rules from the old table. In particular, there is no primary key. To define the primary key for the new PART table, use the following command: ALTER TABLE PART ADD PRIMARY KEY (PART_CODE); Several other scenarios could leave you without entity and referential integrity. For example, you might have forgotten to define the primary and foreign keys when you created the original tables. Or, if you imported tables from a different database, you might have discovered that the importing procedure did not transfer the integrity rules. In any case, you can re-establish the integrity rules by using the ALTER com- mand. For example, if the PART table’s foreign key has not yet been designated, it can be designated by: ALTER TABLE PART ADD FOREIGN KEY (V_CODE) REFERENCES VENDOR; Alternatively, if neither the PART table’s primary key nor its foreign key has been designated, you can incorporate both changes at once: ALTER TABLE ADD ADD PART PRIMARY KEY (PART_CODE) FOREIGN KEY (V_CODE) REFERENCES VENDOR; Even composite primary keys and multiple foreign keys can be designated in a single SQL command. For example, if you want to enforce the integrity rules for the LINE table shown in Figure 7.1, you can use: ALTER TABLE ADD ADD ADD LINE PRIMARY KEY (INV_NUMBER, LINE_NUMBER) FOREIGN KEY (INV_NUMBER) REFERENCES INVOICE FOREIGN KEY (P_CODE) REFERENCES PRODUCT; Chapter 7 Introduction to Structured Query Language (SQL) 289 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 290 Part 3 Advanced Design and Implementation 7-5h Deleting a Table from the Database A table can be deleted from the database using the DROP TABLE command. For example, you can delete the PART table you just created with the following command: DROP TABLE PART; You can drop a table only if it is not the “one” side of any relationship. If you try to drop a table otherwise, the RDBMS will generate an error message indicating that a foreign key integrity violation has occurred. 7-6 Additional SELECT Query Keywords One of the most important advantages of SQL is its ability to produce complex free- form queries. The logical operators that were introduced earlier to update table contents work just as well in the query environment. In addition, SQL provides useful functions that count, find minimum and maximum values, calculate averages, and so on. Better yet, SQL allows the user to limit queries to only those entries that have no duplicates or entries whose duplicates can be grouped. 7-6a Ordering a Listing The ORDER BY clause is especially useful when the listing order is important to you. The syntax is: SELECT FROM [WHERE [ORDER BY columnlist tablelist conditionlist ] columnlist [ASC | DESC] ]; Although you have the option of declaring the order type—ascending or descending— the default order is ascending. For example, if you want the contents of the PRODUCT table to be listed by P_PRICE in ascending order, use the following commands: SELECT FROM ORDER BY P_CODE, P_DESCRIPT, P_QOH, P_PRICE PRODUCT P_PRICE; The output is shown in Figure 7.17. Note that ORDER BY yields an ascending price listing. Comparing the listing in Figure 7.17 to the actual table contents shown earlier in Figure 7.2, you will see that the lowest-priced product is listed first in Figure 7.17, followed by the next lowest-priced product, and so on. However, although ORDER BY produces a sorted output, the actual table contents are unaffected by the ORDER BY command. To produce the list in descending order, you would enter: SELECT FROM ORDER BY P_CODE, P_DESCRIPT, P_INDATE, P_PRICE PRODUCT P_PRICE DESC; rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. DROP TABLE A SQL command used to delete database objects such as tables, views, indexes, and users. ORDER BY A SQL clause that is useful for ordering the output of a SELECT query (for example, in ascending or descending order). Copyright 2017 Cengage Lea Editorial review has deemed that Chapter 7 Introduction to Structured Query Language (SQL) 291 FIGURE7.17 SELECTEDPRODUCTTABLEATTRIBUTES:ORDEREDBY (ASCENDING) P_PRICE Ordered listings are used frequently. For example, suppose that you want to create a phone directory. It would be helpful if you could produce an ordered sequence (last name, first name, initial) in three stages: 1. ORDER BY last name. 2. Within the last names, ORDER BY first name. 3. Within the first and last names, ORDER BY middle initial. Such a multilevel ordered sequence is known as a cascading order sequence, and it can be created easily by listing several attributes, separated by commas, after the ORDER BY clause. The cascading order sequence is the basis for any telephone directory. To illustrate a cascading order sequence, use the following SQL command on the EMPLOYEE table: SELECT FROM ORDER BY EMP_LNAME, EMP_FNAME, EMP_INITIAL, EMP_AREACODE, EMP_PHONE EMPLOYEE EMP_LNAME, EMP_FNAME, EMP_INITIAL; This command yields the results shown in Figure 7.18. The ORDER BY clause is useful in many applications, especially because the DESC qualifier can be invoked. For example, listing the most recent items first is a standard procedure. Typically, invoice due dates are listed in descending order. Or, if you want to examine budgets, it is probably useful to list the largest budget line items first. You can use the ORDER BY clause in conjunction with other SQL commands, too. For example, note the use of restrictions on date and price in the following command sequence: cascading order sequence A nested ordering sequence for a set of rows, such as a list in which all last names are alphabetically ordered and, within the last names, all first names are ordered. SELECT FROM WHERE AND ORDER BY P_DESCRIPT, V_CODE, P_INDATE, P_PRICE PRODUCT P_INDATE < '21-Jan-2016' P_PRICE <= 50.00 V_CODE, P_PRICE DESC; Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 292 Part 3 Advanced Design and Implementation FIGURE 7.18 TELEPHONE LIST QUERY RESULTS FIGURE7.19 AQUERYBASEDONMULTIPLERESTRICTIONS DISTINCT A SQL clause that produces only a list of values that are different from one another. The output is shown in Figure 7.19. Note that within each V_CODE, the P_PRICE values are in descending order. 7-6b Listing Unique Values How many different vendors are currently represented in the PRODUCT table? A sim- ple listing (SELECT) is not very useful if the table contains several thousand rows and you have to sift through the vendor codes manually. Fortunately, SQL’s DISTINCT clause produces a list of only those values that are different from one another. For example, the command SELECT DISTINCT V_CODE FROM PRODUCT; yields only the different vendor codes (V_CODE) in the PRODUCT table, as shown in Figure 7.20. Note that the first output row shows the null. The placement of nulls does not affect the list contents. In Oracle, you could use ORDER BY V_CODE NULLS FIRST to place nulls at the top of the list. 7-6c Aggregate Functions SQL can perform various mathematical summaries for you, such as counting the num- ber of rows that contain a specified condition, finding the minimum or maximum values Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 7 Introduction to Structured Query Language (SQL) 293 FIGURE7.20 ALISTINGOFDISTINCTV_CODEVALUESINTHEPRODUCT TABLE for a specified attribute, summing the values in a specified column, and averaging the values in a specified column. Those aggregate functions are shown in Table 7.8. Note TABLE 7.8 SOME BASIC SQL AGGREGATE FUNCTIONS FUNCTION OUTPUT To illustrate another standard SQL command format, most of the remaining input and output sequences are presented using the Oracle RDBMS. COUNT The COUNT function is used to tally the number of non-null values of an attribute. COUNT can be used in conjunction with the DISTINCT clause. For example, suppose that you want to find out how many different vendors are in the PRODUCT table. The answer, generated by the first SQL code set shown in Figure 7.21, is 6. Note that the nulls are not counted as V_CODE values. The aggregate functions can be combined with the SQL commands explored earlier. For example, the second SQL command set in Figure 7.21 supplies the answer to the question, “How many vendors referenced in the PRODUCT table have supplied prod- ucts with prices that are less than or equal to $10?” The answer is that three vendors’ products meet the price specification. The COUNT aggregate function uses one parameter within parentheses, generally a col- umn name such as COUNT(V_CODE) or COUNT(P_CODE). The parameter may also be an expression such as COUNT(DISTINCT V_CODE) or COUNT(P_PRICE+10). Using that syntax, COUNT always returns the number of non-null values in the given column. (Whether the column values are computed or show stored table row values is immaterial.) In contrast, the syntax COUNT(*) returns the number of total rows from the query, including COUNT A SQL aggregate function that outputs the number of rows containing not null values for a given column or expression, sometimes used in conjunction with the DISTINCT clause. If the ordering column has nulls, they are listed either first or last, depending on the RDBMS. The ORDER BY clause must always be listed last in the SELECT command sequence. COUNT The number of rows containing non-null values MIN The minimum attribute value encountered in a given column MAX The maximum attribute value encountered in a given column SUM The sum of all values for a given column AVG The arithmetic mean (average) for a specified column Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 294 Part 3 Advanced Design and Implementation FIGURE7.21 COUNTFUNCTIONOUTPUTEXAMPLES Note the rows that contain nulls. In the example in Figure 7.21, SELECT COUNT(P_CODE) FROM PRODUCT and SELECT COUNT(*) FROM PRODUCT will yield the same answer because there are no null values in the P_CODE primary key column. Note that the third SQL command set in Figure 7.21 uses the COUNT(*) command to answer the question, “How many rows in the PRODUCT table have a P_PRICE value less than or equal to $10?” The answer indicates that five products have a listed price that meets the specification. The COUNT(*) aggregate function is used to count rows in a query result set. In contrast, the COUNT(column) aggregate function counts the number of non-null val- ues in a given column. For example, in Figure 7.20, the COUNT(*) function would return a value of 7 to indicate seven rows returned by the query. The COUNT(V_CODE) function would return a value of 6 to indicate the six non-null vendor code values. Note to MS Access Users MS Access does not support the use of COUNT with the DISTINCT clause. If you want to use such queries in MS Access, you must create subqueries with DISTINCT and NOT NULL clauses. For example, the equivalent MS Access queries for the first two queries shown in Figure 7.21 are: SELECT FROM and COUNT(*) (SELECT DISTINCT V_CODE FROM PRODUCT WHERE V_CODE IS NOT NULL) COUNT(*) (SELECT DISTINCT(V_CODE) FROM (SELECT V_CODE, P_PRICE FROM PRODUCT WHERE V_CODE IS NOT NULL AND P_PRICE<10)) SELECT FROM Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. MAX and MIN The MAX and MIN functions help you find answers to problems such as the highest and lowest (maximum and minimum) prices in the PRODUCT table. The highest price, $256.99, is supplied by the first SQL command set in Figure 7.22. The second SQL command set shown in Figure 7.22 yields the minimum price of $4.99. The third SQL command set in Figure 7.22 demonstrates that the numeric functions can be used in conjunction with more complex queries. However, you must remember that the numeric functions yield only one value based on all the values found in the table: a single maximum value, a single minimum value, a single count, or a single average value. It is easy to overlook this warning. For example, examine the question, “Which product has the highest price?” Although that query seems simple enough, the SQL command sequence: SELECT FROM WHERE P_CODE, P_DESCRIPT, P_PRICE PRODUCT P_PRICE = MAX(P_PRICE); MAX A SQL aggregate function that yields the maximum attribute value in a given column. MIN A SQL aggregate function that yields the minimum attribute value in a given column. Chapter 7 Introduction to Structured Query Language (SQL) 295 The two queries are available at www.cengagebrain.com in the Ch07_SaleCo (Access) database. MS Access does add a trailer at the end of the query after you have executed it, but you can delete that trailer the next time you use the query. Subqueries are covered in detail in Chapter 8, Advanced SQL. FIGURE7.22 MAXANDMINOUTPUTEXAMPLES does not yield the expected results because the use of MAX(P_PRICE) on the right side of a comparison operator is incorrect, thus producing an error message. The aggregate function MAX(columnname) can be used only in the column list of a SELECT statement. Also, in a comparison that uses an equality symbol, you can use only a single value to the right of the equals sign. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 296 Part 3 Advanced Design and Implementation Note To answer the question, therefore, you must compute the maximum price first, then compare it to each price returned by the query. To do that, you need a nested query. In this case, the nested query is composed of two parts: • The inner query, which is executed first. • The outer query, which is executed last. (Remember that the outer query is always the first SQL command you encounter—in this case, SELECT.) Using the following command sequence as an example, note that the inner query first finds the maximum price value, which is stored in memory. Because the outer query now has a value to which to compare each P_PRICE value, the query executes properly. SELECT FROM WHERE P_CODE, P_DESCRIPT, P_PRICE PRODUCT P_PRICE = (SELECT MAX(P_PRICE) FROM PRODUCT); The execution of the nested query yields the correct answer, shown below the third (nested) SQL command set in Figure 7.22. The MAX and MIN aggregate functions can also be used with date columns. For exam- ple, to find out which product has the oldest date, you would use MIN(P_INDATE). In the same manner, to find out the most recent product, you would use MAX(P_INDATE). You can use expressions anywhere a column name is expected. Suppose that you want to know what product has the highest inventory value. To find the answer, you can write the following query: SELECT * FROM PRODUCT WHERE P_QOH*P_PRICE = (SELECT MAX(P_QOH*P_PRICE) FROM PRODUCT); SUM The SUM function computes the total sum for any specified attribute, using any condition(s) you have imposed. For example, if you want to compute the total amount owed by your customers, you could use the following command: SELECT SUM(CUS_BALANCE) AS TOTBALANCE FROM CUSTOMER; You could also compute the sum total of an expression. For example, if you want to find the total value of all items carried in inventory, you could use the following: SELECT SUM(P_QOH * P_PRICE) AS TOTVALUE FROM PRODUCT; The total value is the sum of the product of the quantity on hand and the price for all items. (See Figure 7.23.) AVG The AVG function format is similar to those of MIN and MAX and is subject to the same operating restrictions. The first SQL command set in Figure 7.24 shows how a simple average P_PRICE value can be generated to yield the computed average price of 56.42125. The second SQL command set in Figure 7.24 produces five output lines that describe products whose prices exceed the average product price. Note that the second query uses nested SQL commands and the ORDER BY clause examined earlier. arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. SUM A SQL aggregate function that yields the sum of all values for a given column or expression. AVG A SQL aggregate function that outputs the mean average for a specified column or expression. Copyright 2017 Cengage Le Editorial review has deemed that SELECT FROM [WHERE [GROUP BY [HAVING [ORDER BY columnlist tablelist conditionlist ] columnlist ] conditionlist ] columnlist [ASC | DESC] ]; GROUP BY A SQL clause used to create frequency distributions when combined with any of the aggregate functions in a SELECT statement. Chapter 7 Introduction to Structured Query Language (SQL) 297 FIGURE 7.23 THE TOTAL VALUE OF ALL ITEMS IN THE PRODUCT TABLE FIGURE7.24 AVGFUNCTIONOUTPUTEXAMPLES 7-6d Grouping Data In the previous examples, the aggregate functions summarized data across all rows in the given tables. Sometimes, however, you do not want to treat the entire table as a sin- gle collection of data for summarizing. Rows can be grouped into smaller collections quickly and easily using the GROUP BY clause within the SELECT statement. The aggre- gate functions will then summarize the data within each smaller collection. The syntax is The GROUP BY clause is generally used when you have attribute columns combined with aggregate functions in the SELECT statement. For example, to determine the min- imum price for each sales code, use the first SQL command set shown in Figure 7.25. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 298 Part 3 Advanced Design and Implementation FIGURE7.25 GROUPBYCLAUSEOUTPUTEXAMPLES Note The second SQL command set in Figure 7.25 generates the average price within each sales code. Note that the P_SALECODE nulls are included within the grouping. The GROUP BY clause is valid only when used in conjunction with one of the SQL aggregate functions, such as COUNT, MIN, MAX, AVG, and SUM. For example, as shown in the first command set in Figure 7.26, if you try to group the output by using SELECT FROM GROUP BY V_CODE, P_CODE, P_DESCRIPT, P_PRICE PRODUCT V_CODE; you generate a “not a GROUP BY expression” error. However, if you write the pre- ceding SQL command sequence in conjunction with an aggregate function, the GROUP BY clause works properly. The second SQL command sequence in Figure 7.26 properly answers the question, “How many products are supplied by each vendor?” because it uses a COUNT aggregate function. Note that the last output line in Figure 7.26 shows a null for the V_CODE, indicating that two products were not supplied by a vendor. Perhaps those products were produced in-house, or they might have been bought without the use of a vendor, or the person who entered the data might have merely forgotten to enter a vendor code. (Remember that nulls can be the result of many things.) When using the GROUP BY clause with a SELECT statement: • The SELECT’s columnlist must include a combination of column names and aggregate functions. • The GROUP BY clause’s columnlist must include all nonaggregate function columns specified in the SELECT’s columnlist. If required, you could also group by any aggregate function columns that appear in the SELECT’s columnlist. • The GROUP BY clause columnlist can include any columns from the tables in the FROM clause of the SELECT statement, even if they do not appear in the SELECT’s columnlist. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. SELECT FROM GROUP BY HAVING ORDER BY V_CODE, SUM(P_QOH * P_PRICE) AS TOTCOST PRODUCT V_CODE (SUM(P_QOH * P_PRICE) > 500)
SUM(P_QOH * P_PRICE) DESC;
HAVING
A clause applied to the output of a GROUP BY operation to restrict selected rows.
Chapter 7 Introduction to Structured Query Language (SQL) 299
FIGURE7.26 INCORRECTANDCORRECTUSEOFTHEGROUPBYCLAUSE
The GROUP BY Feature’s HAVING Clause A particularly useful extension of the GROUP BY feature is the HAVING clause. The HAVING clause operates very much like the WHERE clause in the SELECT statement. However the WHERE clause applies to col- umns and expressions for individual rows, while the HAVING clause is applied to the out- put of a GROUP BY operation. For example, suppose that you want to generate a listing of the number of products in the inventory supplied by each vendor. However, this time you want to limit the listing to products whose prices average less than $10. The first part of that requirement is satisfied with the help of the GROUP BY clause, as illustrated in the first SQL command set in Figure 7.27. Note that the HAVING clause is used in conjunction with the GROUP BY clause in the second SQL command set in Figure 7.27 to generate the desired result.
If you use the WHERE clause instead of the HAVING clause, the second SQL command set in Figure 7.27 will produce an error message.
You can also combine multiple clauses and aggregate functions. For example, consider the following SQL statement:
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FIGURE7.27 ANAPPLICATIONOFTHEHAVINGCLAUSE
This statement will do the following:
• Aggregate the total cost of products grouped by V_CODE.
• Select only the rows with totals that exceed $500.
• List the results in descending order by the total cost.
Note the syntax used in the HAVING and ORDER BY clauses; in both cases, you must specify the column expression (formula) used in the SELECT statement’s column list, rather than the column alias (TOTCOST). Some RDBMSs allow you to replace the column expression with the column alias, while others do not.
7-7 Joining Database Tables
The ability to combine, or join, tables on common attributes is perhaps the most import- ant distinction between a relational database and other databases. A join is performed when data is retrieved from more than one table at a time. If necessary, review the join definitions and examples in Chapter 3, The Relational Database Model.
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Chapter 7 Introduction to Structured Query Language (SQL) 301
To join tables, you simply list the tables in the FROM clause of the SELECT statement. The DBMS will create the Cartesian product of every table in the FROM clause. How- ever, to get the correct result—that is, a natural join—you must select only the rows in which the common attribute values match. To do this, use the WHERE clause to indicate the common attributes used to link the tables; this WHERE clause is sometimes referred to as the join condition.
The join condition is generally composed of an equality comparison between the foreign key and the primary key of related tables. For example, suppose that you want to join the two tables VENDOR and PRODUCT. Because V_CODE is the foreign key in the PRODUCT table and the primary key in the VENDOR table, the link is established on V_CODE. (See Table 7.9.)
TABLE 7.9
CREATING LINKS THROUGH FOREIGN KEYS
TABLE ATTRIBUTES TO BE SHOWN LINKING ATTRIBUTE
When the same attribute name appears in more than one of the joined tables, the source table of the attributes listed in the SELECT command sequence must be defined. To join the PRODUCT and VENDOR tables, you would use the following, which produces the output shown in Figure 7.28:
PRODUCT
P_DESCRIPT, P_PRICE
V_CODE
VENDOR
V_NAME, V_CONTACT, V_AREACODE, V_PHONE
V_CODE
FIGURE 7.28 THE RESULTS OF A JOIN
SELECT
FROM WHERE
P_DESCRIPT, P_PRICE, V_NAME, V_CONTACT, V_AREACODE, V_PHONE
PRODUCT, VENDOR
PRODUCT.V_CODE = VENDOR.V_CODE;
Your output might be presented in a different order because the SQL command pro- duces a listing in which the order of the rows is not relevant. In fact, you are likely to get
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Note
a different order of the same listing the next time you execute the command. However, you can generate a more predictable list by using an ORDER BY clause:
SELECT
FROM WHERE ORDER BY
PRODUCT.P_DESCRIPT, PRODUCT.P_PRICE, VENDOR.V_NAME, VENDOR.V_CONTACT, VENDOR.V_AREACODE, VENDOR.V_PHONE
PRODUCT, VENDOR PRODUCT.V_CODE = VENDOR.V_CODE PRODUCT.P_PRICE;
In this case, your listing will always be arranged from the lowest price to the highest price.
Table names were used as prefixes in the preceding SQL command sequence. For example, PRODUCT.P_PRICE was used rather than P_PRICE. Most current-generation RDBMSs do not require table names to be used as prefixes unless the same attribute name occurs in several of the tables being joined. In the previous case, V_CODE is used as a foreign key in PROD- UCT and as a primary key in VENDOR; therefore, you must use the table names as prefixes in the WHERE clause. In other words, you can write the previous query as:
SELECT P_DESCRIPT, P_PRICE, V_NAME, V_CONTACT, V_AREACODE,
V_PHONE
FROM PRODUCT, VENDOR WHERE PRODUCT.V_CODE = VENDOR.V_CODE ORDER BY P_PRICE;
Naturally, if an attribute name occurs in several places, its origin (table) must be speci- fied. If you fail to provide such a specification, SQL will generate an error message to indi- cate that you have been ambiguous about the attribute’s origin.
The preceding SQL command sequence joins a row in the PRODUCT table with a row in the VENDOR table, in which the V_CODE values of these rows are the same, as indicated in the WHERE clause’s condition. Because any vendor can deliver any number of ordered products, the PRODUCT table might contain multiple V_CODE entries for each V_CODE entry in the VENDOR table. In other words, each V_CODE in VENDOR can be matched with many V_CODE rows in PRODUCT.
If you do not specify the WHERE clause, the result will be the Cartesian product of PRODUCT and VENDOR. Because the PRODUCT table contains 16 rows and the VENDOR table contains 11 rows, the Cartesian product will yield a listing of (16 × 11) = 176 rows. (Each row in PRODUCT will be joined to each row in the VENDOR table.)
All of the SQL commands can be used on the joined tables. For example, the following command sequence is quite acceptable in SQL and produces the output shown in Figure 7.29.
SELECT P_DESCRIPT, P_PRICE, V_NAME, V_CONTACT, V_AREACODE, V_PHONE
FROM PRODUCT, VENDOR
WHERE PRODUCT.V_CODE = VENDOR.V_CODE AND P_INDATE > ’15-Jan-2016′;
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When joining three or more tables, you need to specify a join condition for each pair of tables. The number of join conditions will always be n–1, where n represents the number of tables listed in the FROM clause. For example, if you have three tables, you must have two join conditions; if you have five tables, you must have four join conditions; and so on.
Remember, the join condition will match the foreign key of a table to the primary key of the related table. For example, using Figure 7.1, if you want to list the customer last name, invoice number, invoice date, and product descriptions for all invoices for cus- tomer 10014, you must type the following:
SELECT FROM WHERE AND
AND
AND ORDER BY
CUS_LNAME, INVOICE.INV_NUMBER, INV_DATE, P_DESCRIPT CUSTOMER, INVOICE, LINE, PRODUCT CUSTOMER.CUS_CODE = INVOICE.CUS_CODE INVOICE.INV_NUMBER = LINE.INV_NUMBER
LINE.P_CODE = PRODUCT.P_CODE CUSTOMER.CUS_CODE = 10014 INV_NUMBER;
Finally, be careful not to create circular join conditions. For example, if Table A is related to Table B, Table B is related to Table C, and Table C is also related to Table A, create only two join conditions: join A with B and B with C. Do not join C with A!
7-7a Joining Tables with an Alias
An alias may be used to identify the source table from which the data is taken. The aliases P and V are used to label the PRODUCT and VENDOR tables in the next com- mand sequence. Any legal table name may be used as an alias. (Also notice that there are no table name prefixes because the attribute listing contains no duplicate names in the SELECT statement.)
SELECT
FROM WHERE ORDER BY
P_DESCRIPT, P_PRICE, V_NAME, V_CONTACT, V_AREACODE, V_PHONE
PRODUCT P, VENDOR V P.V_CODE = V.V_CODE P_PRICE;
7-7b Recursive Joins
An alias is especially useful when a table must be joined to itself in a recursive query. For example, suppose that you are working with the EMP table shown in Figure 7.30. Using the data in the EMP table, you can generate a list of all employees with their managers’
recursive query
A nested query that joins a table to itself.
Chapter 7 Introduction to Structured Query Language (SQL) 303
FIGURE7.29 ANORDEREDANDLIMITEDLISTINGAFTERAJOIN
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304 Part 3
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FIGURE 7.30 THE CONTENTS OF THE EMP TABLE
names by joining the EMP table to itself. In that case, you would also use aliases to differentiate the table from itself. The SQL command sequence would look like this:
SELECT
FROM WHERE ORDER BY
E.EMP_NUM, E.EMP_LNAME, E.EMP_MGR, M.EMP_LNAME
EMP E, EMP M E.EMP_MGR=M.EMP_NUM E.EMP_MGR;
The output of the preceding command sequence is shown in Figure 7.31.
Content
For a complete walk- through example of converting an ER model into a database struc- ture and using SQL commands to create tables, see Appendix D, Converting the ER Model into a Data- base Structure at www. cengagebrain.com.
Note
Online
FIGURE7.31 USINGANALIASTOJOINATABLETOITSELF
In MS Access, you would add AS to the previous SQL command sequence. For example:
SELECT FROM WHERE ORDER BY
E.EMP_NUM,E.EMP_LNAME,E.EMP_MGR,M.EMP_LNAME EMP AS E, EMP AS M
E.EMP_MGR = M.EMP_NUM
E.EMP_MGR;
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• SQL commands can be divided into two overall categories: data definition language (DDL) commands and data manipulation language (DML) commands.
• The ANSI standard data types are supported by all RDBMS vendors in different ways. The basic data types are NUMBER, NUMERIC, INTEGER, CHAR, VARCHAR, and DATE.
• The basic data definition commands allow you to create tables and indexes. Many SQL constraints can be used with columns. The commands are CREATE TABLE, CREATE INDEX, ALTER TABLE, DROP TABLE, and DROP INDEX.
• DML commands allow you to add, modify, and delete rows from tables. The basic DML commands are SELECT, INSERT, UPDATE, DELETE, COMMIT, and ROLLBACK.
• The INSERT command is used to add new rows to tables. The UPDATE command is used to modify data values in existing rows of a table. The DELETE command is used to delete rows from tables. The COMMIT and ROLLBACK commands are used to permanently save or roll back changes made to the rows. Once you COMMIT the changes, you cannot undo them with a ROLLBACK command.
• The SELECT statement is the main data retrieval command in SQL. A SELECT statement has the following syntax:
SELECT FROM [WHERE [GROUP BY [HAVING [ORDER BY
columnlist
tablelist
conditionlist ]
columnlist ]
conditionlist ]
columnlist [ASC | DESC] ];
Chapter 7 Introduction to Structured Query Language (SQL) 305
• The column list represents one or more column names separated by commas. The column list may also include computed columns, aliases, and aggregate functions. A computed column is represented by an expression or formula (for example, P_PRICE * P_QOH). The FROM clause contains a list of table names.
• The WHERE clause can be used with the SELECT, UPDATE, and DELETE state- ments to restrict the rows affected by the DDL command. The condition list rep- resents one or more conditional expressions separated by logical operators (AND, OR, and NOT). The conditional expression can contain any comparison operators (=, >, <, >=, <=, and <>) as well as special operators (BETWEEN, IS NULL, LIKE, IN, and EXISTS).
• Aggregate functions (COUNT, MIN, MAX, and AVG) are special functions that per- form arithmetic computations over a set of rows. The aggregate functions are usually used in conjunction with the GROUP BY clause to group the output of aggregate computations by one or more attributes. The HAVING clause is used to restrict the output of the GROUP BY clause by selecting only the aggregate rows that match a given condition.
• The ORDER BY clause is used to sort the output of a SELECT statement. The ORDER BY clause can sort by one or more columns and can use either ascending or descend- ing order.
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Summary

306 Part 3
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• You can join the output of multiple tables with the SELECT statement. The join operation is performed every time you specify two or more tables in the FROM clause and use a join condition in the WHERE clause to match the foreign key of one table to the primary key of the related table. If you do not specify a join con- dition, the DBMS will automatically perform a Cartesian product of the tables you specify in the FROM clause.
• The natural join uses the join condition to match only rows with equal values in the specified columns.
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
Key Terms
alias
ALTER TABLE
AND
authentication
AVG
BETWEEN
Boolean algebra cascading order sequence COMMIT
COUNT
CREATE INDEX
CREATE TABLE
DELETE
DISTINCT
DROP INDEX DROP TABLE EXISTS FROM GROUP BY HAVING
IN
inner query INSERT
IS NULL
LIKE
MAX
MIN
nested query
NOT
OR
ORDER BY
recursive query reserved words ROLLBACK
rules of precedence schema
SELECT
subquery
SUM
UPDATE
WHERE
wildcard character
Review Questions
1. In a SELECT query, what is the difference between a WHERE clause and a HAVING clause?
2. Explain why the following command would create an error and what changes could be made to fix the error:
SELECT V_CODE, SUM(P_QOH) FROM PRODUCT;
3. What type of integrity is enforced when a primary key is declared?
4. Explain why it might be more appropriate to declare an attribute that contains only digits as a character data type instead of a numeric data type.
5. What is the difference between a column constraint and a table constraint?
6. What are “referential constraint actions”?
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Chapter 7 Introduction to Structured Query Language (SQL) 307
7. Rewrite the following WHERE clause without the use of the IN special operator: WHERE V_STATE IN (‘TN’, ‘FL’, ‘GA’)
8. Explain the difference between an ORDER BY clause and a GROUP BY clause.
9. Explain why the following two commands produce different results:
SELECT DISTINCT COUNT (V_CODE) FROM PRODUCT; SELECT COUNT (DISTINCT V_CODE) FROM PRODUCT;
10. What is the difference between the COUNT aggregate function and the SUM aggregate function?
11. Explain why it would be preferable to use a DATE data type to store date data instead of a character data type.
12. What is a recursive join?
Online Content
Problems 1−25 are based on the Ch07_ConstructCo data- base at www.cengagebrain. com. This database is stored in Microsoft Access format. Oracle, MySQL, and MS SQL Server script files are available at www.cengagebrain.com.
The Ch07_ConstructCo database stores data for a consulting company that tracks all charges to projects. The charges are based on the hours each employee works on each project. The structure and contents of the Ch07_ConstructCo database are shown in Figure P7.1.
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FIGURE P7.1 THE CH07_CONSTRUCTCO DATABASE
Relational diagram Table name: EMPLOYEE
Database name: Ch07_ConstructCo
Table name: JOB
Table name: PROJECT
Table name: ASSIGNMENT
Problems

308 Part 3
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Note that the ASSIGNMENT table in Figure P7.1 stores the JOB_CHG_HOUR val- ues as an attribute (ASSIGN_CHG_HR) to maintain historical accuracy of the data. The JOB_CHG_HOUR values are likely to change over time. In fact, a JOB_CHG_ HOUR change will be reflected in the ASSIGNMENT table. Naturally, the employee primary job assignment might also change, so the ASSIGN_JOB is also stored. Because those attributes are required to maintain the historical accuracy of the data, they are not redundant.
Given the structure and contents of the Ch07_ConstructCo database shown in Figure P7.1, use SQL commands to answer Problems 1–25.
1. Write the SQL code that will create the table structure for a table named EMP_1. This table is a subset of the EMPLOYEE table. The basic EMP_1 table structure is summarized in the following table. (Note that the JOB_CODE is the FK to JOB.)
ATTRIBUTE (FIELD) NAME
DATA DECLARATION
EMP_NUM
CHAR(3)
EMP_LNAME
VARCHAR(15)
EMP_FNAME
VARCHAR(15)
EMP_INITIAL
CHAR(1)
EMP_HIREDATE
DATE
JOB_CODE
CHAR(3)
2. Having created the table structure in Problem 1, write the SQL code to enter the first two rows for the table shown in Figure P7.2.
3. Assuming that the data shown in the EMP_1 table have been entered, write the SQL code that will list all attributes for a job code of 502.
4. Write the SQL code that will save the changes made to the EMP_1 table.
5. Write the SQL code to change the job code to 501 for the person whose employee number (EMP_NUM) is 107. After you have completed the task, examine the results and then reset the job code to its original value.
6. Write the SQL code to delete the row for William Smithfield, who was hired on June 22, 2004, and whose job code is 500. (Hint: Use logical operators to include all of the information given in this problem. Remember, if you are using MySQL, you will have to first disable “safe mode.”)
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FIGURE P7.2 THE CONTENTS OF THE EMP_1 TABLE

7. Write the SQL code that will restore the data to its original status; that is, the table should contain the data that existed before you made the changes in Problems 5 and 6.
8. Write the SQL code to create a copy of EMP_1, naming the copy EMP_2. Then write the SQL code that will add the attributes EMP_PCT and PROJ_NUM to the struc- ture. The EMP_PCT is the bonus percentage to be paid to each employee. The new attribute characteristics are:
EMP_PCT NUMBER(4,2) PROJ_NUM CHAR(3)
[Note: If your SQL implementation allows it, you may use DECIMAL(4,2) or NUMERIC(4,2) rather than NUMBER(4,2).]
9. Write the SQL code to change the EMP_PCT value to 3.85 for the person whose employee number (EMP_NUM) is 103. Next, write the SQL command sequences to change the EMP_PCT values, as shown in Figure P7.9.
10. Using a single command sequence, write the SQL code that will change the proj- ect number (PROJ_NUM) to 18 for all employees whose job classification (JOB_ CODE) is 500.
11. Using a single command sequence, write the SQL code that will change the proj- ect number (PROJ_NUM) to 25 for all employees whose job classification (JOB_ CODE) is 502 or higher. When you finish Problems 10 and 11, the EMP_2 table will contain the data shown in Figure P7.11. (You may assume that the table has been saved again at this point.)
Chapter 7 Introduction to Structured Query Language (SQL) 309
FIGURE P7.9 THE EMP_2 TABLE AFTER THE MODIFICATIONS
FIGURE P7.11 THE EMP_2 TABLE CONTENTS AFTER THE MODIFICATIONS
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12. WritetheSQLcodethatwillchangethePROJ_NUMto14foremployeeswhowere hired before January 1, 1994, and whose job code is at least 501. (You may assume that the table will be restored to its condition preceding this question.)
13. Write the two SQL command sequences required to:
a. Create a temporary table named TEMP_1 whose structure is composed of the EMP_2 attributes EMP_NUM and EMP_PCT.
b. Copy the matching EMP_2 values into the TEMP_1 table.
14. WritetheSQLcommandthatwilldeletethenewlycreatedTEMP_1tablefromthe database.
15. WritetheSQLcoderequiredtolistallemployeeswhoselastnamesstartwithSmith. In other words, the rows for both Smith and Smithfield should be included in the listing. Assume case sensitivity.
16. Using the EMPLOYEE, JOB, and PROJECT tables in the Ch07_ConstructCo data- base (see Figure P7.1), write the SQL code that will produce the results shown in Figure P7.16.
17. WritetheSQLcodethatwillproducethesameinformationthatwasshowninProb- lem 16, but sorted by the employee’s last name.
18. Write the SQL code to find the average bonus percentage in the EMP_2 table you created in Problem 8.
19. Write the SQL code that will produce a listing for the data in the EMP_2 table in ascending order by the bonus percentage.
20. Write the SQL code that will list only the distinct project numbers in the EMP_2 table.
21. WritetheSQLcodetocalculatetheASSIGN_CHARGEvaluesintheASSIGNMENT table in the Ch07_ConstructCo database. (See Figure P7.1.) Note that ASSIGN_ CHARGE is a derived attribute that is calculated by multiplying ASSIGN_CHG_HR by ASSIGN_HOURS.
22. Using the data in the ASSIGNMENT table, write the SQL code that will yield the total number of hours worked for each employee and the total charges stemming from those hours worked. The results of running that query are shown in Figure P7.22.
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FIGURE P7.16 THE QUERY RESULTS FOR PROBLEM 16

23. Write a query to produce the total number of hours and charges for each of the projects represented in the ASSIGNMENT table. The output is shown in Figure P7.23.
24. WritetheSQLcodetogeneratethetotalhoursworkedandthetotalchargesmadeby all employees. The results are shown in Figure P7.24. (Hint: This is a nested query. If you use Microsoft Access, you can use the output shown in Figure P7.22 as the basis for the query that will produce the output shown in Figure P7.24.)
25. Write the SQL code to generate the total hours worked and the total charges made to all projects. The results should be the same as those shown in Figure P7.24. (Hint:
This is a nested query. If you use Microsoft Access, you can use the output shown in Figure P7.23 as the basis for this query.)
The structure and contents of the Ch07_SaleCo database are shown in Figure P7.26. Use this database to answer the following problems. Save each query as QXX, where XX is the problem number.
26. Write a query to count the number of invoices.
27. Write a query to count the number of customers with a balance of more than $500.
28. Generate a listing of all purchases made by the customers, using the output shown in Figure P7.28 as your guide. (Hint: Use the ORDER BY clause to order the resulting rows shown in Figure P7.28.)
Chapter 7 Introduction to Structured Query Language (SQL) 311
FIGURE P7.22 TOTAL HOURS AND CHARGES BY EMPLOYEE
FIGURE P7.23 TOTAL HOUR AND CHARGES BY PROJECT
FIGURE P7.24 TOTAL HOURS AND CHARGES, ALL EMPLOYEES
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Online Content
Problems 26−43 are based on the Ch07_SaleCo database, which is available at www. cengagebrain.com. This data- base is stored in Microsoft Access format. Oracle, MySQL, and MS SQL Server script files are available at www.cengage brain.com.

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FIGURE P7.26 THE CH07_SALECO DATABASE
Relational diagram
Table name: CUSTOMER
Table name: VENDOR
Table name: INVOICE Table name: LINE Table name: PRODUCT
FIGURE P7.28 LIST OF CUSTOMER PURCHASES
29. Using the output shown in Figure P7.29 as your guide, generate a list of cus- tomer purchases, including the subtotals for each of the invoice line numbers. (Hint: Modify the query format used to produce the list of customer purchases in Problem 28, delete the INV_DATE column, and add the derived attribute LINE_ UNITS * LINE_PRICE to calculate the subtotals.)
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Chapter 7 Introduction to Structured Query Language (SQL) 313
FIGUREP7.29 SUMMARYOFCUSTOMERPURCHASESWITHSUBTOTALS
30. Modify the query used in Problem 29 to produce the summary shown in Figure P7.30.
31. Modify the query in Problem 30 to include the number of individual product pur- chases made by each customer. (In other words, if the customer’s invoice is based on three products, one per LINE_NUMBER, you count three product purchases. Note that in the original invoice data, customer 10011 generated three invoices, which contained a total of six lines, each representing a product purchase.) Your output values must match those shown in Figure P7.31.
FIGUREP7.31 CUSTOMERTOTALPURCHASEAMOUNTSANDNUMBEROF PURCHASES
32. Useaquerytocomputetheaveragepurchaseamountperproductmadebyeachcus- tomer. (Hint: Use the results of Problem 31 as the basis for this query.) Your output val- ues must match those shown in Figure P7.32. Note that the average purchase amount is equal to the total purchases divided by the number of purchases per customer.
FIGURE P7.30 CUSTOMER PURCHASE SUMMARY
FIGURE P7.32 AVERAGE PURCHASE AMOUNT BY CUSTOMER
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33. Create a query to produce the total purchase per invoice, generating the results shown in Figure P7.33. The invoice total is the sum of the product purchases in the LINE that corresponds to the INVOICE.
FIGUREP7.33 INVOICETOTALS
34. UseaquerytoshowtheinvoicesandinvoicetotalsinFigureP7.34.(Hint:Groupby the CUS_CODE.)
FIGUREP7.34 INVOICETOTALSBYCUSTOMER
35. Write a query to produce the number of invoices and the total purchase amounts by customer, using the output shown in Figure P7.35 as your guide. (Compare this summary to the results shown in Problem 34.)
FIGUREP7.35 NUMBEROFINVOICESANDTOTALPURCHASEAMOUNTS BY CUSTOMER
36. Using the query results in Problem 35 as your basis, write a query to generate the total number of invoices, the invoice total for all of the invoices, the smallest of the customer purchase amounts, the largest of the customer purchase amounts, and the average of all the customer purchase amounts. (Hint: Check the figure output in Problem 35.) Your output must match Figure P7.36.
FIGUREP7.36 NUMBEROFINVOICES,INVOICETOTALS,MINIMUM, MAXIMUM, AND AVERAGE SALES
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Chapter 7 Introduction to Structured Query Language (SQL) 315
37. Listthebalancesofcustomerswhohavemadepurchasesduringthecurrentinvoice cycle—that is, for the customers who appear in the INVOICE table. The results of this query are shown in Figure P7.37.
38. UsingtheresultsofthequerycreatedinProblem37,provideasummaryofcustomer balance characteristics, as shown in Figure P7.38.
FIGUREP7.38 BALANCESUMMARYOFCUSTOMERSWHOMADE PURCHASES
39. Create a query to find the balance characteristics for all customers, including the total of the outstanding balances. The results of this query are shown in Figure P7.39.
40. Find the listing of customers who did not make purchases during the invoicing period. Your output must match the output shown in Figure P7.40.
FIGUREP7.40 BALANCESOFCUSTOMERSWHODIDNOTMAKE PURCHASES
41. Findthecustomerbalancesummaryforallcustomerswhohavenotmadepurchases during the current invoicing period. The results are shown in Figure P7.41.
FIGURE P7.41 SUMMARY OF CUSTOMER BALANCES FOR CUSTOMERS WHO DID NOT MAKE PURCHASES
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FIGUREP7.37 BALANCESFORCUSTOMERSWHOMADEPURCHASES
FIGURE P7.39 BALANCE SUMMARY FOR ALL CUSTOMERS

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FIGURE P7.42 VALUE OF PRODUCTS CURRENTLY IN INVENTORY
FIGURE P7.43 TOTAL VALUE OF ALL PRODUCTS IN INVENTORY
Online Content
Problems 44−64 are based on the Ch07_LargeCo data- base, which is available at www.cengagebrain.com. This database is stored in Microsoft Access format. Oracle, MySQL, and MS SQL Server script files are avail- able at www.cengagebrain. com.
42. Createaquerythatsummarizesthevalueofproductscurrentlyininventory.Note that the value of each product is a result of multiplying the units currently in inventory by the unit price. Use the ORDER BY clause to match the order shown in Figure P7.42.
43. Using the results of the query created in Problem 42, find the total value of the product inventory. The results are shown in Figure P7.43.
The Ch07_LargeCo database (see Figure P7.44) stores data for a company that sells paint products. The company tracks the sale of products to customers. The database keeps data on customers (LGCUSTOMER), sales (LGINVOICE), products (LGPRODUCT), which products are on which invoices (LGLINE), employees (LGEMPLOYEE), the salary history of each employee (LGSALARY_ HISTORY), departments (LGDEPARTMENT), product brands (LGBRAND), vendors (LGVENDOR), and which vendors supply each product (LGSUPPLIES). Some of the tables contain only a few rows of data, while other tables are quite large; for example, there are only eight departments, but more than 3,300 invoices containing over 11,000 invoice lines. For Problems 45–64, a figure of the correct output for each problem is provided. If the output of the query is very large, only the first several rows of the output are shown.
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Chapter 7 Introduction to Structured Query Language (SQL) 317
FIGURE P7.44 THE CH07_LARGECO ERD
44. Write a query to display the eight departments in the LGDEPARTMENT table.
45. Write a query to display the SKU (stock keeping unit), description, type, base, cat- egory, and price for all products that have a PROD_BASE of water and a PROD_ CATEGORY of sealer (Figure P7.45).
46. Write a query to display the first name, last name, and email address of employees hired from January 1, 2003, to December 31, 2012. Sort the output by last name and then by first name (Figure P7.46).
47. Write a query to display the first name, last name, phone number, title, and department number of employees who work in department 300 or have the title “CLERK I.” Sort the output by last name and then by first name (Figure P7.47).
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FIGURE P7.45 WATER-BASED SEALERS

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FIGUREP7.46 EMPLOYEESHIREDFROM2003–2012
FIGUREP7.47 CLERKSANDEMPLOYEESINDEPARTMENT300
48. Write a query to display the employee number, last name, first name, salary “from” date, salary end date, and salary amount for employees 83731, 83745, and 84039. Sort the output by employee number and salary “from” date (Figure P7.48).
49. Write a query to display the first name, last name, street, city, state, and zip code of any customer who purchased a Foresters Best brand top coat between July 15, 2015, and July 31, 2015. If a customer purchased more than one such product, display the customer’s information only once in the output. Sort the output by state, last name, and then first name (Figure P7.49).
50. Write a query to display the employee number, last name, email address, title, and department name of each employee whose job title ends in the word “ASSOCIATE.” Sort the output by department name and employee title (Figure P7.50).
51. Writeaquerytodisplayabrandnameandthenumberofproductsofthatbrandthat are in the database. Sort the output by the brand name (Figure P7.51).
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FIGURE P7.48 SALARY HISTORY FOR SELECTED EMPLOYEES

Chapter 7 Introduction to Structured Query Language (SQL) 319 FIGUREP7.49 CUSTOMERSWHOPURCHASEDFORESTERSBESTTOP
COAT
52. Write a query to display the number of products in each category that have a water base (Figure P7.52).
53. Write a query to display the number of products within each base and type combination (Figure P7.53).
54. Writeaquerytodisplaythetotalinventory—thatis,thesumofallproductsonhand for each brand ID. Sort the output by brand ID in descending order (Figure P7.54).
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FIGUREP7.50 EMPLOYEESWITHTHETITLEOFASSOCIATE
FIGURE P7.51 NUMBER OF PRODUCTS OF EACH BRAND

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FIGUREP7.52 NUMBEROFWATER-BASEDPRODUCTSINEACH CATEGORY
55. Write a query to display the brand ID, brand name, and average price of products of each brand. Sort the output by brand name. (Results are shown with the average price rounded to two decimal places.) (Figure P7.55.)
56. Writeaquerytodisplaythedepartmentnumberandmostrecentemployeehiredate for each department. Sort the output by department number (Figure P7.56).
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FIGUREP7.53 NUMBEROFPRODUCTSOFEACHBASEANDTYPE
FIGURE P7.54 TOTAL INVENTORY OF EACH BRAND OF PRODUCTS
FIGURE P7.55 AVERAGE PRICE OF PRODUCTS OF EACH BRAND
FIGURE P7.56 MOST RECENT HIRE IN EACH DEPARTMENT

57. Write a query to display the employee number, first name, last name, and largest salary amount for each employee in department 200. Sort the output by largest salary in descending order (Figure P7.57).
FIGURE P7.57 LARGEST SALARY AMOUNT FOR EACH EMPLOYEE IN DEPARTMENT 200
58. Write a query to display the customer code, first name, last name, and sum of all invoice totals for customers with cumulative invoice totals greater than $1,500. Sort the output by the sum of invoice totals in descending order (Figure P7.58).
FIGUREP7.58 LISTOFCUSTOMERSWITHCUMULATIVEPURCHASESOF MORE THAN $1,500
Chapter 7 Introduction to Structured Query Language (SQL) 321
59. Write a query to display the department number, department name, department phone number, employee number, and last name of each department manager. Sort the output by department name (Figure P7.59).
FIGURE P7.59 DEPARTMENT MANAGERS
60. Write a query to display the vendor ID, vendor name, brand name, and number of products of each brand supplied by each vendor. Sort the output by vendor name and then by brand name (Figure P7.60).
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FIGURE P7.60 NUMBER OF PRODUCTS OF EACH BRAND SUPPLIED BY EACH VENDOR
61. Write a query to display the employee number, last name, first name, and sum of invoice totals for all employees who completed an invoice. Sort the output by employee last name and then by first name (Figure P7.61).
FIGURE P7.61 TOTAL VALUE OF INVOICES COMPLETED BY EACH EMPLOYEE
62. Writeaquerytodisplaythelargestaverageproductpriceofanybrand(FigureP7.62).
63. Write a query to display the brand ID, brand name, brand type, and average price of products for the brand that has the largest average product price (Figure P7.63).
64. Write a query to display the manager name, department name, department phone number, employee name, customer name, invoice date, and invoice total for the department manager of the employee who made a sale to a customer whose last name is Hagan on May 18, 2015 (Figure P7.64).
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FIGURE P7.62 LARGEST AVERAGE BRAND PRICE
FIGUREP7.63 BRANDWITHHIGHESTAVERAGEPRICE

Chapter 7 Introduction to Structured Query Language (SQL) 323 FIGUREP7.64 MANAGEROFEMPLOYEEMAKINGASALETOCUSTOMER
HAGAN
The CIS Department at Tiny College maintains the Free Access to Current Technology (FACT) library of ebooks. FACT is a collection of current technology ebooks for use by faculty and stu- dents. Agreements with the publishers allow patrons to electronically check out a book, which gives them exclusive access to the book online through the FACT website, but only one patron at a time can have access to a book. A book must have at least one author but can have many. An author must have written at least one book to be included in the system, but may have writ- ten many. A book may have never been checked out, but can be checked out many times by the same patron or different patrons over time. Because all faculty and staff in the department are given accounts at the online library, a patron may have never checked out a book or they may have checked out many books over time. To simplify determining which patron currently has a given book checked out, a redundant relationship between BOOK and PATRON is main- tained. The ERD for this system is shown in Figure P7.65 and should be used to answer Prob- lems 65–95. For Problems 66–95, a figure of the correct output is provided for each problem. If the output of the query is very large, only the first several rows of the output are shown.
65. Writeaquerythatdisplaysthebooktitle,costandyearofpublicationforeverybook in the system.
FIGURE P7.65 THE CH07_FACT ERD
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66. Write a query that displays the first and last name of every patron (Figure P7.66). (50 rows)
67. Writeaquerytodisplaythecheckoutnumber,checkoutdate,andduedateforevery book that has been checked out (Figure P7.67). (68 rows)
68. Write a query to display the book number, book title, and year of publication for every book (Figure P7.68).
69. Write a query to display the different years in which books have been published. Include each year only once (Figure P7.69).
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FIGURE P7.66 ALL PATRON NAMES
FIGURE P7.67 ALL CHECKOUTS
FIGURE P7.68 TITLE AND YEAR FOR ALL BOOKS
FIGUREP7.69 UNIQUEBOOKYEARS

Chapter 7 Introduction to Structured Query Language (SQL) 325
70. Write a query to display the different subjects on which FACT has books. Include
each subject only once (Figure P7.70).
71. Writeaquerytodisplaythebooknumber,title,andcostofeachbook(FigureP7.71).
72. Write a query to display the checkout number, book number, patron ID, checkout date, and due date for every checkout that has ever occurred in the system. Sort the results by checkout date in descending order (Figure P7.72). (68 rows)
73. Write a query to display the book title, year, and subject for every book. Sort the results by book subject in ascending order, year in descending order, and then title in ascending order (Figure P7.73).
74. Writeaquerytodisplaythebooknumber,title,andyearofpublicationforallbooks published in 2012 (Figure P7.74).
75. Writeaquerytodisplaythebooknumber,title,andyearofpublicationforallbooks in the “Database” subject (Figure P7.75).
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FIGURE P7.70 UNIQUE BOOK SUBJECTS
FIGURE P7.71 TITLE AND REPLACEMENT COST FOR BOOKS
FIGURE P7.72 CHECKOUTS BY DATE

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FIGURE P7.73 BOOKS BY CASCADING SORT
FIGUREP7.74 BOOKSPUBLISHEDIN2012
FIGURE P7.75 DATABASE BOOKS
76. Write a query to display the checkout number, book number, and checkout date of all books checked out before April 5, 2015 (Figure P7.76).
77. Writeaquerytodisplaythebooknumber,title,andyearofpublicationofallbooks published after 2013 and on the “Programming” subject (Figure P7.77).
78. Writeaquerytodisplaythebooknumber,title,yearofpublication,subject,andcost for all books that are on the subjects of “Middleware” or “Cloud,” and that cost more than $70 (Figure P7.78).
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FIGUREP7.76 CHECKOUTSBEFOREAPRIL5TH
FIGURE P7.77 NEWER BOOKS ON PROGRAMMING

Chapter 7 Introduction to Structured Query Language (SQL) 327
FIGURE P7.78 EXPENSIVE MIDDLEWARE OR CLOUD BOOKS
79. Write a query to display the author ID, first name, last name, and year of birth for all authors born in the decade of the 1980s (Figure P7.79).
80. Writeaquerytodisplaythebooknumber,title,andyearofpublicationforallbooks that contain the word “Database” in the title, regardless of how it is capitalized (Figure P7.80).
81. Write a query to display the patron ID, first and last name of all patrons who are students (Figure P7.81). (44 rows)
82. Write a query to display the patron ID, first and last name, and patron type for all patrons whose last name begins with the letter “C” (Figure P7.82).
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FIGUREP7.79 AUTHORSBORNINTHE1980S
FIGUREP7.80 BOOKTITLESCONTAININGDATABASE
FIGURE P7.81 STUDENT PATRONS

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FIGUREP7.82 PATRONSWHOSELASTNAMESTARTSWITH’C’
83. WriteaquerytodisplaytheauthorID,firstandlastnameofallauthorswhoseyear of birth is unknown (Figure P7.83).
84. WriteaquerytodisplaytheauthorID,firstandlastnameofallauthorswhoseyear of birth is known (Figure P7.84).
85. Write a query to display the checkout number, book number, patron ID, checkout date, and due date for all checkouts that have not yet been returned. Sort the results by book number (Figure P7.85).
86. WriteaquerytodisplaytheauthorID,firstname,lastname,andyearofbirthforall authors. Sort the results in descending order by year of birth, and then in ascending order by last name (Figure P7.86). (Note: Some DBMS sort NULLs as being large and some DBMS sort NULLs as being small.)
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FIGUREP7.83 AUTHORSWITHUNKNOWNBIRTHYEAR
FIGUREP7.84 AUTHORSWITHKNOWNBIRTHYEAR
FIGURE P7.85 UNRETURNED CHECKOUTS

Chapter 7 Introduction to Structured Query Language (SQL) 329
FIGUREP7.86 AUTHORSBYBIRTHYEAR
87. Write a query to display the number of books in the FACT system (Figure P7.87).
88. Write a query to display the number of different book subjects in the FACT system (Figure P7.88).
89. Write a query to display the number of books that are available (not currently checked out) (Figure P7.89).
90. Write a query to display the highest book cost in the system (Figure P7.90).
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FIGURE P7.87 NUMBER OF BOOKS
FIGURE P7.88 NUMBER OF DIFFERENT SUBJECTS
FIGURE P7.89 NUMBER OF BOOKS NOT CURRENTLY CHECKED OUT
FIGURE P7.90 MOST EXPENSIVE BOOK PRICE

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91. Write a query to display the lowest book cost in the system (Figure P7.91).
92. Writeaquerytodisplaythenumberofdifferentpatronswhohaveevercheckedout a book (Figure P7.92).
93. Write a query to display the subject and the number of books in each subject. Sort the results by the number of books in descending order, then by subject name in ascending order (Figure P7.93).
94. Write a query to display the author ID and the number of books written by that author. Sort the results in descending order by number of books, then in ascending order by author ID (Figure P7.94).
95. Write a query to display the total value of all books in the library (Figure P7.95).
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FIGURE P7.91 LEAST EXPENSIVE BOOK PRICE
FIGUREP7.92 DIFFERENTPATRONSTOCHECKOUTABOOK
FIGURE P7.93 NUMBER OF BOOKS PER SUBJECT
FIGURE P7.94 NUMBER OF BOOKS PER AUTHOR

Chapter 7 Introduction to Structured Query Language (SQL) 331
FIGURE P7.95 TOTAL OF ALL BOOKS
EliteVideo is a startup company providing concierge DVD kiosk service in upscale neighborhoods. EliteVideo can own several copies (VIDEO) of each movie (MOVIE). For example, a kiosk may have 10 copies of the movie Twist in the Wind. In the database, Twist in the Wind would be one MOVIE, and each copy would be a VIDEO. A rental transaction (RENTAL) involves one or more videos being rented to a member (MEM- BERSHIP). A video can be rented many times over its lifetime; therefore, there is an M:N relationship between RENTAL and VIDEO. DETAILRENTAL is the bridge table to resolve this relationship. The complete ERD is provided in Figure P7.96.
96. Write the SQL code to create the table structures for the entities shown in Figure P7.96. The structures should contain the attributes specified in the ERD. Use data types that are appropriate for the data that will need to be stored in each attribute. Enforce primary key and foreign key constraints as indicated by the ERD.
97. Thefollowingtablesprovideaverysmallportionofthedatathatwillbekeptinthe database. The data needs to be inserted into the database for testing purposes. Write the INSERT commands necessary to place the following data in the tables that were created in Problem 96.
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FIGURE P7.96 THE CH07_MOVIECO ERD
Cases

332 Part 3 Advanced Design and Implementation
MEMBERSHIP
MEM NUM
MEM_ FNAME
MEM_ LNAME
MEM_STREET
MEM_CITY
MEM_ STATE
MEM_ ZIP
MEM_ BALANCE
102
Tami
Dawson
2632 Takli Circle
Norene
TN
37136
11
103
Curt
Knight
4025 Cornell Court
Flatgap
KY
41219
6
104
Jamal
Melendez
788 East 145th Avenue
Quebeck
TN
38579
0
105
Iva
Mcclain
6045 Musket Ball Circle
Summit
KY
42783
15
106
Miranda
Parks
4469 Maxwell Place
Germantown
TN
38183
0
107
Rosario
Elliott
7578 Danner Avenue
Columbia
TN
38402
5
108
Mattie
Guy
4390 Evergreen Street
Lily
KY
40740
0
109
Clint
Ochoa
1711 Elm Street
Greeneville
TN
37745
10
110
Lewis
Rosales
4524 Southwind Circle
Counce
TN
38326
0
111
Stacy
Mann
2789 East Cook Avenue
Murfreesboro
TN
37132
8
112
Luis
Trujillo
7267 Melvin Avenue
Heiskell
TN
37754
3
113
Minnie
Gonzales
6430 Vasili Drive
Williston
TN
38076
0
RENTAL
RENT_NUM
RENT_DATE
MEM_NUM
1001
01-MAR-16
103
1002
01-MAR-16
105
1003
02-MAR-16
102
1004
02-MAR-16
110
1005
02-MAR-16
111
1006
02-MAR-16
107
1007
02-MAR-16
104
1008
03-MAR-16
105
1009
03-MAR-16
111
DETAILRENTAL
RENT_ NUM
VID_NUM
DETAIL_FEE
DETAIL_DUEDATE
DETAIL_RETURNDATE
DETAIL_ DAILYLATEFEE
1001
34342
2
04-MAR-16
02-MAR-16
1001
61353
2
04-MAR-16
03-MAR-16
1
1002
59237
3.5
04-MAR-16
04-MAR-16
3
1003
54325
3.5
04-MAR-16
09-MAR-16
3
1003
61369
2
06-MAR-16
09-MAR-16
1
1003
61388
0
06-MAR-16
09-MAR-16
1
1004
44392
3.5
05-MAR-16
07-MAR-16
3
1004
34367
3.5
05-MAR-16
07-MAR-16
3
1004
34341
2
07-MAR-16
07-MAR-16
1
1005
34342
2
07-MAR-16
05-MAR-16
1
1005
44397
3.5
05-MAR-16
05-MAR-16
3
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Chapter 7 Introduction to Structured Query Language (SQL) 333
DETAILRENTAL
RENT_ NUM
VID_NUM
DETAIL_FEE
DETAIL_DUEDATE
DETAIL_RETURNDATE
DETAIL_ DAILYLATEFEE
1006
34366
3.5
05-MAR-16
04-MAR-16
3
1006
61367
2
07-MAR-16
1
1007
34368
3.5
05-MAR-16
3
1008
34369
3.5
05-MAR-16
05-MAR-16
3
1009
54324
3.5
05-MAR-16
3
1001
34366
3.5
04-MAR-16
02-MAR-16
3
VIDEO
VID_NUM VID_INDATE MOVIE_NUM
54321 18-JUN-15 1234
54324 18-JUN-15 1234
54325 18-JUN-15 1234
34341 22-JAN-14 1235
34342 22-JAN-14 1235
34366 02-MAR-16 1236
34367 02-MAR-16 1236
34368 02-MAR-16 1236
34369 02-MAR-16 1236
44392 21-OCT-15 1237
44397 21-OCT-15 1237
59237 14-FEB-16 1237
61388 25-JAN-14 1239
61353 28-JAN-13 1245
61354 28-JAN-13 1245
61367 30-JUL-15 1246
61369 30-JUL-15 1246
MOVIE
MOVIE_ NUM
MOVIE_TITLE
MOVIE_YEAR
MOVIE_COST
MOVIE_GENRE
PRICE_CODE
1234
The Cesar Family Christmas
2014
39.95
FAMILY
2
1235
Smokey Mountain Wildlife
2011
59.95
ACTION
1
1236
Richard Goodhope
2015
59.95
DRAMA
2
1237
Beatnik Fever
2014
29.95
COMEDY
2
1238
Constant Companion
2015
89.95
DRAMA
1239
Where Hope Dies
2005
25.49
DRAMA
3
1245
Time to Burn
2012
45.49
ACTION
1
1246
What He Doesn’t Know
2013
58.29
COMEDY
1
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334 Part 3
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PRICE
PRICE_CODE
PRICE_DESCRIPTION
PRICE_RENTFEE
PRICE_DAILYLATEFEE
1
Standard
2
1
2
New Release
3.5
3
3
Discount
1.5
1
4
Weekly Special
1
.5
For Questions 98–127, use the tables that were created in Problem 96 and the data that was loaded into those tables in Problem 97.
98. Write the SQL command to save the rows inserted in Problem 97.
99. WritetheSQLcommandtochangethemovieyearformovienumber1245to2013.
100.Write the SQL command to change the price code for all action movies to price code 3.
101.Write a single SQL command to increase all price rental fee values in the PRICE table by $0.50.
102.Write the SQL command to save the changes made to the PRICE and MOVIE tables in Problems 98–101.
103.Write a query to display the movie title, movie year, and movie genre for all movies. (The results are shown in Figure P7.103.)
104.Write a query to display the movie year, movie title, and movie cost sorted by movie year in descending order. (The results are shown in Figure P7.104.)
105.Write a query to display the movie title, movie year, and movie genre for all movies sorted by movie genre in ascending order, then sorted by movie year in descending order within genre. (The results are shown in Figure P7.105.)
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FIGURE P7.103 ALL MOVIES
FIGUREP7.104 MOVIESBYYEAR

Chapter 7 Introduction to Structured Query Language (SQL) 335
FIGUREP7.105 MOVIESWITHMULTICOLUMNSORT
106.Write a query to display the movie number, movie title, and price code for all movies with a title that starts with the letter R. (The results are shown in Figure P7.106.)
107.Write a query to display the movie title, movie year, and movie cost for all movies that contain the word hope in the title. Sort the results in ascending order by title. (The results are shown in Figure P7.107.)
108.Write a query to display the movie title, movie year, and movie genre for all action movies. (The results are shown in Figure P7.108.)
109.Write a query to display the movie number, movie title, and movie cost for all movies that cost more than $40. (The results are shown in Figure P7.109.)
110.Write a query to display the movie number, movie title, movie cost, and movie genre for all action or comedy movies that cost less than $50. Sort the results in ascending order by genre. (The results are shown in Figure P7.110.)
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FIGUREP7.106 MOVIESSTARTINGWITHR
FIGUREP7.107 MOVIESWITH“HOPE”
FIGURE P7.108 ACTION MOVIES
FIGUREP7.109 MOVIESCOSTINGMORETHAN$40

336 Part 3
Advanced Design and Implementation
FIGUREP7.110 ACTIONORCOMEDYMOVIESLESSTHAN$50
111.Write a query to display the membership number, name, street, state, and balance for all members in Tennessee (TN), with a balance less than $5, and whose street name ends in “Avenue”. (The results are shown in Figure P7.111.)
112.Write a query to display the movie genre and the number of movies in each genre. (The results are shown in Figure P7.112.)
113.Write a query to display the average cost of all the movies. (The results are shown in Figure P7.113.)
114.Write a query to display the movie genre and average cost of movies in each genre. (The results are shown in Figure P7.114.)
115.Write a query to display the movie title, movie genre, price description, and price rental fee for all movies with a price code. (The results are shown in Figure P7.115.)
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FIGUREP7.111 MEMBERSWITHMULTIPLERESTRICTIONS
FIGURE P7.112 NUMBER OF MOVIES IN GENRE
FIGURE P7.113 AVERAGE MOVIE COST
FIGURE P7.114 AVERAGE COST BY GENRE

116.Write a query to display the movie genre and average rental fee for movies in each genre that have a price. (The results are shown in Figure P7.116.)
117.Write a query to display the movie title and breakeven amount for each movie that has a price. The breakeven amount is the movie cost divided by the price rental fee for each movie that has a price; it determines the number of rentals needed to break even on the purchase of the movie. (The results are shown in Figure P7.117.)
Chapter 7 Introduction to Structured Query Language (SQL) 337
FIGURE P7.115 RENTAL FEES FOR MOVIES
FIGURE P7.116 AVERAGE RENTAL FEE BY GENRE
FIGURE P7.117 BREAKEVEN RENTALS
118.Write a query to display the movie title and movie year for all movies that have a price code. (The results are shown in Figure P7.118.)
FIGUREP7.118 MOVIESWITHAPRICE
119.Write a query to display the movie title, movie genre, and movie cost for all movies that cost between $44.99 and $49.99. (The results are shown in Figure P7.119.)
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338 Part 3
Advanced Design and Implementation
FIGUREP7.119 MOVIESCOSTSWITHINARANGE
120.Write a query to display the movie title, price description, price rental fee, and genre for all movies that are in the genres of family, comedy, or drama. (The results are shown in Figure P7.120.)
121.Write a query to display the membership number, first name, last name, and balance of the memberships that have a rental. (The results are shown in Figure P7.121.)
122.Write a query to display the minimum balance, maximum balance, and aver- age balance for memberships that have a rental. (The results are shown in Figure P7.122.)
123.Write a query to display the rental number, rental date, video number, movie title, due date, and return date for all videos that were returned after the due date. Sort the results by rental number and movie title. (The results are shown in Figure P7.123.)
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FIGUREP7.120 MOVIESWITHINSPECIFICGENRES
FIGUREP7.121 BALANCESOFMEMBERSHIPSWITHRENTALS
FIGUREP7.122 MINIMUM,MAXIMUM,ANDAVERAGEBALANCES

Chapter 7 Introduction to Structured Query Language (SQL) 339
FIGUREP7.123 LATEVIDEORETURNS
124.Write a query to display the rental number, rental date, movie title, and detail fee for each movie that was returned on or before the due date. (The results are shown in Figure P7.124.)
FIGURE P7.124 ACTUAL RENTAL FEES CHARGED
125.Write a query to display the movie number, movie genre, average cost of movies in that genre, cost of the individual movie, and the percentage difference between the average movie cost and the individual movie cost. The results are shown in Figure P7.125. The percentage difference is the cost of the individual movie minus the aver- age cost of movies in that genre, divided by the average cost of movies in that genre multiplied by 100. For example, if the average cost of movies in the family genre is $25 and a given family movie costs $26, then the calculation would be [(26 − 25) / 25 * 100], or 4.00 percent. In this case, the individual movie costs 4 percent more than the average family movie.
FIGURE P7.125 MOVIE DIFFERENCES FROM GENRE AVERAGE
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Chapter 8
Advanced SQL
In this chapter, you will learn:
• How to use the advanced SQL JOIN operator syntax
• About the different types of subqueries and correlated queries
• How to use SQL functions to manipulate dates, strings, and other data
• About the relational set operators UNION, UNION ALL, INTERSECT, and MINUS • How to create and use views and updatable views
• How to create and use triggers and stored procedures
• How to create embedded SQL
Preview
In Chapter 7, Introduction to Structured Query Language (SQL), you learned the basic SQL data definition and data manipulation commands. In this chapter, you build on that knowledge and learn how to use more advanced SQL features.
You will learn about the SQL relational set operators (UNION, INTERSECT, and MINUS) and learn how they are used to merge the results of multiple queries. Joins are at the heart of SQL, so you must learn how to use the SQL JOIN statement to extract information from multiple tables. You will also learn about the different styles of subqueries that you can implement in a SELECT statement and about more of SQL’s many functions to extract information from data, including manipulation of dates and strings and computations based on stored or even derived data.
Finally, you will learn how to use triggers and stored procedures to perform actions when a specific event occurs. You will also see how SQL facilitates the application of business procedures when it is embedded in a programming language such as Visual Basic .NET, C#, or COBOL.
Data Files and Available Formats
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CH08_AviaCo CH08_Fact
P P P P P P P P P P P P P PPP P PPP
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8-1 SQL Join Operators
The relational join operation merges rows from two tables and returns the rows with one
of the following conditions:
• Have common values in common columns (natural join).
• Meet a given join condition (equality or inequality).
• Have common values in common columns or have no matching values (outer join).
In Chapter 7, you learned how to use the SELECT statement in conjunction with the WHERE clause to join two or more tables. For example, you can join the PRODUCT and VENDOR tables through their common V_CODE by writing the following:
SELECT FROM WHERE
P_CODE, P_DESCRIPT, P_PRICE, V_NAME PRODUCT, VENDOR
PRODUCT.V_CODE = VENDOR.V_CODE;
The preceding SQL join syntax is sometimes referred to as an “old-style” join. Note that the FROM clause contains the tables being joined and that the WHERE clause contains the condition(s) used to join the tables.
Note the following points about the preceding query:
• The FROM clause indicates which tables are to be joined. If three or more tables are included, the join operation takes place two tables at a time, from left to right. For example, if you are joining tables T1, T2, and T3, the first join is table T1 with T2; the results of that join are then joined to table T3.
• The join condition in the WHERE clause tells the SELECT statement which rows will be returned. In this case, the SELECT statement returns all rows for which the V_CODE values in the PRODUCT and VENDOR tables are equal.
• The number of join conditions is always equal to the number of tables being joined minus one. For example, if you join three tables (T1, T2, and T3), you will have two join conditions (j1 and j2). All join conditions are connected through an AND logical operator. The first join condition (j1) defines the join criteria for T1 and T2. The second join condition (j2) defines the join criteria for the output of the first join and T3.
• Generally, the join condition will be an equality comparison of the primary key in one table and the related foreign key in the second table.
Join operations can be classified as inner joins and outer joins. The inner join is the traditional join in which only rows that meet a given criterion are selected. The join criterion can be an equality condition (also called a natural join or an equijoin) or an inequality condition (also called a theta join). An outer join returns not only the matching rows but the rows with unmatched attribute values for one table or both tables to be joined. The SQL standard also introduces a special type of join, called a cross join, that returns the same result as the Cartesian product of two sets or tables.
In this section, you will learn various ways to express join operations that meet the ANSI SQL standard, as outlined in Table 8.1. Remember that not all DBMS vendors provide the same level of SQL support and that some do not support the join styles shown in this section. Oracle 12c is used to demonstrate the following queries; refer to your DBMS manual if you are using a different DBMS.
inner join
A join operation in which only rows that meet a given criterion are selected. The join criterion can be an equality condition (natural join or equijoin) or an inequality condition (theta join). The inner join is the most commonly used type of join. Contrast with outer join.
outer join
A join operation that produces a table in which all unmatched pairs are retained; unmatched values in the related table are left null. Contrast with inner join.
Chapter 8
Advanced SQL 341
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342 Part 3 Advanced Design and Implementation
TABLE 8.1
SQL JOIN EXPRESSION STYLES
JOIN JOIN SQL SYNTAX EXAMPLE DESCRIPTION CLASSIFICATION TYPE
CROSS
CROSS JOIN
SELECT * FROM T1, T2
Returns the Cartesian product of T1 and T2 (old style)
SELECT *
FROM T1 CROSS JOIN T2
Returns the Cartesian product of T1 and T2
INNER
Old-style JOIN
SELECT *
FROM T1, T2
WHERE T1.C1=T2.C1
Returns only the rows that meet the join condition in the WHERE clause (old style); only rows with matching values are selected
NATURAL JOIN
SELECT *
FROM T1 NATURAL JOIN T2
Returns only the rows with matching values in the matching columns; the matching columns must have the same names and similar data types
JOIN USING
SELECT *
FROM T1 JOIN T2 USING (C1)
Returns only the rows with matching values in the columns indicated in the USING clause
JOIN ON
SELECT *
FROM T1 JOIN T2 ON T1.C1=T2.C1
Returns only the rows that meet the join condition indicated in the ON clause
OUTER
LEFT JOIN
SELECT *
FROM T1 LEFT OUTER JOIN T2 ON T1.C1=T2.C1
Returns rows with matching values and includes all rows from the left table (T1) with unmatched values
RIGHT JOIN
SELECT *
FROM T1 RIGHT OUTER JOIN T2 ON T1.C1=T2.C1
Returns rows with matching values and includes all rows from the right table (T2) with unmatched values
FULL JOIN
SELECT *
FROM T1 FULL OUTER JOIN T2 ON T1.C1=T2.C1
Returns rows with matching values and includes all rows from both tables (T1 and T2) with unmatched values
8-1a CrossJoin
A cross join performs a relational product (also known as the Cartesian product) of two
tables. The cross join syntax is:
SELECT column-list FROM table1 CROSS JOIN table2 For example, the following command:
SELECT * FROM INVOICE CROSS JOIN LINE;
performs a cross join of the INVOICE and LINE tables that generates 144 rows. (There are 8 invoice rows and 18 line rows, yielding 8 × 18 = 144 rows.)
You can also perform a cross join that yields only specified attributes. For example, you can specify:
SELECT INVOICE.INV_NUMBER, CUS_CODE, INV_DATE, P_CODE FROM INVOICE CROSS JOIN LINE;
The results generated through that SQL statement can also be generated by using the following syntax:
SELECT INVOICE.INV_NUMBER, CUS_CODE, INV_DATE, P_CODE FROM INVOICE, LINE;
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
cross join
A join that performs a relational product (or Cartesian product) of two tables.
Copyright 2017 Cengage Lea Editorial review has deemed that

Note
Chapter 8
Advanced SQL 343
Unlike Oracle, MS SQL Server, and MySQL, Access does not support the CROSS JOIN command. However, all DBMSs support producing a cross join by placing a comma between the tables in the FROM clause.
8-1b Natural Join
Recall from Chapter 3 that a natural join returns all rows with matching values in the matching columns and eliminates duplicate columns. This style of query is used when the tables share one or more common attributes with common names. The natural join syntax is:
SELECT column-list FROM table1 NATURAL JOIN table2 The natural join will perform the following tasks:
• Determine the common attribute(s) by looking for attributes with identical names and compatible data types.
• Select only the rows with common values in the common attribute(s).
• If there are no common attributes, return the relational product of the two tables.
The following example performs a natural join of the CUSTOMER and INVOICE tables and returns only selected attributes:
SELECT CUS_CODE, CUS_LNAME, INV_NUMBER, INV_DATE FROM CUSTOMER NATURAL JOIN INVOICE;
The SQL code and its results are shown at the top of Figure 8.1.
You are not limited to two tables when performing a natural join. For example, you
can perform a natural join of the INVOICE, LINE, and PRODUCT tables and project only selected attributes by writing the following:
SELECT INV_NUMBER, P_CODE, P_DESCRIPT, LINE_UNITS, LINE_PRICE FROM INVOICE NATURAL JOIN LINE NATURAL JOIN PRODUCT;
The SQL code and its results are shown at the bottom of Figure 8.1.
One important difference between the natural join and the old-style join syntax is that the natural join does not require the use of a table qualifier for the common attributes. In the first natural join example, you projected CUS_CODE. However, the projection did not require any table qualifier, even though the CUS_CODE attribute appears in both the CUSTOMER and INVOICE tables. The same can be said of the INV_NUMBER attribute
in the second natural join example.
Note
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Although natural joins are common in theoretical discussions of databases and DBMS functionality, they are typically discouraged in most development environments. Natural joins do not document the join condition in the code, so they are harder to maintain, and many developers do not like the DBMS “guessing” about how the tables should be joined. Oracle and MySQL support NATURAL JOIN, but MS SQL Server and Access do not.

344 Part 3
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FIGURE8.1 NATURALJOINRESULTS
8-1c JOINUSINGClause
A second way to express a join is through the USING keyword. The query returns only the rows with matching values in the column indicated in the USING clause—and that column must exist in both tables. The syntax is:
SELECT column-list FROM table1 JOIN table2 USING (common-column)
To see the JOIN USING query in action, perform a join of the INVOICE and LINE
tables by writing the following:
SELECT INV_NUMBER, P_CODE, P_DESCRIPT, LINE_UNITS, LINE_PRICE FROM INVOICE JOIN LINE USING (INV_NUMBER) JOIN PRODUCT
USING (P_CODE);
The SQL statement produces the results shown in Figure 8.2.
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As with the NATURAL JOIN command, the JOIN USING operand does not require table qualifiers and will only return one copy of the common attribute.
Note
8-1d JOINONClause
The previous two join styles use common attribute names in the joining tables. Another way to express a join when the tables have no common attribute names is to use the JOIN ON operand. The query will return only the rows that meet the indicated join condition. The join condition will typically include an equality comparison expression of two columns. (The columns may or may not share the same name, but obviously they must have comparable data types.) The syntax is:
SELECT column-list FROM table1 JOIN table2 ON join-condition
The following example performs a join of the INVOICE and LINE tables using the
ON clause. The result is shown in Figure 8.3.
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FIGURE8.2 JOINUSINGRESULTS
Oracle and MySQL support the JOIN USING syntax. MS SQL Server and Access do not. If JOIN USING is used in Oracle, then table qualifiers cannot be used with the common attribute anywhere within the query. MySQL will allow table qualifiers on the common attribute anywhere except in the USING clause itself.

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FIGURE8.3 JOINONRESULTS
Note
SELECT INVOICE.INV_NUMBER, PRODUCT.P_CODE, P_DESCRIPT, LINE_UNITS, LINE_PRICE
FROM INVOICE JOIN LINE ON INVOICE.INV_NUMBER = LINE. INV_NUMBER
JOIN PRODUCT ON LINE.P_CODE = PRODUCT.P_CODE;
Unlike the NATURAL JOIN and JOIN USING operands, the JOIN ON clause requires a table qualifier for the common attributes. If you do not specify the table qualifier, you will get a “column ambiguously defined” error message.
Keep in mind that the JOIN ON syntax lets you perform a join even when the tables do not share a common attribute name. For example, to generate a list of all employees with the managers’ names, you can use the following (recursive) query:
SELECT FROM ORDER BY
E.EMP_MGR, M.EMP_LNAME, E.EMP_NUM, E.EMP_LNAME EMP E JOIN EMP M ON E.EMP_MGR = M.EMP_NUM E.EMP_MGR;
Oracle, MS SQL Server, MySQL, and Access all support the JOIN ON syntax. In many environments, including the SQL code generated by Access when queries are created using the QBE window, it is common to include the optional word INNER to the join syntax. For example,
SELECT P.P_CODE, P.P_DESCRIPT, V.V_CODE, V.V_NAME
FROM PRODUCT P INNER JOIN VENDOR V ON P.V_CODE = V.V_CODE;
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8-1e Outer Joins
An outer join returns not only the rows matching the join condition (that is, rows with matching values in the common columns), it returns the rows with unmatched values. The ANSI standard defines three types of outer joins: left, right, and full. The left and right designations reflect the order in which the tables are processed by the DBMS. Remember that join operations take place two tables at a time. The first table named in the FROM clause will be the left side, and the second table named will be the right side. If three or more tables are being joined, the result of joining the first two tables becomes the left side, and the third table becomes the right side.
The left outer join returns not only the rows matching the join condition (that is, rows with matching values in the common column), it returns the rows in the left table with unmatched values in the right table. The syntax is:
SELECT column-list
FROM table1 LEFT [OUTER] JOIN table2 ON join-condition
For example, the following query lists the product code, vendor code, and vendor name for all products and includes those vendors with no matching products:
SELECT P_CODE, VENDOR.V_CODE, V_NAME
FROM VENDOR LEFT JOIN PRODUCT ON VENDOR.
V_CODE = PRODUCT.V_CODE;
The preceding SQL code and its results are shown in Figure 8.4.
The right outer join returns not only the rows matching the join condition (that is, rows with matching values in the common column), it returns the rows in the right table with unmatched values in the left table. The syntax is:
SELECT column-list
FROM table1 RIGHT [OUTER] JOIN table2 ON join-condition
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FIGURE8.4 LEFTJOINRESULTS

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For example, the following query lists the product code, vendor code, and vendor name for all products and includes products that do not have a matching vendor code:
SELECT P_CODE, VENDOR.V_CODE, V_NAME
FROM VENDOR RIGHT JOIN PRODUCT ON VENDOR.
V_CODE = PRODUCT.V_CODE; The SQL code and its output are shown in Figure 8.5.
The full outer join returns not only the rows matching the join condition (that is, rows with matching values in the common column), it returns all of the rows with unmatched values in the table on either side. The syntax is:
SELECT column-list
FROM table1 FULL [OUTER] JOIN table2 ON join-condition
For example, the following query lists the product code, vendor code, and vendor name for all products and includes all product rows (products without matching vendors) as well as all vendor rows (vendors without matching products):
SELECT P_CODE, VENDOR.V_CODE, V_NAME
FROM VENDOR FULL JOIN PRODUCT ON VENDOR.
V_CODE = PRODUCT.V_CODE; The SQL code and its results are shown in Figure 8.6.
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FIGURE8.5 RIGHTJOINRESULTS

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FIGURE8.6 FULLJOINRESULTS
Note
8-2 Subqueries and Correlated Queries
The use of joins in a relational database allows you to get information from two or more tables. For example, the following query allows you to get customer data with its respective invoices by joining the CUSTOMER and INVOICE tables.
Oracle and MS SQL Server support the FULL JOIN syntax. MySQL and Access do not.
SELECT
FROM WHERE
INV_NUMBER, INVOICE.CUS_CODE, CUS_LNAME, CUS_FNAME
CUSTOMER, INVOICE
CUSTOMER.CUS_CODE = INVOICE.CUS_CODE;
In the previous query, the data from both tables (CUSTOMER and INVOICE) is processed at once, matching rows with shared CUS_CODE values.
However, it is often necessary to process data based on other processed data. For example, suppose that you want to generate a list of vendors who do not provide prod- ucts. (Recall that not all vendors in the VENDOR table have provided products—some are only potential vendors.) In Chapter 7, you learned that you could generate such a list by writing the following query:
SELECT V_CODE, V_NAME FROM VENDOR
WHERE V_CODE NOT IN (SELECT V_CODE FROM PRODUCT);
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TABLE 8.2
Similarly, to generate a list of all products with a price greater than or equal to the average product price, you can write the following query:
SELECT P_CODE, P_PRICE FROM PRODUCT
WHERE P_PRICE >= (SELECT AVG(P_PRICE) FROM PRODUCT);
In both queries, you needed to get information that was not previously known:
• What vendors provide products?
• What is the average price of all products?
In both cases, you used a subquery to generate the required information, which could then be used as input for the originating query. You learned how to use subqueries in Chapter 7; review their basic characteristics:
• A subquery is a query (SELECT statement) inside another query.
• A subquery is normally expressed inside parentheses.
• The first query in the SQL statement is known as the outer query.
• The query inside the SQL statement is known as the inner query.
• The inner query is executed first.
• The output of an inner query is used as the input for the outer query.
• The entire SQL statement is sometimes referred to as a nested query.
In this section, you learn more about the practical use of subqueries. You already know that a subquery is based on the use of the SELECT statement to return one or more values to another query, but subqueries have a wide range of uses. For example, you can use a subquery within a SQL data manipulation language (DML) statement such as INSERT, UPDATE, or DELETE, in which a value or list of values (such as multiple vendor codes or a table) is expected. Table 8.2 uses simple examples to summarize the use of SELECT subqueries in DML statements.
SELECT SUBQUERY EXAMPLES
SELECT SUBQUERY EXAMPLES EXPLANATION
INSERT INTO PRODUCT SELECT * FROM P;
Inserts all rows from Table P into the PRODUCT table. Both tables must have the same attributes. The subquery returns all rows from Table P.
UPDATE PRODUCT
SET P_PRICE = (SELECT AVG(P_PRICE)
FROM PRODUCT) WHERE V_CODE IN (SELECT V_CODE
FROM VENDOR
WHERE V_AREACODE = ‘615’)
Updates the product price to the average product price, but only for products provided by vendors who have an area code equal to 615. The first subquery returns the average price; the second subquery returns the list of vendors with an area code equal to 615.
DELETE FROM PRODUCT
WHERE V_CODE IN (SELECT V_CODE
FROM VENDOR
WHERE V_AREACODE = ‘615’)
Deletes the PRODUCT table rows provided by vendors with an area code equal to 615. The subquery returns the list of vendor codes with an area code equal to 615.
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Using the examples in Table 8.2, note that the subquery is always on the right side of a comparison or assigning expression. Also, a subquery can return one or more values. To be precise, the subquery can return the following:
• One single value (one column and one row). This subquery is used anywhere a single value is expected, as in the right side of a comparison expression. An example is the preceding UPDATE subquery, in which you assigned the average price to the product’s price. Obviously, when you assign a value to an attribute, you are assign- ing a single value, not a list of them. Therefore, the subquery must return only one value (one column, one row). If the query returns multiple values, the DBMS will generate an error.
• A list of values (one column and multiple rows). This type of subquery is used any- where a list of values is expected, such as when using the IN clause—for example, when comparing the vendor code to a list of vendors. Again, in this case, there is only one column of data with multiple value instances. This type of subquery is used frequently in combination with the IN operator in a WHERE conditional expression.
• A virtual table (multicolumn, multirow set of values). This type of subquery can be used anywhere a table is expected, such as when using the FROM clause. You will see an example later in this chapter.
It is important to note that a subquery can return no values at all; it is a NULL. In such cases, the output of the outer query might result in an error or a null empty set, depending on where the subquery is used (in a comparison, an expression, or a table set).
In the following sections, you will learn how to write subqueries within the SELECT statement to retrieve data from the database.
8-2a WHERE Subqueries
The most common type of subquery uses an inner SELECT subquery on the right side of a WHERE comparison expression. For example, to find all products with a price greater than or equal to the average product price, you write the following query:
SELECT P_CODE, P_PRICE FROM PRODUCT
WHERE P_PRICE >= (SELECT AVG(P_PRICE) FROM PRODUCT);
The output of the preceding query is shown in Figure 8.7. Note that this type of query, when used in a >, <, =, >=, or <= conditional expression, requires a subquery that returns only one value (one column, one row). The value generated by the subquery must be of a comparable data type; if the attribute to the left of the comparison symbol is a character type, the subquery must return a character string. Also, if the query returns more than a single value, the DBMS will generate an error. Subqueries can also be used in combination with joins. For example, the following query lists all customers who ordered a claw hammer: SELECT FROM DISTINCT CUS_CODE, CUS_LNAME, CUS_FNAME CUSTOMER JOIN INVOICE USING (CUS_CODE) JOIN LINE USING (INV_NUMBER) JOIN PRODUCT USING (P_CODE) P_CODE = (SELECT P_CODE FROM PRODUCT WHERE WHERE The result of the query is shown in Figure 8.7. P_DESCRIPT = 'Claw hammer'); Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 8 Advanced SQL 351 352 Part 3 Advanced Design and Implementation FIGURE 8.7 WHERE SUBQUERY EXAMPLES In the preceding example, the inner query finds the P_CODE for the claw hammer. The P_CODE is then used to restrict the selected rows to those in which the P_CODE in the LINE table matches the P_CODE for “Claw hammer.” Note that the previous query could have been written this way: SELECT FROM WHERE DISTINCT CUS_CODE, CUS_LNAME, CUS_FNAME CUSTOMER JOIN INVOICE USING (CUS_CODE) JOIN LINE USING (INV_NUMBER) JOIN PRODUCT USING (P_CODE) P_DESCRIPT = 'Claw hammer'; If the original query encounters the “Claw hammer” string in more than one product description, you get an error message. To compare one value to a list of values, you must use an IN operand, as shown in the next section. 8-2b INSubqueries What if you wanted to find all customers who purchased a hammer or any kind of saw or saw blade? Note that the product table has two different types of hammers: a claw hammer and a sledge hammer. Also, there are multiple occurrences of products that contain “saw” in their product descriptions, including saw blades and jigsaws. In such cases, you need to compare the P_CODE not to one product code (a single value), but to a list of product code values. When you want to compare a single attribute to a list of values, you use the IN operator. When the P_CODE values are not known beforehand, but they can be derived using a query, you must use an IN subquery. The following example lists all customers who have purchased hammers, saws, or saw blades. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. SELECT FROM DISTINCT CUS_CODE, CUS_LNAME, CUS_FNAME JOIN INVOICE USING (CUS_CODE) JOIN LINE USING (INV_NUMBER) JOIN PRODUCT USING (P_CODE) (SELECT P_CODE FROM PRODUCT WHERE P_DESCRIPT LIKE '%hammer%' OR P_DESCRIPT LIKE '%saw%'); CUSTOMER P_CODE IN WHERE The result of the query is shown in Figure 8.8. Chapter 8 Advanced SQL 353 FIGURE8.8 INSUBQUERYEXAMPLE 8-2c HAVINGSubqueries Just as you can use subqueries with the WHERE clause, you can use a subquery with a HAVING clause. The HAVING clause is used to restrict the output of a GROUP BY query by applying conditional criteria to the grouped rows. For example, to list all products with a total quantity sold greater than the average quantity sold, you would write the following query: SELECT FROM GROUP BY HAVING P_CODE, SUM(LINE_UNITS) LINE P_CODE SUM(LINE_UNITS) > (SELECT AVG(LINE_UNITS) FROM LINE);
The result of the query is shown in Figure 8.9.
8-2d Multirow Subquery Operators: ANY and ALL
So far, you have learned that you must use an IN subquery to compare a value to a list of values. However, the IN subquery uses an equality operator; that is, it selects only those rows that are equal to at least one of the values in the list. What happens if you need to make an inequality comparison ( > or < ) of one value to a list of values? Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 354 Part 3 Advanced Design and Implementation FIGURE8.9 HAVINGSUBQUERYEXAMPLE For example, suppose you want to know which products cost more than all individual products provided by vendors from Florida: SELECT FROM WHERE P_CODE, P_QOH * P_PRICE PRODUCT P_QOH * P_PRICE > ALL (SELECT P_QOH * P_PRICE
FROM PRODUCT
WHERE V_CODE IN (SELECT V_CODE FROM VENDOR
WHERE V_STATE = ‘FL’));
The result of the query is shown in Figure 8.10.
It is important to note the following points about the query and its output in Figure 8.10:
• The query is a typical example of a nested query.
• The query has one outer SELECT statement with a SELECT subquery (call it sqA) that
contains a second SELECT subquery (call it sqB).
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FIGURE8.10 MULTIROWSUBQUERYOPERATOREXAMPLE

• The last SELECT subquery (sqB) is executed first and returns a list of all vendors from Florida.
• The first SELECT subquery (sqA) uses the output of the second SELECT subquery (sqB). The sqA subquery returns the list of costs for all products provided by vendors from Florida.
• The use of the ALL operator allows you to compare a single value (P_QOH * P_PRICE) with a list of values returned by the first subquery (sqA) using a compari- son operator other than equals.
• For a row to appear in the result set, it has to meet the criterion P_QOH * P_PRICE > ALL of the individual values returned by the subquery sqA. The values returned by sqA are a list of product costs. In fact, “greater than ALL” is equivalent to “greater than the highest product cost of the list.” In the same way, a condition of “less than ALL” is equivalent to “less than the lowest product cost of the list.”
Another powerful operator is the ANY multirow operator, which you can consider the cousin of the ALL multirow operator. The ANY operator allows you to compare a single value to a list of values and select only the rows for which the inventory cost is greater than or less than any value in the list. You could use the equal to ANY operator, which would be the equivalent of the IN operator.
8-2e FROMSubqueries
So far you have seen how the SELECT statement uses subqueries within WHERE, HAV- ING, and IN statements, and how the ANY and ALL operators are used for multirow subqueries. In all of those cases, the subquery was part of a conditional expression, and it always appeared at the right side of the expression. In this section, you will learn how to use subqueries in the FROM clause.
As you already know, the FROM clause specifies the table(s) from which the data will be drawn. Because the output of a SELECT statement is another table (or more precisely, a “virtual” table), you could use a SELECT subquery in the FROM clause. For example, assume that you want to know all customers who have purchased products 13-Q2/P2 and 23109-HB. All product purchases are stored in the LINE table, so you can easily find out who purchased any given product by searching the P_CODE attribute in the LINE table. In this case, however, you want to know all customers who purchased both products, not just one. You could write the following query:
SELECT FROM
WHERE
DISTINCT CUSTOMER.CUS_CODE, CUSTOMER.CUS_LNAME CUSTOMER,
(SELECT INVOICE.CUS_CODE FROM INVOICE NATURAL JOIN LINE WHERE P_CODE = ’13-Q2/P2′) CP1,
(SELECT INVOICE.CUS_CODE FROM INVOICE NATURAL JOIN LINE WHERE P_CODE = ‘23109-HB’) CP2
CUSTOMER.CUS_CODE = CP1.CUS_CODE AND
CP1.CUS_CODE = CP2.CUS_CODE;
The result of the query is shown in Figure 8.11.
Note in Figure 8.11 that the first subquery returns all customers who purchased
product 13-Q2/P2, while the second subquery returns all customers who purchased
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FIGURE8.11 FROMSUBQUERYEXAMPLE
product 23109-HB. So, in this FROM subquery, you are joining the CUSTOMER table with two virtual tables. The join condition selects only the rows with matching CUS_CODE values in each table (base or virtual).
8-2f Attribute List Subqueries
The SELECT statement uses the attribute list to indicate what columns to project in the resulting set. Those columns can be attributes of base tables, computed attributes, or the result of an aggregate function. The attribute list can also include a subquery expression, also known as an inline subquery. A subquery in the attribute list must return one value; otherwise, an error code is raised. For example, a simple inline query can be used to list the difference between each product’s price and the average product price:
SELECT P_CODE, P_PRICE, (SELECT AVG(P_PRICE) FROM PRODUCT) AS AVGPRICE,
P_PRICE – (SELECT AVG(P_PRICE) FROM PRODUCT) AS DIFF FROM PRODUCT;
Figure 8.12 shows the result of the query.
In Figure 8.12, note that the inline query output returns one value (the average prod-
uct’s price) and that the value is the same in every row. Note also that the query uses the full expression instead of the column aliases when computing the difference. In fact, if you try to use the alias in the difference expression, you will get an error message. The column alias cannot be used in computations in the attribute list when the alias is defined in the same attribute list. That DBMS requirement is the result of the way the DBMS parses and executes queries.
Another example will help you understand the use of attribute list subqueries and column aliases. For example, suppose that you want to know the product code, the total sales by product, and the contribution by employee of each product’s sales. To get the sales by product, you need to use only the LINE table. To compute the contribution by employee, you need to know the number of employees (from the EMPLOYEE table). As you study the tables’ structures, you can see that the LINE and EMPLOYEE tables do not share a common attribute. In fact, you do not need a common attribute. You only need to know the total number of employees, not the
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total employees related to each product. So, to answer the query, you would write the following code:
SELECT
FROM GROUP BY
P_CODE, SUM(LINE_UNITS * LINE_PRICE) AS SALES, (SELECT COUNT(*) FROM EMPLOYEE) AS ECOUNT, SUM(LINE_UNITS * LINE_PRICE)/(SELECT COUNT(*) FROM EMPLOYEE) AS CONTRIB
LINE P_CODE;
The result of the query is shown in Figure 8.13.
As you can see in Figure 8.13, the number of employees remains the same for each
row in the result set. The use of this type of subquery is limited to certain instances when you need to include data from other tables that is not directly related to a main table or tables in the query. The value will remain the same for each row, like a constant in a programming language. (You will learn another use of inline subqueries in Section 8-2g, Correlated Subqueries.) Note that you cannot use an alias in the attribute list to write the expression that computes the contribution per employee.
Another way to write the same query by using column aliases requires the use of a subquery in the FROM clause, as follows:
SELECT P_CODE, SALES, ECOUNT, SALES/ECOUNT AS CONTRIB FROM (SELECT P_CODE, SUM(LINE_UNITS * LINE_PRICE) AS SALES,
(SELECT COUNT(*) FROM EMPLOYEE) AS ECOUNT FROM LINE
GROUP BY P_CODE);
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FIGURE8.13 ANOTHEREXAMPLEOFANINLINESUBQUERY
In this case, you are actually using two subqueries. The subquery in the FROM clause executes first and returns a virtual table with three columns: P_CODE, SALES, and ECOUNT. The FROM subquery contains an inline subquery that returns the number of employees as ECOUNT. Because the outer query receives the output of the inner query, you can now refer to the columns in the outer subquery by using the column aliases.
8-2g Correlated Subqueries
Until now, all subqueries you have learned execute independently. That is, each subquery in a command sequence executes in a serial fashion, one after another. The inner sub- query executes first; its output is used by the outer query, which then executes until the last outer query finishes (the first SQL statement in the code).
In contrast, a correlated subquery is a subquery that executes once for each row in the outer query. The process is similar to the typical nested loop in a programming language. For example:
FOR X = 1 TO 2
FOR Y = 1 TO 3
PRINT “X = ”X, “Y = ”Y END
END
will yield the following output:
X= 1 X= 1 X= 1 X= 2 X= 2 X= 2
Y= 1 Y= 2 Y= 3 Y= 1 Y= 2 Y= 3
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
correlated subquery
A subquery that executes once for each row in the outer query.
Copyright 2017 Cengage Le Editorial review has deemed that

Note that the outer loop X = 1 TO 2 begins the process by setting X = 1, and then the inner loop Y = 1 TO 3 is completed for each X outer loop value. The relational DBMS uses the same sequence to produce correlated subquery results:
1. It initiates the outer query.
2. For each row of the outer query result set, it executes the inner query by passing the outer row to the inner query.
This process is the opposite of that of the subqueries, as you have already seen. The query is called a correlated subquery because the inner query is related to the outer query; the inner query references a column of the outer subquery.
To see the correlated subquery in action, suppose that you want to know all product sales in which the units sold value is greater than the average units sold value for that product (as opposed to the average for all products). In that case, the following procedure must be completed:
1. Compute the average units sold for a product.
2. Compare the average computed in Step 1 to the units sold in each sale row, and then select only the rows in which the number of units sold is greater.
The following correlated query completes the preceding two-step process:
SELECT FROM WHERE
INV_NUMBER, P_CODE, LINE_UNITS
LINE LS
LS.LINE_UNITS > (SELECT AVG(LINE_UNITS)
FROM LINE LA
WHERE LA.P_CODE = LS.P_CODE);
The first example in Figure 8.14 shows the result of the query.
In the top query and its result in Figure 8.14, note that the LINE table is used more than once, so you must use table aliases. In this case, the inner query computes the average units sold of the product that matches the P_CODE of the outer query P_CODE. That is, the
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inner query runs once, using the first product code found in the outer LINE table, and it returns the average sale for that product. When the number of units sold in the outer LINE row is greater than the average computed, the row is added to the output. Then the inner query runs again, this time using the second product code found in the outer LINE table. The process repeats until the inner query has run for all rows in the outer LINE table. In this case, the inner query will be repeated as many times as there are rows in the outer query.
To verify the results and to provide an example of how you can combine subqueries, you can add a correlated inline subquery to the previous query. (See the second query and its results in Figure 8.14.) As you can see, the new query contains a correlated inline subquery that computes the average units sold for each product. You not only get an answer, you can also verify that the answer is correct.
Correlated subqueries can also be used with the EXISTS special operator. For example, suppose that you want to know the names of all customers who have placed an order lately. In that case, you could use a correlated subquery like the first one shown in Figure 8.15.
FIGURE8.15 EXISTSCORRELATEDSUBQUERYEXAMPLES
SELECT FROM WHERE
CUS_CODE, CUS_LNAME, CUS_FNAME
CUSTOMER
EXISTS (SELECT CUS_CODE FROM INVOICE
WHERE INVOICE.CUS_CODE = CUSTOMER.CUS_CODE);
The second example in Figure 8.15 will help you understand how to use correlated queries. For example, suppose that you want to know what vendors you must contact to order prod- ucts that are approaching the minimum quantity-on-hand value. In particular, you want to know the vendor code and vendor name for products with a quantity on hand that is less than double the minimum quantity. The query that answers the question is as follows:
SELECT FROM WHERE
V_CODE, V_NAME VENDOR
EXISTS (SELECT *
FROM PRODUCT
WHERE P_QOH < P_MIN * 2 AND VENDOR.V_CODE = PRODUCT.V_CODE); Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. In the second query in Figure 8.15, note that: 1. The inner correlated subquery runs using the first vendor. 2. If any products match the condition (the quantity on hand is less than double the minimum quantity), the vendor code and name are listed in the output. 3. The correlated subquery runs using the second vendor, and the process repeats itself until all vendors are used. 8-3 SQL Functions The data in databases is the basis of critical business information. Generating informa- tion from data often requires many data manipulations. Sometimes such data manip- ulation involves the decomposition of data elements. For example, an employee’s date of birth can be subdivided into a day, a month, and a year. A product manufacturing code (for example, SE-05-2-09-1234-1-3/12/16-19:26:48) can be designed to record the manufacturing region, plant, shift, production line, employee number, date, and time. For years, conventional programming languages have had special functions that enabled programmers to perform data transformations like the preceding data decompositions. If you know a modern programming language, it is very likely that the SQL functions in this section will look familiar. SQL functions are very useful tools. You’ll need to use functions when you want to list all employees ordered by year of birth, or when your marketing department wants you to generate a list of all customers ordered by zip code and the first three digits of their telephone numbers. In both of these cases, you’ll need to use data elements that are not present as such in the database. Instead, you will need a SQL function that can be derived from an existing attribute. Functions always use a numerical, date, or string value. The value may be part of the command itself (a constant or literal) or it may be an attribute located in a table. Therefore, a function may appear anywhere in a SQL statement where a value or an attribute can be used. There are many types of SQL functions, such as arithmetic, trigonometric, string, date, and time functions. This section will not explain all of these functions in detail, but it will give you a brief overview of the most useful ones. Note 8-3a Date and Time Functions All SQL-standard DBMSs support date and time functions. All date functions take one parameter of a date or character data type and return a value (character, numeric, or date type). Unfortunately, date/time data types are implemented differently by different DBMS vendors. The problem occurs because the ANSI SQL standard defines date data Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 8 Advanced SQL 361 Although the main DBMS vendors support the SQL functions covered here, the syntax or degree of support will probably differ. In fact, DBMS vendors invariably add their own func- tions to products to lure new customers. The functions covered in this section represent just a small portion of functions supported by your DBMS. Read your DBMS SQL reference manual for a complete list of available functions. 362 Part 3 Advanced Design and Implementation TABLE 8.3 types, but it does not specify how those data types are to be stored. Instead, it lets the vendor deal with that issue. Because date/time functions differ from vendor to vendor, this section will cover basic date/time functions for MS Access, SQL Server, and Oracle. Table 8.3 shows a list of selected MS Access and SQL Server date/time functions. SELECTED MS ACCESS AND SQL SERVER DATA/TIME FUNCTIONS FUNCTION EXAMPLE(S) CONVERT (MS SQL Server) Convert can be used to perform a wide array of data type conversions as discussed next. It can also be used to format date data. Syntax: CONVERT(varchar(length), date_value, fmt_code) fmt_code = format used; can be: 1: MM/DD/YY 101: MM/DD/YYYY 2: YY.MM.DD 102: YYYY.MM.DD 3: DD/MM/YY 103: DD/MM/YYYY Displays the product code and date the product was last received into stock for all products: SELECT P_CODE, CONVERT(VARCHAR(8), P_INDATE, 1) FROM PRODUCT; SELECT P_CODE, CONVERT(VARCHAR(10), P_INDATE, 102) FROM PRODUCT; YEAR Returns a four-digit year Syntax: YEAR(date_value) Lists all employees born in 1982: SELECT FROM WHERE EMP_LNAME, EMP_FNAME, EMP_DOB, YEAR(EMP_DOB) AS YEAR EMPLOYEE YEAR(EMP_DOB) = 1982; MONTH Returns a two-digit month code Syntax: MONTH(date_value) Lists all employees born in November: SELECT FROM WHERE EMP_LNAME, EMP_FNAME, EMP_DOB, MONTH(EMP_DOB) AS MONTH EMPLOYEE MONTH(EMP_DOB) = 11; DAY Returns the number of the day Syntax: DAY(date_value) Lists all employees born on the 14th day of the month: SELECT FROM WHERE EMP_LNAME, EMP_FNAME, EMP_DOB, DAY(EMP_DOB) AS DAY EMPLOYEE DAY(EMP_DOB) = 14; DATE() MS Access GETDATE() SQL Server Returns today’s date Lists how many days are left until Christmas: SELECT #25-Dec-2016# – DATE(); Note two features: • There is no FROM clause, which is acceptable in Access and MS SQL Server. • The Christmas date is enclosed in number signs ( # ) because you are doing date arithmetic. In MS SQL Server: Use GETDATE() to get the current system date. To compute the difference between dates, use the DATEDIFF function (see below). Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. TABLE8.3 (CONTINUED) SELECTED MS ACCESS AND SQL SERVER DATA/TIME FUNCTIONS Chapter 8 Advanced SQL 363 DATEADD SQL Server Adds a number of selected time peri- ods to a date Syntax: DATEADD(datepart, number, date) Adds a number of dateparts to a given date. Dateparts can be minutes, hours, days, weeks, months, quarters, or years. For example: SELECT DATEADD(day,90, P_INDATE) AS DueDate FROM PRODUCT; The preceding example adds 90 days to P_INDATE. In MS Access, use the following: SELECT P_INDATE+90 AS DueDate FROM PRODUCT; DATEDIFF SQL Server Subtracts two dates Syntax: DATEDIFF(datepart, startdate, enddate) Returns the difference between two dates expressed in a selected datepart. For example: SELECT DATEDIFF(day, P_INDATE, GETDATE()) AS DaysAgo FROM PRODUCT; In MS Access, use the following: SELECT DATE() - P_INDATE AS DaysAgo FROM PRODUCT; Table 8.4 shows the equivalent date/time functions used in Oracle. Note that Oracle uses the same function (TO_CHAR) to extract the various parts of a date. Also, another function (TO_DATE) is used to convert character strings to a valid Oracle date format that can be used in date arithmetic. TABLE 8.4 SELECTED ORACLE DATE/TIME FUNCTIONS FUNCTION EXAMPLE(S) TO_CHAR Returns a character string or a for- matted string from a date value Syntax: TO_CHAR(date_value, fmt) fmt = format used; can be: MONTH: name of month MON: three-letter month name MM: two-digit month name D: number for day of week DD: number for day of month DAY: name of day of week YYYY: four-digit year value YY: two-digit year value Lists all SELECT FROM WHERE Lists all SELECT FROM WHERE Lists all SELECT FROM WHERE employees born in 1982: EMP_LNAME, EMP_FNAME, EMP_DOB, TO_CHAR(EMP_DOB, 'YYYY') AS YEAR EMPLOYEE TO_CHAR(EMP_DOB, 'YYYY') = '1982'; employees born in November: EMP_LNAME, EMP_FNAME, EMP_DOB, TO_CHAR(EMP_DOB, 'MM') AS MONTH EMPLOYEE TO_CHAR(EMP_DOB, 'MM') = '11'; employees born on the 14th day of the month: EMP_LNAME, EMP_FNAME, EMP_DOB, TO_CHAR(EMP_DOB, 'DD') AS DAY EMPLOYEE TO_CHAR(EMP_DOB, 'DD') = '14'; Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 364 Part 3 Advanced Design and Implementation TABLE8.4 (CONTINUED) SELECTED ORACLE DATE/TIME FUNCTIONS TO_DATE Returns a date value using a character string and a date format mask; also used to translate a date between formats Syntax: TO_DATE(char_value, fmt) fmt = format used; can be: MONTH: name of month MON: three-letter month name MM: two-digit month name D: number for day of week DD: number for day of month DAY: name of day of week YYYY: four-digit year value YY: two-digit year value Lists the approximate age of employees on the company’s tenth anniversary date (11/25/2016): SELECT EMP_LNAME, EMP_FNAME, EMP_DOB, '11/25/2016' AS ANIV_DATE, (TO_DATE('11/25/2004','MM/DD/YYYY') - EMP_DOB)/365 AS YEARS FROM EMPLOYEE ORDER BY YEARS; Note the following: • '11/25/2016' is a text string, not a date. • The TO_DATE function translates the text string to a valid Oracle date used in date arithmetic. How many days are there between Thanksgiving and Christmas 2016? SELECT TO_DATE('2016/12/25','YYYY/MM/DD') – TO_DATE('NOVEMBER 27, 2016','MONTH DD, YYYY') FROM DUAL; Note the following: • The TO_DATE function translates the text string to a valid Oracle date used in date arithmetic. • DUAL is Oracle’s pseudo-table, used only for cases in which a table is not really needed. SYSDATE Returns today’s date Lists how many days are left until Christmas: SELECT TO_DATE('25-Dec-2016','DD-MON-YYYY') - SYSDATE FROM DUAL; Notice two things: • DUAL is Oracle’s pseudo-table, used only for cases in which a table is not really needed. • The Christmas date is enclosed in a TO_DATE function to translate the date to a valid date format. ADD_MONTHS Adds a number of months or years to a date Syntax: ADD_MONTHS(date_value, n) n = number of months Lists all products with their expiration date (two years from the purchase date): SELECT FROM ORDER BY P_CODE, P_INDATE, ADD_MONTHS(P_INDATE,24) PRODUCT ADD_MONTHS(P_INDATE,24); LAST_DAY Returns the date of the last day of the month given in a date Syntax: LAST_DAY(date_value) Lists all employees who were hired within the last seven days of a month: SELECT FROM WHERE EMP_LNAME, EMP_FNAME, EMP_HIRE_DATE EMPLOYEE EMP_HIRE_DATE >=LAST_DAY(EMP_HIRE_DATE)-7;
Table 8.5 shows the equivalent functions for MySQL.
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TABLE 8.5
SELECTED MYSQL DATE/TIME FUNCTIONS
FUNCTION EXAMPLE(S)
Chapter 8 Advanced SQL 365
Date_Format
Returns a character string or a formatted string from a date value
Syntax:
DATE_FORMAT(date_value, fmt)
fmt = format used; can be: %M: name of month
%m: two-digit month number %b: abbreviated month name %d: number of day of month %W: weekday name
%a: abbreviated weekday name %Y: four-digit year
%y: two-digit year
Displays the product code and date the product was last received into stock for all products:
SELECT P_CODE, DATE_FORMAT(P_INDATE, ‘%m/%d/%y’) FROM PRODUCT;
SELECT P_CODE, DATE_FORMAT(P_INDATE, ‘%M %d, %Y’) FROM PRODUCT;
YEAR
Returns a four-digit year Syntax: YEAR(date_value)
Lists all employees born in 1982:
SELECT
FROM WHERE
EMP_LNAME, EMP_FNAME, EMP_DOB, YEAR(EMP_DOB) AS YEAR
EMPLOYEE
YEAR(EMP_DOB) = 1982;
MONTH
Returns a two-digit month code Syntax:
MONTH(date_value)
Lists all employees born in November:
SELECT
FROM WHERE
EMP_LNAME, EMP_FNAME, EMP_DOB, MONTH(EMP_DOB) AS MONTH EMPLOYEE
MONTH(EMP_DOB) = 11;
DAY
Returns the number of the day Syntax:
DAY(date_value)
Lists all employees born on the 14th day of the month:
SELECT
FROM WHERE
EMP_LNAME, EMP_FNAME, EMP_DOB, DAY(EMP_DOB) AS DAY
EMPLOYEE
DAY(EMP_DOB) = 14;
ADDDATE
Adds a number of days to a date Syntax:
ADDDATE(date_value, n)
n = number of days
DATE_ADD
Adds a number of days, months, or years to a date. This is similar to ADDDATE except it is more robust. It allows the user to specify the date unit to add. Syntax:
DATE_ADD(date, INTERVAL n unit) n = number to add
unit = date unit, can be:
DAY: add n days
WEEK: add n weeks MONTH: add n months YEAR: add n years
List all products with the date they will have been on the shelf for 30
days.
SELECT
FROM
ORDER BY
Lists all products with their expiration date (two years from the purchase date):
SELECT
FROM ORDER BY
P_CODE, P_INDATE, DATE_ADD(P_INDATE, INTERVAL 2 YEAR)
PRODUCT
DATE_ADD(P_INDATE, INTERVAL 2 YEAR);
P_CODE, P_INDATE, ADDDATE(P_INDATE, 30) PRODUCT
ADDDATE(P_INDATE, 30);
LAST_DAY
Returns the date of the last day of the month given in a date
Syntax:
LAST_DAY(date_value)
Lists all employees who were hired within the last seven days of a month:
SELECT FROM WHERE
EMP_LNAME, EMP_FNAME, EMP_HIRE_DATE EMPLOYEE
EMP_HIRE_DATE >= DATE_ADD(LAST_DAY (EMP_HIRE_DATE), INTERVAL -7 DAY);
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366 Part 3
Advanced Design and Implementation
8-3b Numeric Functions
TABLE 8.6
Numeric functions can be grouped in many different ways, such as algebraic, trigono- metric, and logarithmic. In this section, you will learn two very useful functions. Do not confuse the SQL aggregate functions you saw in the previous chapter with the numeric functions in this section. The first group operates over a set of values (multiple rows— hence, the name aggregate functions), while the numeric functions covered here operate over a single row. Numeric functions take one numeric parameter and return one value. Table 8.6 shows a selected group of available numeric functions.
SELECTED NUMERIC FUNCTIONS
FUNCTION EXAMPLE(S)
ABS
Returns the absolute value of a number Syntax:
ABS(numeric_value)
In Oracle, use the following:
SELECT 1.95, −1.93, ABS(1.95), ABS(−1.93)
FROM DUAL;
In MS Access, MySQL, and MS SQL Server, use the following: SELECT 1.95, −1.93, ABS(1.95), ABS(−1.93);
ROUND
Rounds a value to a specified precision (number of digits) Syntax:
ROUND(numeric_value, p)
p = precision
Lists the product prices rounded to one and zero decimal places:
SELECT P_CODE, P_PRICE,
ROUND(P_PRICE,1) AS PRICE1,
ROUND(P_PRICE,0) AS PRICE0 FROM PRODUCT;
CEIL/CEILING/FLOOR
Returns the smallest integer greater than or equal to a number or returns the largest integer equal to or less than a number, respectively
Syntax:
CEIL(numeric_value) Oracle or MySQL CEILING(numeric_value) MS SQL Server or MySQL FLOOR(numeric_value)
Lists the product price, the smallest integer greater than or equal to the product price, and the largest integer equal to or less than the product price.
In Oracle or MySQL, use the following:
SELECT P_PRICE, CEIL(P_PRICE), FLOOR(P_PRICE) FROM PRODUCT;
In MS SQL Server or MySQL, use the following:
SELECT P_PRICE, CEILING(P_PRICE), FLOOR(P_PRICE) FROM PRODUCT;
MS Access does not support these functions. Note that MySQL supports both CEIL and CEILING.
8-3c String Functions
String manipulations are among the most-used functions in programming. If you have ever created a report using any programming language, you know the importance of properly concatenating strings of characters, printing names in uppercase, or knowing the length of a given attribute. Table 8.7 shows a subset of useful string manipulation functions.
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TABLE 8.7
SELECTED STRING FUNCTIONS
FUNCTION EXAMPLE(S)
Chapter 8 Advanced SQL 367
Concatenation
|| Oracle
+ Access and MS SQL Server
& Access
CONCAT() MySQL
Concatenates data from two different character columns and returns a single column.
Syntax:
strg_value || strg_value
strg_value + strg_value
strg_value & strg_value
CONCAT(strg_value, strg_value)
The CONCAT function can only accept two string values so nested CONCAT functions are required when more than two values are to be concatenated.
Lists all employee names (concatenated).
In Oracle, use the following:
SELECT EMP_LNAME || ‘, ‘ || EMP_FNAME AS NAME FROM EMPLOYEE;
In Access and MS SQL Server, use the following: SELECT EMP_LNAME + ‘, ‘ + EMP_FNAME AS NAME FROM EMPLOYEE;
In MySQL, use the following:
SELECT CONCAT(CONCAT(EMP_LNAME, ‘, ‘),
EMP_FNAME AS NAME FROM EMPLOYEE;
UPPER Oracle, MS SQL Server, and MySQL UCASE MySQL and Access
LOWER Oracle, MS SQL Server, and MySQL LCASE MySQL and Access
Returns a string in all capital or all lowercase letters Syntax:
UPPER(strg_value)
UCASE(strg_value)
LOWER(strg_value) LCASE(strg_value)
Lists all employee names in all capital letters (concatenated). In Oracle, use the following:
SELECT UPPER(EMP_LNAME || ‘, ‘ || EMP_FNAME) AS NAME FROM EMPLOYEE;
In MS SQL Server, use the following:
SELECT UPPER(EMP_LNAME + ‘, ‘ + EMP_FNAME) AS NAME FROM EMPLOYEE;
In Access, use the following:
SELECT UCASE(EMP_LNAME & ‘, ‘ & EMP_FNAME) AS NAME FROM EMPLOYEE;
In MySQL, use the following:
SELECT UPPER(CONCAT(CONCAT(EMP_LNAME, ‘, ‘),
EMP_FNAME AS NAME FROM EMPLOYEE;
Lists all employee names in all lowercase letters (concatenated). In Oracle, use the following:
SELECT LOWER(EMP_LNAME || ‘, ‘ || EMP_FNAME) AS NAME FROM EMPLOYEE;
In MS SQL Server, use the following:
SELECT LOWER(EMP_LNAME + ‘, ‘ + EMP_FNAME) AS NAME FROM EMPLOYEE;
In Access, use the following:
SELECT LCASE(EMP_LNAME & ‘, ‘ & EMP_FNAME) AS NAME FROM EMPLOYEE;
In MySQL, use the following:
SELECT LOWER(CONCAT(CONCAT(EMP_LNAME, ‘, ‘),
EMP_FNAME AS NAME FROM EMPLOYEE;
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368 Part 3 Advanced Design and Implementation
TABLE8.7 (CONTINUED)
SELECTED STRING FUNCTIONS
FUNCTION EXAMPLE(S)
SUBSTRING
Returns a substring or part of a given string parameter
Syntax:
SUBSTR(strg_value, p, l) Oracle and MySQL SUBSTRING(strg_value,p,l) MS SQL
Server and MySQL
MID(strg_value,p,l) Access
p = start position
l = length of characters
If the length of characters is omitted, the functions will return the remainder of the string value.
Lists the first three characters of all employee phone numbers. In Oracle or MySQL, use the following:
SELECT EMP_PHONE, SUBSTR(EMP_PHONE,1,3) AS PREFIX FROM EMPLOYEE;
In MS SQL Server or MySQL, use the following:
SELECT EMP_PHONE, SUBSTRING(EMP_PHONE,1,3) AS PREFIX FROM EMPLOYEE;
In Access, use the following:
SELECT EMP_PHONE, MID(EMP_PHONE, 1,3) AS PREFIX FROM EMPLOYEE;
LENGTH
Returns the number of characters in a string value Syntax:
LENGTH(strg_value) Oracle and MySQL LEN(strg_value) MS SQL Server and Access
Lists all employee last names and the length of their names in descending order by last name length.
In Oracle and MySQL, use the following:
SELECT EMP_LNAME, LENGTH(EMP_LNAME) AS NAMESIZE FROM EMPLOYEE;
In MS Access and SQL Server, use the following:
SELECT EMP_LNAME, LEN(EMP_LNAME) AS NAMESIZE FROM EMPLOYEE;
8-3d Conversion Functions
Conversion functions allow you to take a value of a given data type and convert it to the equivalent value in another data type. In Section 8-3a, you learned about two basic Oracle SQL conversion functions: TO_CHAR and TO_DATE. Note that the TO_CHAR function takes a date value and returns a character string representing a day, a month, or a year. In the same way, the TO_DATE function takes a character string represent- ing a date and returns an actual date in Oracle format. SQL Server uses the CAST and CONVERT functions to convert one data type to another. A summary of the selected functions is shown in Table 8.8.
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TABLE 8.8
SELECTED CONVERSION FUNCTIONS
FUNCTION EXAMPLE(S)
Chapter 8 Advanced SQL 369
Numeric or Date to Character:
TO_CHAR Oracle
CAST Oracle, MS SQL Server, MySQL
CONVERT MS SQL Server, MySQL
CSTR Access
Returns a character string from a numeric or date value.
Syntax:
TO_CHAR(value-to-convert, fmt)
fmt = format used; can be:
9 = displays a digit
0 = displays a leading zero
, = displays the comma
. = displays the decimal point
$= displays the dollar sign
B = leading blank
S = leading sign
MI = trailing minus sign
CAST (value-to-convert AS char(length))
Note that Oracle and MS SQL Server can use CAST to convert the numeric data into fixed length or variable length character data type.
MySQL cannot CAST into variable length character data, only fixed length.
MS SQL Server:
CONVERT(varchar(length), value-to-convert)
MySQL:
CONVERT(value-to-convert, char(length))
The primary difference between CAST and CONVERT is that CONVERT can also be used to change the char- acter set of the data.
CSTR(value-to-convert)
Lists all product prices, product received date, and percent discount using formatted values.
TO_CHAR:
SELECT P_CODE,
TO_CHAR(P_PRICE,’999.99’) AS PRICE, TO_CHAR(P_INDATE, ‘MM/DD/YYYY’) AS INDATE, TO_CHAR(P_DISCOUNT,’0.99′) AS DISC
FROM PRODUCT;
CAST in Oracle and MS SQL Server:
SELECT P_CODE, CAST(P_PRICE AS VARCHAR(8)) AS PRICE,
CAST(P_INDATE AS VARCHAR(20)) AS INDATE,
CAST(P_DISCOUNT AS VARCHAR(4)) AS DISC FROM PRODUCT;
CAST in MySQL:
SELECT P_CODE, CAST(P_PRICE AS CHAR(8)) AS PRICE,
CAST(P_INDATE AS CHAR(20)) AS INDATE,
CAST(P_DISCOUNT AS CHAR(4)) AS DISC FROM PRODUCT;
CONVERT in MS SQL Server:
SELECT P_CODE, CONVERT(VARCHAR(8), P_PRICE) AS PRICE,
CONVERT(VARCHAR(20), P_INDATE) AS INDATE,
CONVERT(VARCHAR(4), P_DISC) AS DISC FROM PRODUCT;
CONVERT in MySQL:
SELECT P_CODE, CONVERT(P_PRICE, CHAR(8)) AS PRICE,
CONVERT(P_INDATE, CHAR(20)) AS INDATE,
CONVERT(P_DISC, CHAR(4)) AS DISC FROM PRODUCT;
CSTR in Access:
SELECT P_CODE, CSTR(P_PRICE) AS PRICE,
CSTR(P_INDATE) AS INDATE,
CSTR(P_DISC) AS DISCOUNT FROM PRODUCT;
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370 Part 3 Advanced Design and Implementation
TABLE8.8 (CONTINUED)
SELECTED CONVERSION FUNCTIONS
FUNCTION EXAMPLE(S)
String to Number:
TO_NUMBER Oracle
CAST Oracle, MS SQL Server, MySQL
CONVERT MS SQL Server, MySQL
CINT Access
CDEC Access
Returns a number from a character string
Syntax:
Oracle:
TO_NUMBER(char_value, fmt)
fmt = format used; can be:
9 = indicates a digit
B = leading blank
S = leading sign
MI = trailing minus sign
CAST (value-to-convert as numeric-data type) Note that in addition to the INTEGER and DECIMAL(l,d) data types, Oracle supports NUMBER and MS SQL Server supports NUMERIC.
MS SQL Server:
CONVERT(value-to-convert, decimal(l,d))
MySQL:
CONVERT(value-to-convert, decimal(l,d))
Other than the data type to be converted into, these functions operate the same as described above.
CINT in Access returns the number in the integer data type, while CDEC returns decimal data type.
Converts text strings to numeric values when importing data to a table from another source in text format; for example, the query shown here uses the TO_NUMBER function to convert text formatted to Oracle default numeric values using the format masks given.
TO_NUMBER:
SELECT
FROM CAST: SELECT
TO_NUMBER(‘−123.99’, ‘S999.99’), TO_NUMBER(‘99.78−’,’B999.99MI’) DUAL;
CAST(‘−123.99’ AS DECIMAL(8,2)),
CAST(‘−99.78’ AS DECIMAL(8,2));
The CAST function does not support the trailing sign on the character string.
CINT and CDEC:
SELECT CINT(‘−123’), CDEC(‘−123.99’);
CASE Oracle, MS SQL Server, MySQL
DECODE Oracle
SWITCH Access
Compares an attribute or expression with a series of values and returns an associated value or a default value if no match is found
Syntax:
DECODE:
DECODE(e, x, y, d)
e = attribute or expression
x = value with which to compare e
y = value to return in e = x
d = default value to return if e is not equal to x
CASE:
CASE When condition
THEN value1 ELSE value2 END
SWITCH:
SWITCH(e1, x, e2, y, TRUE, d)
e1 = comparison expression
x = value to return if e1 is true
e2 = comparison expression
y = value to return if e2 is true
TRUE = keyword indicating the next value is the default d = default value to return if none of the expressions were true
The following example returns the sales tax rate for specified states:
Compares V_STATE to ‘CA’; if the values match, it returns .08. Compares V_STATE to ‘FL’; if the values match, it returns .05. Compares V_STATE to ‘TN’; if the values match, it returns .085. If there is no match, it returns 0.00 (the default value). SELECT V_CODE, V_STATE,
DECODE(V_STATE,’CA’,.08,’FL’,.05, ‘TN’,.085, 0.00)
ASTAX FROM VENDOR;
CASE:
SELECT V_CODE, V_STATE,
CASE WHEN V_STATE = ‘CA’ THEN .08 WHEN V_STATE = ‘FL’ THEN .05
WHEN V_STATE = ‘TN’ THEN .085
ELSE 0.00 END AS TAX FROM VENDOR
SWITCH:
SELECT V_CODE, V_STATE,
SWITCH(V_STATE =’CA’,.08, V_STATE = ‘FL’,.05, V_STATE = ‘TN’,.085,
TRUE, 0.00) AS TAX
FROM VENDOR;
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8-4 Relational Set Operators
In Chapter 3, The Relational Database Model, you learned about the eight general relational operators. In this section, you will learn how to use three SQL commands— UNION, INTERSECT, and EXCEPT (MINUS)—to implement the union, intersection, and difference relational operators.
In previous chapters, you learned that SQL data manipulation commands are set- oriented; that is, they operate over entire sets of rows and columns (tables) at once. You can combine two or more sets to create new sets (or relations). That is precisely what the UNION, INTERSECT, and EXCEPT (MINUS) statements do. In relational database terms, you can use the words sets, relations, and tables interchangeably because they all provide a conceptual view of the data set as it is presented to the relational database user.
Note
UNION, INTERSECT, and EXCEPT (MINUS) work properly only if relations are union-compatible, which means that the number of attributes must be the same and their corresponding data types must be alike. In practice, some RDBMS vendors require the data types to be compatible but not exactly the same. For example, compatible data types are VARCHAR (35) and CHAR (15). Both attributes store character (string) values; the only difference is the string size. Another example of compatible data types is NUMBER and SMALLINT. Both data types are used to store numeric values.
Note
8-4a UNION
Suppose that SaleCo has bought another company. SaleCo’s management wants to make sure that the acquired company’s customer list is properly merged with its own customer list. Because some customers might have purchased goods from both companies, the two lists might contain common customers. SaleCo’s management wants to make sure that customer records are not duplicated when the two customer lists are merged. The UNION query is a perfect tool for generating a combined listing of customers—one that excludes duplicate records.
model, SQL operators are set-oriented because they operate over
entire sets of rows and columns at once.
union-compatible
Two or more tables
that share the same number of columns
and have columns with compatible data types or domains.
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The SQL standard defines the operations that all DBMSs must perform on data, but it leaves the implementation details to the DBMS vendors. Therefore, some advanced SQL features might not work on all DBMS implementations. Also, some DBMS vendors might implement additional features not found in the SQL standard. The SQL standard defines UNION, INTERSECT, and EXCEPT as the keywords for the UNION, INTERSECT, and DIFFERENCE relational operators, and these are the names used in MS SQL Server. However, Oracle uses MINUS as the name of the DIFFERENCE operator instead of EXCEPT. Other RDBMS vendors might use a different command name or might not implement a given command at all. For example, Access and MySQL do not have direct support for INTERSECT or DIFFERENCE operations because that functionality can be achieved using combinations of joins and subqueries. To learn more about the ANSI/ISO SQL standards and find out how to obtain the latest standard documents in electronic form, check the ANSI website (www.ansi.org).
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Some DBMS products might require union-compatible tables to have identical data types.
set-oriented
Dealing with or related to sets, or groups of things. In the relational

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The UNION statement combines rows from two or more queries without including duplicate rows. The syntax of the UNION statement is:
query UNION query
In other words, the UNION statement combines the output of two SELECT queries. (Remember that the SELECT statements must be union-compatible. That is, they must return the same number of attributes and similar data types.)
To demonstrate the use of the UNION statement in SQL, use the CUSTOMER and CUSTOMER_2 tables in the Ch08_SaleCo database. To show the combined CUSTOMER and CUSTOMER_2 records without duplicates, the UNION query is written as follows:
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE, CUS_PHONE
FROM CUSTOMER
UNION
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE,
CUS_PHONE FROM CUSTOMER_2;
Figure 8.16 shows the contents of the CUSTOMER and CUSTOMER_2 tables and the result of the UNION query. Although MS Access is used to show the results here, similar results can be obtained with Oracle, MS SQL Server, and MySQL.
Note the following in Figure 8.16:
• The CUSTOMER table contains 10 rows, while the CUSTOMER_2 table contains seven rows.
• Customers Dunne and Olowski are included in the CUSTOMER table as well as the CUSTOMER_2 table.
• The UNION query yields 15 records because the duplicate records of customers Dunne and Olowski are not included. In short, the UNION query yields a unique set of records.
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FIGURE8.16 UNIONQUERYRESULTS
Database name: Ch08_SaleCo Table name: CUSTOMER Query name: qryUNION-of-CUSTOMER-and-CUSTOMER_2
Table name: CUSTOMER_2

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The SQL standard calls for the elimination of duplicate rows when the UNION SQL statement is used. However, some DBMS vendors might not adhere to that standard. Check your DBMS manual to see if the UNION statement is supported, and if so, how it is supported.
The UNION statement can be used to unite more than just two queries. For example, assume that you have four union-compatible queries named T1, T2, T3, and T4. With the UNION statement, you can combine the output of all four queries into a single result set. The SQL statement will be similar to this:
SELECT column-list FROM T1 UNION
SELECT column-list FROM T2 UNION
SELECT column-list FROM T3 UNION
SELECT column-list FROM T4;
8-4b UNION ALL
If SaleCo’s management wants to know how many customers are on both the CUSTOMER and CUSTOMER_2 lists, a UNION ALL query can be used to produce a relation that retains the duplicate rows. Therefore, the following query will keep all rows from both queries (including the duplicate rows) and return 17 rows.
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE, CUS_PHONE
FROM CUSTOMER
UNION ALL
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE,
CUS_PHONE FROM CUSTOMER_2;
Running the preceding UNION ALL query produces the result shown in Figure 8.17. Like the UNION statement, the UNION ALL statement can be used to unite more than just two queries.
8-4c INTERSECT
If SaleCo’s management wants to know which customer records are duplicated in the CUSTOMER and CUSTOMER_2 tables, the INTERSECT statement can be used to combine rows from two queries, returning only the rows that appear in both sets. The syntax for the INTERSECT statement is:
query INTERSECT query
To generate the list of duplicate customer records, you can use the following commands:
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE, CUS_PHONE
FROM CUSTOMER
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INTERSECT
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE,
CUS_PHONE FROM CUSTOMER_2;
FIGURE8.17 UNIONALLQUERYRESULTS
Database name: Ch08_SaleCo Table name: CUSTOMER Query name: qryUNION-ALL-of-CUSTOMER-and-CUSTOMER_2
Table name: CUSTOMER_2
The INTERSECT statement can be used to generate additional useful customer informa- tion. For example, the following query returns the customer codes for all customers who are in area code 615 and who have made purchases. (If a customer has made a purchase, there must be an invoice record for that customer.)
SELECT CUS_CODE FROM CUSTOMER WHERE CUS_AREACODE = ‘615’ INTERSECT
SELECT DISTINCT CUS_CODE FROM INVOICE;
Figure 8.18 shows both sets of SQL statements and their output.
FIGURE8.18 INTERSECTQUERYRESULTS
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Note
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Access and MySQL do not support the INTERSECT query. These DBMSs are able to give the desired results using alternative query formats. For example, INTERSECT results can also be produced in Access and MySQL through an inner join that includes all of the attributes to be returned in the join condition. The query:
SELECT CUS_AREACODE FROM CUSTOMER INTERSECT
SELECT V_AREACODE FROM VENDOR;
can also be produced without the INTERSECT command with the query: SELECT DISTINCT CUS_AREACODE
FROM CUSTOMER JOIN VENDOR ON CUS_AREACODE = V_AREACODE;
8-4d EXCEPT (MINUS)
The EXCEPT statement in SQL combines rows from two queries and returns only the rows that appear in the first set but not in the second. The syntax for the EXCEPT statement in MS SQL Server and the MINUS statement in Oracle is:
query EXCEPT query and
query MINUS query
For example, if the SaleCo managers want to know which customers in the CUSTOMER table are not found in the CUSTOMER_2 table, they can use the following commands in Oracle:
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE, CUS_PHONE
FROM CUSTOMER
MINUS
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE,
CUS_PHONE FROM CUSTOMER_2;
If the managers want to know which customers in the CUSTOMER_2 table are not found in the CUSTOMER table, they merely switch the table designations:
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE, CUS_PHONE
FROM CUSTOMER_2
MINUS
SELECT CUS_LNAME, CUS_FNAME, CUS_INITIAL, CUS_AREACODE,
CUS_PHONE FROM CUSTOMER;
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Users of MS SQL Server would substitute the keyword EXCEPT in place of MINUS, but otherwise the syntax is exactly the same. You can extract useful information by combining MINUS with various clauses such as WHERE. For example, the following query returns the customer codes for all customers in area code 615 minus the ones who have made purchases, leaving the customers in area code 615 who have not made purchases.
SELECT CUS_CODE FROM CUSTOMER WHERE CUS_AREACODE = ‘615’ MINUS
SELECT DISTINCT CUS_CODE FROM INVOICE;
Figure 8.19 shows the preceding three SQL statements and their output.
FIGURE8.19 MINUSQUERYRESULTS
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8-4e Syntax Alternatives
If your DBMS does not support the INTERSECT or EXCEPT (MINUS) statements, you can use IN and NOT IN subqueries to obtain similar results. For example, the following query will produce the same results as the INTERSECT query shown in Section 8-4c:
SELECT CUS_CODE FROM CUSTOMER WHERE CUS_AREACODE = ‘615’ AND
CUS_CODE IN (SELECT DISTINCT CUS_CODE FROM INVOICE); Figure 8.20 shows the use of the INTERSECT alternative.
Using the same alternative to the MINUS statement, you can generate the output for the third MINUS query shown in Section 8-4d by entering the following:
SELECT CUS_CODE FROM CUSTOMER WHERE CUS_AREACODE = ‘615’ AND
CUS_CODE NOT IN (SELECT DISTINCT CUS_CODE FROM INVOICE);
The results of the query are shown in Figure 8.21. Note that the query output includes only the customers in area code 615 who have not made any purchases and therefore have not generated invoices.
8-5 Virtual Tables: Creating a View
As you learned earlier, the output of a relational operator such as SELECT is another relation (or table). Suppose that at the end of each day, you would like to have a list of all products to reorder—that is, products with a quantity on hand that is less than or equal to the minimum quantity. Instead of typing the same query at the end of each day, wouldn’t it be better to permanently save that query in the database? That is the function of a relational view. A view is a virtual table based on a SELECT query. The query can contain columns, computed columns, aliases, and aggregate functions from one or more tables. The tables on which the view is based are called base tables.
view
A virtual table based on a SELECT query that is saved as an object in the database.
base table
The table on which a view is based.
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FIGURE8.20 INTERSECTALTERNATIVE
Table name: CUSTOMER
Database name: Ch08_SaleCo Table name: INVOICE
Query name: qry-INTERSECT-Alternative
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FIGURE8.21 MINUSALTERNATIVE
Table name: CUSTOMER
Database name: Ch08_SaleCo Table name: INVOICE
Query name: qry-MINUS-Alternative
You can create a view by using the CREATE VIEW command:
CREATE VIEW viewname AS SELECT query
The CREATE VIEW statement is a data definition command that stores the subquery specification—the SELECT statement used to generate the virtual table—in the data dictionary. The first SQL command set in Figure 8.22 shows the syntax used to create a view named PRICEGT50. This view contains only the designated three attributes (P_DESCRIPT, P_QOH, and P_PRICE) and only rows in which the price is over $50. The second SQL
command sequence in Figure 8.22 shows the rows that make up the view.
FIGURE8.22 CREATINGAVIRTUALTABLEWITHTHECREATEVIEWCOMMAND
CREATE VIEW
A SQL command that creates a logical, “virtual” table. The view can be treated as a real table.
Note
Note to MS Access Users
The CREATE VIEW command is not directly supported in MS Access. To create a view in MS Access, you simply create a SQL query and then save it.
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A relational view has several special characteristics:
• You can use the name of a view anywhere a table name is expected in a SQL statement.
• Views are dynamically updated. That is, the view is re-created on demand each time it is invoked. Therefore, if new products are added or deleted to meet the criterion P_PRICE > 50.00, those new products will automatically appear or disappear in the PRICEGT50 view the next time the view is invoked.
• Views provide a level of security in the database because they can restrict users to seeing only specified columns and rows in a table. For example, if you have a company with hundreds of employees in several departments, you could give each department secretary a view of certain attributes only for the employees who belong to that secretary’s department.
• Views may also be used as the basis for reports. For example, if you need a report that shows a summary of total product cost and quantity-on-hand statistics grouped by vendor, you could create a PROD_STATS view as:
CREATE VIEW PROD_STATS AS
SELECT
FROM GROUP BY
V_CODE, SUM(P_QOH*P_PRICE) AS TOTCOST, MAX(P_QOH) AS MAXQTY, MIN(P_QOH) AS MINQTY, AVG(P_QOH) AS AVGQTY
PRODUCT
V_CODE;
8-5a UpdatableViews
One of the most common operations in production database environments is to use batch update routines to update a master table attribute (field) with transaction data. As the name implies, a batch update routine pools multiple transactions into a single batch to update a master table field in a single operation. For example, a batch update routine is commonly used to update a product’s quantity on hand based on summary sales transactions. Such routines are typically run as overnight batch jobs to update the quantity on hand of products in inventory. For example, the sales transactions performed by traveling salespeople can be entered during periods when the system is offline.
To perform a batch update routine, begin by defining the master product table (PRODMASTER) and the product monthly sales totals table (PRODSALES) shown in Figure 8.23. Note the 1:1 relationship between the two tables.
Online Content
The PRODMASTER and PRODSALES tables are in the Ch08_UV databases for the different DBMSs, which are available at www.cengagebrain.com.
batch update routine
A routine that pools transactions into a single group to update a master table in a single operation.
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FIGURE 8.23 THE PRODMASTER AND PRODSALES TABLES
Database name: Ch08_UV Table name: PRODMASTER Table name: PRODSALES
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Note
Using the tables in Figure 8.23, update the PRODMASTER table by subtracting the PRODSALES table’s product monthly sales quantity (PS_QTY) from the PRODMASTER table’s PROD_QOH. To produce the required update, the update query would be written like this:
UPDATE SET WHERE
PRODMASTER, PRODSALES PRODMASTER.PROD_QOH = PROD_QOH – PS_QTY PRODMASTER.PROD_ID = PRODSALES.PROD_ID;
Note that the update statement reflects the following sequence of events:
• Join the PRODMASTER and PRODSALES tables.
• Update the PROD_QOH attribute (using the PS_QTY value in the PRODSALES table) for each row of the PRODMASTER table with matching PROD_ID values in the PRODSALES table.
Updating using multiple tables in MS SQL Server requires the UPDATE FROM syntax. The above code would be written in MS SQL Server as the following:
UPDATE PRODMASTER
SET PROD_QOH = PROD_QOH – PS_QTY
FROM PRODMASTER JOIN PRODSALES ON PRODMASTER.PROD_ID = PRODSALES. PROD_ID;
FIGURE 8.24 THE ORACLE UPDATE ERROR MESSAGE
updatable view
A view that can update attributes in base tables that are used in the view.
To be used in a batch update, the PRODSALES data must be stored in a base table rather than in a view. The query will work in MySQL and Access, but Oracle will return the error message shown in Figure 8.24.
Oracle produced the error message because it expected to find a single table name in the UPDATE statement. In fact, you cannot join tables in the UPDATE statement in Oracle. To solve that problem, you have to create an updatable view. As its name suggests, an updatable view can be used to update attributes in any base table(s) used in the view. You must realize that not all views are updatable. Actually, several restrictions govern updatable views, and some of them are vendor-specific.
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While the examples in this section are generated in Oracle, the same code and techniques also work in MS SQL Server, MySQL, and Access. To see what additional restrictions are placed on updatable views by the DBMS you are using, check the appropriate DBMS documentation.
The most common updatable view restrictions are as follows:
• GROUP BY expressions or aggregate functions cannot be used.
• You cannot use set operators such as UNION, INTERSECT, and MINUS.
• Most restrictions are based on the use of JOINs or group operators in views. More specifically, the base table to be updated must be key-preserved, meaning that the values of the primary key of the base table must still be unique by definition in the view.
An updatable view named PSVUPD has been created, as shown in Figure 8.25.
One easy way to determine whether a view can be used to update a base table is to examine the view’s output. If the primary key columns of the base table you want to update still have unique values in the view, the base table is updatable. For example, if the PROD_ID column of the view returns the A123 or BX34 values more than once, the PRODMASTER table cannot be updated through the view.
After creating the updatable view shown in Figure 8.25, you can use the UPDATE command to update the view, thereby updating the PRODMASTER table. Figure 8.26 shows how the UPDATE command is used and shows the final contents of the PRODMASTER table after the UPDATE has been executed.
Although the batch update procedure just illustrated meets the goal of updating a master table with data from a transaction table, the preferred real-world solution to the update problem is to use procedural SQL, which you will learn about later in this chapter.
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FIGURE8.25 CREATINGANUPDATABLEVIEW

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FIGURE8.26 PRODMASTERTABLEUPDATE,USINGANUPDATABLEVIEW
8-6 Sequences
If you use MS Access, you might be familiar with the AutoNumber data type, which you can use to define a column in your table that will be automatically populated with unique numeric values. In fact, if you create a table in MS Access and forget to define a primary key, MS Access will offer to create a primary key column; if you accept, you will notice that MS Access creates a column named “ID” with an AutoNumber data type. After you define a column as an AutoNumber type, every time you insert a row in the table, MS Access will automatically add a value to that column, starting with 1 and increasing the value by 1 in every new row you add. Also, you cannot include that column in your INSERT statements—Access will not let you edit that value at all. MS SQL Server tradi- tionally has used the Identity column property to serve a similar purpose. In MS SQL Server, a table can have at most one column defined as an Identity column. This column behaves similarly to an MS Access column with the AutoNumber data type. MySQL uses the AUTO_INCREMENT property during table creation to indicate that values for an attribute should be generated in the same fashion. AUTO_INCREMENT can be adjusted to start with a value other than 1. Similar to IDENTITY columns in MS SQL Server, only one column in a table can have AUTO_INCREMENT specified, and that column must also be defined as the primary key of the table.
Oracle does not support the AutoNumber data type, or Auto_Increment column properties. Traditionally, Oracle uses a sequence to assign values to a column on a table. However, beginning in Oracle 12c, Oracle has added support for Identity columns, and beginning in MS SQL Server 2012, SQL Server supports sequences. There are many similarities in the use of sequences across these DBMS so a database programmer who is comfortable with one should be able to easily transition to the other. However,
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a sequence is very different from the Access AutoNumber data type and deserves closer scrutiny:
• Sequences are an independent object in the database. (Sequences are not a data type.)
• Sequences have a name.
• Sequences can be used anywhere a value is expected.
• Sequences are not tied to a table or a column.
• Sequences generate a numeric value that can be assigned to any column in any table.
• The table attribute to which you assigned a value based on a sequence can be edited
and modified.
The basic syntax to create a sequence is as follows:
CREATE SEQUENCE name [START WITH n] [INCREMENT BY n] [CACHE | NOCACHE]
where
• name is the name of the sequence.
• n is an integer value that can be positive or negative.
• START WITH specifies the initial sequence value. (The default value is 1.)
• INCREMENT BY determines the value by which the sequence is incremented. (The default increment value is 1. The sequence increment can be positive or negative to enable you to create ascending or descending sequences.)
• The CACHE or NOCACHE/NO CACHE clause indicates whether the DBMS will preallocate sequence numbers in memory. Oracle uses NOCACHE as one word and preallocates 20 values by default. SQL Server uses NO CACHE as two words. If a cache size is not specified in SQL Server, then the DBMS will determine a default cache size that is not guaranteed to be consistent across different databases.
For example, you could create a sequence to automatically assign values to the customer code each time a new customer is added, and create another sequence to automatically assign values to the invoice number each time a new invoice is added. The SQL code to accomplish those tasks is:
CREATE SEQUENCE CUS_CODE_SEQ START WITH 20010 NOCACHE; CREATE SEQUENCE INV_NUMBER_SEQ START WITH 4010 NOCACHE;
Note
You can check all of the sequences you have created by using the following SQL command, as illustrated in Figure 8.27.
SELECT * FROM USER_SEQUENCES;
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Remember, SQL Server uses NO CACHE as two words so the corresponding commands in SQL Server would be:
CREATE SEQUENCE CUS_CODE_SEQ START WITH 20010 NO CACHE; CREATE SEQUENCE INV_NUMBER_SEQ START WITH 4010 NO CACHE;

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FIGURE8.27 ORACLESEQUENCE
Note
SQL Server also stores sequences as schema level objects so they can be viewed with the command
SELECT * FROM SYS.SEQUENCES;
Oracle and SQL Server differ slightly in the syntax for retrieving a value from the sequence. In SQL Server, the phrase NEXT VALUE FOR causes the sequence to generate and return the next value. In Oracle, you must use two special pseudo-columns: NEXTVAL and CURRVAL. NEXTVAL retrieves the next available value from a sequence, and CURRVAL retrieves the current value of a sequence. For example, you can use the following code to enter a new customer in SQL Server:
INSERT INTO CUSTOMER
VALUES (NEXT VALUE FOR CUS_CODE_SEQ, ‘Connery’, ‘Sean’, NULL, ‘615’, ‘898-2007’, 0.00);
In Oracle, you would use:
INSERT INTO CUSTOMER
VALUES (CUS_CODE_SEQ.NEXTVAL, ‘Connery’, ‘Sean’, NULL, ‘615’, ‘898-2007′, 0.00);
The preceding SQL statement adds a new customer to the CUSTOMER table and assigns the value 20010 to the CUS_CODE attribute. Examine some important sequence characteristics:
• CUS_CODE_SEQ.NEXTVAL retrieves the next available value from the sequence.
• Each time you use NEXTVAL, the sequence is incremented.
• Once a sequence value is used (through NEXTVAL), it cannot be used again. If your SQL statement rolls back for some reason, the sequence value does not roll back. If you issue another SQL statement (with another NEXTVAL), the next available sequence value will be returned to the user—it will look like the sequence skips a number.
• You can issue an INSERT statement without using the sequence.
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In Oracle, CURRVAL retrieves the current value of a sequence—that is, the last sequence number used, which was generated with a NEXTVAL. You cannot use CUR- RVAL unless a NEXTVAL was issued previously in the same session. The main use for CURRVAL is to enter rows in dependent tables. For example, the INVOICE and LINE tables are related in a one-to-many relationship through the INV_NUMBER attri- bute. You can use the INV_NUMBER_SEQ sequence to automatically generate invoice numbers. Then, using CURRVAL, you can get the latest INV_NUMBER used and assign it to the related INV_NUMBER foreign key attribute in the LINE table. For example:
INSERT INTO INVOICE VALUES INSERT INTO LINE VALUES INSERT INTO LINE VALUES COMMIT;
The results are shown in Figure 8.28.
(INV_NUMBER_SEQ.NEXTVAL, 20010, SYSDATE); (INV_NUMBER_SEQ.CURRVAL, 1,’13-Q2/P2′, 1, 14.99); (INV_NUMBER_SEQ.CURRVAL, 2,’23109-HB’, 1, 9.95);
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FIGURE8.28 ORACLESEQUENCEEXAMPLES
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386 Part 3 Note
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The reason that CURRVAL can only be used in the same session as a NEXTVAL is because the value returned by CURRVAL is not actually provided by the sequence. Remember, the sequence will not generate a value twice. Whenever NEXTVAL is called, Oracle makes a note of the call, which sequence was called, and what number was provided in the user’s session information. When CURRVAL is invoked, the DBMS will look in the session informa- tion to see what the last value provided to that user by that sequence was. This is very pow- erful in a multiuser environment. For example, imagine that Maria and Zameer are working in the same database application and with the same data. When Maria calls on a sequence with NEXTVAL, she is provided a new value. If Zameer calls the same sequence with NEXT- VAL, he is provided a number different from Maria’s number. If, in her same session, Maria now calls on the sequence with CURRVAL, she is not provided with the last number that the sequence generated (which was given to Zameer), but she is given the last number that the sequence provided to her session! Similarly, CURRVAL would provide Zameer with the last value provided to his session. Since this information about the last value provided by the sequence to each user is kept in the user’s session information, when Maria discon- nects from the database, ending her session, that information is lost. If she reconnects to the database, she will be starting a new session. If she immediately calls on CURRVAL, she will get an error because the DBMS does not have a record of that session being provided any values from the sequence.
Note
In the example shown in Figure 8.28, INV_NUMBER_SEQ.NEXTVAL retrieves the next available sequence number (4010) and assigns it to the INV_NUMBER column in the INVOICE table. Also note the use of the SYSDATE attribute to automatically insert the current date in the INV_DATE attribute. Next, the following two INSERT statements add the products being sold to the LINE table. In this case, INV_NUMBER_SEQ.CURRVAL refers to the last-used INV_NUMBER_SEQ sequence number (4010). In this way, the relationship between INVOICE and LINE is established automatically. The COMMIT statement at the end of the command sequence makes the changes permanent. Of course, you can also issue a ROLLBACK statement, in which case the rows you inserted in the INVOICE and LINE tables would be rolled back (but remember that the sequence number would not). Once you use a sequence number with NEXTVAL, there is no way to reuse it! This “no-reuse” characteristic is designed to guarantee that the sequence will always gen- erate unique values.
At this writing, SQL Server does not provide a direct equivalent to Oracle’s CURRVAL. If you wish to find the last number generated by a sequence in SQL Server, you can retrieve it by querying the metadata, but this will only give the last number generated by the sequence for any user.
Remember these points when you think about sequences:
• The use of sequences is optional. You can enter the values manually.
• A sequence is not associated with a table. As in the examples in Figure 8.28, two distinct sequences were created (one for customer code values and one for invoice number values), but you could have created just one sequence and used it to generate unique values for both tables.
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Note
Finally, you can drop a sequence from a database with a DROP SEQUENCE com- mand. For example, to drop the sequences created earlier, you would type:
DROP SEQUENCE CUS_CODE_SEQ; DROP SEQUENCE INV_NUMBER_SEQ;
Dropping a sequence does not delete the values you assigned to table attributes (CUS_ CODE and INV_NUMBER); it deletes only the sequence object from the database. The values you assigned to the table columns (CUS_CODE and INV_NUMBER) remain in the database.
Because the CUSTOMER and INVOICE tables are used in the following examples, you will want to keep the original data set. Therefore, you can delete the customer, invoice, and line rows you just added by using the following commands:
DELETE FROM INVOICE WHERE INV_NUMBER = 4010; DELETE FROM CUSTOMER WHERE CUS_CODE = 20010; COMMIT;
Those commands delete the recently added invoice, all of the invoice line rows associated with the invoice (the LINE table’s INV_NUMBER foreign key was defined with the ON DELETE CASCADE option), and the recently added customer. The COMMIT statement saves all changes to permanent storage.
Note
8-7 Procedural SQL
Thus far, you have learned to use SQL to read, write, and delete data in the database. For example, you learned to update values in a record, to add records, and to delete records. Unfortunately, SQL does not support the conditional execution of procedures that are typically supported by a programming language using the general format:
IF
THEN Chapter 8
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The SQL standard defines the use of Identity columns and sequence objects. However, some DBMS vendors might not adhere to the standard. Check your DBMS documentation.
At this point, you need to re-create the CUS_CODE_SEQ and INV_NUMBER_SEQ sequences, as they will be used again later in the chapter. Enter:
CREATE SEQUENCE CUS_CODE_SEQ START WITH 20010 NOCACHE; CREATE SEQUENCE INV_NUMBER_SEQ START WITH 4010 NOCACHE;
END IF
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388 Part 3
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SQL also fails to support looping operations in programming languages that permit the execution of repetitive actions typically encountered in a programming environ- ment. The typical format is:
DO WHILE END DO
Traditionally, if you wanted to perform a conditional or looping type of operation (that is, a procedural type of programming using an IF-THEN-ELSE or DO-WHILE state- ment), you would use a programming language such as Visual Basic .NET, C#, or Java. Although that approach is still common, it usually involves the duplication of application code in many programs. Therefore, when procedural changes are required, modifica- tions must be made in many different programs. An environment characterized by such redundancies often creates data-management problems.
A better approach is to isolate critical code and then have all application programs call the shared code. The advantage of this modular approach is that the application code is isolated in a single program, thus yielding better maintenance and logic control. In any case, the rise of distributed databases and object-oriented databases required that more application code be stored and executed within the database. (For more informa- tion on these databases, see Chapter 12, Distributed Database Management Systems, and Appendix G, Object-Oriented Databases, at www.cengagebrain.com, respectively.) To meet that requirement, most RDBMS vendors created numerous programming language extensions. Those extensions include:
• Flow-control procedural programming structures (IF-THEN-ELSE, DO-WHILE) for logic representation
• Variable declaration and designation within the procedures
• Error management
To remedy the lack of procedural functionality in SQL and to provide some stan- dardization within the many vendor offerings, the SQL-99 standard defined the use of persistent stored modules. A persistent stored module (PSM) is a block of code con- taining standard SQL statements and procedural extensions that is stored and executed at the DBMS server. The PSM represents business logic that can be encapsulated, stored, and shared among multiple database users. A PSM lets an administrator assign specific access rights to a stored module to ensure that only authorized users can use it. Support for PSMs is left to each vendor to implement. In fact, for many years, some RDBMSs (such as Oracle, SQL Server, and DB2) supported stored procedure modules within the database before the official standard was promulgated.
MS SQL Server implements PSMs via Transact-SQL and other language extensions, the most notable of which are the .NET family of programming languages. Oracle imple- ments PSMs through its procedural SQL language. MySQL uses a procedural version of SQL that is similar in many respects to the Oracle procedural language. Procedural Language SQL (PL/SQL) is a language that makes it possible to use and store procedural code and SQL statements within the database and to merge SQL and traditional pro- gramming constructs, such as variables, conditional processing (IF-THEN-ELSE), basic loops (FOR and WHILE loops), and error trapping. The procedural code is executed as a unit by the DBMS when it is invoked (directly or indirectly) by the end user. End users can use PL/SQL to create:
• Anonymous PL/SQL blocks
• Triggers (covered in Section 8-7a)
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
persistent stored module (PSM)
A block of code with standard SQL statements and procedural extensions that is stored and executed at the DBMS server.
Procedural Language SQL (PL/SQL)
An Oracle-specific programming language based on SQL with procedural extensions designed to run inside the Oracle database.
Copyright 2017 Cengage Lea Editorial review has deemed that

• Stored procedures (covered in Section 8-7b and Section 8-7c)
• PL/SQL functions (covered in Section 8-7d)
Do not confuse PL/SQL functions with SQL’s built-in aggregate functions such as MIN and MAX. SQL built-in functions can be used only within SQL statements, while PL/SQL functions are mainly invoked within PL/SQL programs such as triggers and stored procedures. Functions can also be called within SQL statements, provided that they conform to very specific rules that are dependent on your DBMS environment.
Note
Using Oracle SQL*Plus, you can write a PL/SQL code block by enclosing the commands inside BEGIN and END clauses. For example, the following PL/SQL block inserts a new row in the VENDOR table, as shown in Figure 8.29.
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PL/SQL, triggers, and stored procedures are illustrated within the context of an Oracle DBMS. All examples in the following sections assume the use of Oracle RDBMS.
FIGURE8.29 ANONYMOUSPL/SQLBLOCKEXAMPLES

390 Part 3
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BEGIN
END; /
INSERT INTO VENDOR
VALUES (25678,’Microsoft Corp.’, ‘Bill Gates’,’765′,’546-8484′,’WA’,’N’);
The PL/SQL block shown in Figure 8.29 is known as an anonymous PL/SQL block because it has not been given a specific name. The block’s last line uses a forward slash ( / ) to indicate the end of the command-line entry. This type of PL/SQL block executes as soon as you press Enter after typing the forward slash. Following the PL/SQL block’s execution, you will see the message “PL/SQL procedure successfully completed.”
Suppose that you want a more specific message displayed on the SQL*Plus screen after a procedure is completed, such as “New Vendor Added.” To produce a more specific message, you must do two things:
1. At the SQL > prompt, type SET SERVEROUTPUT ON. This SQL*Plus command enables the client console (SQL*Plus) to receive messages from the server side (Oracle DBMS). Remember, just like standard SQL, the PL/SQL code (anonymous blocks, triggers, and procedures) are executed at the server side, not at the client side. To stop receiving messages from the server, you would enter SET SERVEROUTPUT OFF.
2. To send messages from the PL/SQL block to the SQL*Plus console, use the DBMS_ OUTPUT.PUT_LINE function.
The following anonymous PL/SQL block inserts a row in the VENDOR table and
displays the message “New Vendor Added!” (see Figure 8.29).
BEGIN
END; /
INSERT INTO VENDOR
VALUES (25772, ‘Clue Store’, ‘Issac Hayes’, ‘456’,’323-2009′, ‘VA’, ‘N’); DBMS_OUTPUT.PUT_LINE(‘New Vendor Added!’);
In Oracle, you can use the SQL*Plus command SHOW ERRORS to help you diagnose errors found in PL/SQL blocks. The SHOW ERRORS command yields additional debugging information whenever you generate an error after creating or executing a PL/SQL block.
The following example of an anonymous PL/SQL block demonstrates several of the con- structs supported by the procedural language. Remember that the exact syntax of the language is vendor-dependent; in fact, many vendors enhance their products with proprietary features.
DECLARE
W_P1 NUMBER(3) := 0; W_P2 NUMBER(3) := 10; W_NUM NUMBER(2) := 0; BEGIN
WHILE W_P2 < 300 LOOP SELECT COUNT(P_CODE) INTO W_NUM FROM PRODUCT WHERE P_PRICE BETWEEN W_P1 AND W_P2; DBMS_OUTPUT.PUT_LINE('There are ' || W_NUM || ' Products with price between ' || W_P1 || ' and ' || W_P2); W_P1 := W_P2 + 1; W_P2 := W_P2 + 50; END LOOP; END; / rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. anonymous PL/SQL block A PL/SQL block that has not been given a specific name. Copyright 2017 Cengage Lea Editorial review has deemed that The block’s code and execution are shown in Figure 8.30. Chapter 8 Advanced SQL 391 FIGURE8.30 ANONYMOUSPL/SQLBLOCKWITHVARIABLESANDLOOPS The PL/SQL block shown in Figure 8.30 has the following characteristics: • The PL/SQL block starts with the DECLARE section, in which you declare the variable names, the data types, and, if desired, an initial value. Supported data types are shown in Table 8.9. • A WHILE loop is used. Note the following syntax: WHILE condition LOOP PL/SQL statements; END LOOP • The SELECT statement uses the INTO keyword to assign the output of the query to a PL/SQL variable. You can use the INTO keyword only inside a PL/SQL block of code. If the SELECT statement returns more than one value, you will get an error. • Note the use of the string concatenation symbol ( || ) to display the output. • Each statement inside the PL/SQL code must end with a semicolon ( ; ). The most useful feature of PL/SQL blocks is that they let you create code that can be named, stored, and executed—either implicitly or explicitly—by the DBMS. That capa- bility is especially desirable when you need to use triggers and stored procedures, which you will explore next. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 392 Part 3 Advanced Design and Implementation TABLE 8.9 PL/SQL BASIC DATA TYPES DATA TYPE DESCRIPTION CHAR Character values of a fixed length; for example: W_ZIP CHAR(5) VARCHAR2 Variable-length character values; for example: W_FNAME VARCHAR2(15) NUMBER Numeric values; for example: W_PRICE NUMBER(6,2) DATE Date values; for example: W_EMP_DOB DATE %TYPE Inherits the data type from a variable that you declared previously or from an attribute of a database table; for example: W_PRICE PRODUCT.P_PRICE%TYPE Assigns W_PRICE the same data type as the P_PRICE column in the PRODUCT table Note PL/SQL blocks can contain only standard SQL data manipulation language (DML) com- mands such as SELECT, INSERT, UPDATE, and DELETE. The use of data definition language (DDL) commands is not directly supported in a PL/SQL block. 8-7a Triggers Automating business procedures and automatically maintaining data integrity and consistency are critical in a modern business environment. One of the most critical busi- ness procedures is proper inventory management. For example, you want to make sure that current product sales can be supported with sufficient product availability. There- fore, you must ensure that a product order is written to a vendor when that product’s inventory drops below its minimum allowable quantity on hand. Better yet, how about ensuring that the task is completed automatically? To automate product ordering, you first must make sure the product’s quantity on hand reflects an up-to-date and consistent value. After the appropriate product availabil- ity requirements have been set, two key issues must be addressed: 1. Business logic requires an update of the product quantity on hand each time there is a sale of that product. 2. If the product’s quantity on hand falls below its minimum allowable inventory level, the product must be reordered. To accomplish these two tasks, you could write multiple SQL statements: one to update the product quantity on hand and another to update the product reorder flag. Next, you would have to run each statement in the correct order each time there was a new sale. Such a multistage process would be inefficient because a series of SQL statements must be written and executed each time a product is sold. Even worse, this SQL environment requires that someone must remember to perform the SQL tasks. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. A trigger is procedural SQL code that is automatically invoked by the RDBMS upon the occurrence of a given data manipulation event. It is useful to remember that: • A trigger is invoked before or after a data row is inserted, updated, or deleted. • A trigger is associated with a database table. • Each database table may have one or more triggers. • A trigger is executed as part of the transaction that triggered it. Triggers are critical to proper database operation and management. For example: • Triggers can be used to enforce constraints that cannot be enforced at the DBMS design and implementation levels. • Triggers add functionality by automating critical actions and providing appropriate warnings and suggestions for remedial action. In fact, one of the most common uses for triggers is to facilitate the enforcement of referential integrity. • Triggers can be used to update table values, insert records in tables, and call other stored procedures. Triggers play a critical role in making the database truly useful; they also add processing power to the RDBMS and to the database system as a whole. Oracle recommends triggers for: • Auditing purposes (creating audit logs) • Automatic generation of derived column values • Enforcement of business or security constraints • Creation of replica tables for backup purposes To see how a trigger is created and used, examine a simple inventory management problem. For example, if a product’s quantity on hand is updated when the product is sold, the system should automatically check whether the quantity on hand falls below its minimum allowable quantity. To demonstrate that process, use the PRODUCT table in Figure 8.31. Note the use of the minimum order quantity (P_MIN_ORDER) and product reorder flag (P_REORDER) columns. The P_MIN_ORDER indicates the minimum quantity for restocking an order. The P_REORDER column is a numeric field that indicates whether the product needs to be reordered (1 = Yes, 0 = No). The initial P_REORDER values are set to 0 (No) to serve as the basis for the initial trigger development. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 8 Advanced SQL 393 trigger A procedural SQL code that is automatically invoked by the relational database management system when a data manipulation event occurs. FIGURE 8.31 THE PRODUCT TABLE 394 Part 3 Advanced Design and Implementation Online Content Oracle users can run the PRODLIST.SQL script file to format the output of the PRO- DUCT table shown in Figure 8.31. The script file is available at www. cengagebrain.com. Given the PRODUCT table listing shown in Figure 8.31, create a trigger to evaluate the product’s quantity on hand, P_QOH. If the quantity on hand is below the minimum quantity shown in P_MIN, the trigger will set the P_REORDER column to 1, which represents “Yes.” The syntax to create a trigger in Oracle is as follows: CREATE OR REPLACE TRIGGER trigger_name [BEFORE / AFTER] [DELETE / INSERT / UPDATE OF column_name] ON table_name [FOR EACH ROW] [DECLARE] [variable_namedata type[:=initial_value] ] BEGIN PL/SQL instructions; ... END; As you can see, a trigger definition contains the following parts: • The triggering timing: BEFORE or AFTER. This timing indicates when the trigger’s PL/ SQL code executes—in this case, before or after the triggering statement is completed. • The triggering event: The statement that causes the trigger to execute (INSERT, UPDATE, or DELETE). – The triggering level: The two types of triggers are statement-level triggers and row- level triggers.A statement-level trigger is assumed if you omit the FOR EACH ROW keywords. This type of trigger is executed once, before or after the trigger- ing statement is completed. This is the default case. – A row-level trigger requires use of the FOR EACH ROW keywords. This type of trigger is executed once for each row affected by the triggering statement. (In other words, if you update 10 rows, the trigger executes 10 times.) • The triggering action: The PL/SQL code enclosed between the BEGIN and END keywords. Each statement inside the PL/SQL code must end with a semicolon ( ; ). statement-level trigger A SQL trigger that is assumed if the FOR EACH ROW keywords are omitted. This type of trigger is executed once, before or after the triggering statement completes, and is the default case. row-level trigger A trigger that is executed once for each row affected by the triggering SQL statement. A row-level trigger requires the use of the FOR EACH ROW keywords in the trigger declaration. Note Oracle and MS SQL Server allow a trigger to include multiple triggering conditions; that is, any combination of INSERT, UPDATE, and/or DELETE. MySQL allows only one triggering condition per trigger. Therefore, if a certain set of actions should be taken in the case of multiple events, for example, during an UPDATE or an INSERT, then two separate triggers are required in MySQL. To reduce having duplicate code in both triggers, it is a common practice to create a stored procedure that performs the common actions, then have both triggers call the same stored procedure. Previously, Access did not support triggers for tables. However, starting with Access 2013, “Table Events” have been added that provide trigger functionality. A table can have events before and/or after rows are inserted, updated, or deleted. In the PRODUCT table’s case, you will create a statement-level trigger that is implicitly executed AFTER an UPDATE of the P_QOH attribute for an existing row or AFTER an INSERT of a new row in the PRODUCT table. The trigger action executes an UPDATE statement that compares the P_QOH with the P_MIN column. If the value of P_QOH is equal to or less than P_MIN, the trigger updates the P_REORDER to 1. To create the trigger, Oracle’s SQL*Plus will be used. The trigger code is shown in Figure 8.32. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. To test the TRG_PRODUCT_REORDER trigger, update the quantity on hand of product '11QER/31' to 4. After the UPDATE completes, the trigger is automatically fired and the UPDATE statement inside the trigger code sets the P_REORDER to 1 for all products that are below the minimum. (See Figure 8.33.) Chapter 8 Advanced SQL 395 Online Content The source code for the stored procedures in this section is available at www.cengagebrain.com. FIGURE8.32 CREATINGTHETRG_PRODUCT_REORDERTRIGGER FIGURE8.33 VERIFYINGTHETRG_PRODUCT_REORDERTRIGGEREXECUTION The trigger shown in Figure 8.33 seems to work, but what happens if you reduce the minimum quantity of product '2232/QWE'? Figure 8.34 shows that when you update the minimum quantity, the quantity on hand of the product '2232/QWE' falls below the new minimum, but the reorder flag is still 0. Why? The answer is simple: you updated the P_MIN column, but the trigger is never executed. TRG_PRODUCT_ REORDER executes only after an update of the P_QOH column! To avoid that inconsistency, you must modify the trigger event to exe- cute after an update of the P_MIN field, too. The updated trigger code is shown in Figure 8.35. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 396 Part 3 Advanced Design and Implementation FIGURE 8.34 THE P_REORDER VALUE MISMATCH AFTER UPDATE OF THE P_MIN ATTRIBUTE FIGURE8.35 SECONDVERSIONOFTHETRG_PRODUCT_REORDERTRIGGER To test this new trigger version, change the minimum quantity for product '23114- AA' to 10. After that update, the trigger makes sure that the reorder flag is properly set for all of the products in the PRODUCT table. (See Figure 8.36.) This second version of the trigger seems to work well, but nothing happens if you change the P_QOH value for product '11QER/31', as shown in Figure 8.37! (Note that the reorder flag is still set to 1.) Why didn’t the trigger change the reorder flag to 0? The answer is that the trigger does not consider all possible cases. Examine the second version of the TRG_PRODUCT_REORDER trigger code (Figure 8.35) in more detail: • The trigger fires after the triggering statement is completed. Therefore, the DBMS always executes two statements (INSERT plus UPDATE or UPDATE plus UPDATE). That is, after you update P_MIN or P_QOH or you insert a new row in the PRODUCT table, the trigger executes another UPDATE statement automatically. • The triggering action performs an UPDATE of all the rows in the PRODUCT table, even if the triggering statement updates just one row! This can affect the performance of Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 8 Advanced SQL 397 FIGURE8.36 SUCCESSFULTRIGGEREXECUTIONAFTERTHEP_MINVALUEISUPDATED FIGURE 8.37 THE P_REORDER VALUE MISMATCH AFTER INCREASING THE P_QOH VALUE the database. Imagine what will happen if you have a PRODUCT table with 519,128 rows and you insert just one product. The trigger will update all 519,129 rows, including the rows that do not need an update! • The trigger sets the P_REORDER value only to 1; it does not reset the value to 0, even if such an action is clearly required when the inventory level is back to a value greater than the minimum value. In short, the second version of the TRG_PRODUCT_REORDER trigger still does not complete all of the necessary steps. Now modify the trigger to handle all update scenarios, as shown in Figure 8.38. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 398 Part 3 Advanced Design and Implementation FIGURE8.38 THETHIRDVERSIONOFTHETRG_PRODUCT_REORDERTRIGGER The trigger in Figure 8.38 sports several new features: • The trigger is executed before the actual triggering statement is completed. In Figure 8.38, the triggering timing is defined in line 2, BEFORE INSERT OR UPDATE. This clearly indicates that the triggering statement is executed before the INSERT or UPDATE completes, unlike the previous trigger examples. • The trigger is a row-level trigger instead of a statement-level trigger. The FOR EACH ROW keywords make the trigger a row-level trigger. Therefore, this trigger executes once for each row affected by the triggering statement. • The trigger action uses the :NEW attribute reference to change the value of the P_REORDER attribute. The use of the :NEW attribute references deserves a more detailed explanation. To understand its use, you must first consider a basic computing tenet: all changes are done first in primary memory, then transferred to permanent memory. In other words, the com- puter cannot change anything directly in permanent storage (on disk). It must first read the data from permanent storage to primary memory, then make the change in primary memory, and finally write the changed data back to permanent memory (on disk). The DBMS operates in the same way, with one addition. Because ensuring data integrity is critical, the DBMS makes two copies of every row being changed by a DML (INSERT, UPDATE, or DELETE) statement. You will learn more about this in Chapter 10, Transaction Management and Concurrency Control. The first copy contains the original (“old”) values of the attributes before the changes. The second copy contains the changed (“new”) values of the attributes that will be permanently saved to the database after any changes made by an INSERT, UPDATE, or DELETE. You can use :OLD to refer to the original values; you can use :NEW to refer to the changed values (the values that will be stored in the table). You can use :NEW and :OLD attribute references only within the PL/SQL code of a database trigger action. For example: • IF :NEW.P_QOH < = :NEW.P_MIN compares the quantity on hand with the mini- mum quantity of a product. Remember that this is a row-level trigger. Therefore, this comparison is made for each row that is updated by the triggering statement. • Although the trigger is a BEFORE trigger, this does not mean that the triggering state- ment has not executed yet. To the contrary, the triggering statement has already taken place; otherwise, the trigger would not have fired and the :NEW values would not exist. Remember, BEFORE means before the changes are permanently saved to disk, but after the changes are made in memory. • The trigger uses the :NEW reference to assign a value to the P_REORDER column before the UPDATE or INSERT results are permanently stored in the table. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. The assignment is always made to the :NEW value (never to the :OLD value), and the assignment always uses the := assignment operator. The :OLD values are read-only values; you cannot change them. Note that :NEW.P_REORDER := 1; assigns the value 1 to the P_REORDER column and :NEW.P_REORDER := 0; assigns the value 0 to the P_REORDER column. • This new trigger version does not use any DML statements! Before testing the new trigger, note that product '11QER/31' currently has a quantity on hand that is above the minimum quantity, yet the reorder flag is set to 1. Given that condition, the reorder flag must be 0. After creating the new trigger, you can execute an UPDATE statement to fire it, as shown in Figure 8.39. Note the following important features of the code in Figure 8.39: • The trigger is automatically invoked for each affected row—in this case, all rows of the PRODUCT table. If your triggering statement would have affected only three rows, not all PRODUCT rows would have the correct P_REORDER value set, which is why the triggering statement was set up as shown in Figure 8.38. • The trigger will run only if you insert a new product row or update P_QOH or P_MIN. If you update any other attribute, the trigger will not run. You can also use a trigger to update an attribute in a table other than the one being modified. For example, suppose that you would like to create a trigger that automatically reduces the quantity on hand of a product with every sale. To accomplish that task, you must create a trigger for the LINE table that updates a row in the PRODUCT table. The sample code for that trigger is shown in Figure 8.40. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 8 Advanced SQL 399 FIGURE8.39 EXECUTIONOFTHETHIRDTRIGGERVERSION 400 Part 3 Advanced Design and Implementation FIGURE8.40 TRG_LINE_PRODTRIGGERTOUPDATETHEPRODUCT QUANTITY ON HAND Note that the TRG_LINE_PROD row-level trigger executes after inserting a new invoice’s LINE and reduces the quantity on hand of the recently sold product by the num- ber of units sold. This row-level trigger updates a row in a different table (PRODUCT), using the :NEW values of the recently added LINE row. A third trigger example shows the use of variables within a trigger. In this case, you want to update the customer balance (CUS_BALANCE) in the CUSTOMER table after inserting every new LINE row. This trigger code is shown in Figure 8.41. FIGURE8.41 TRG_LINE_CUSTRIGGERTOUPDATETHECUSTOMERBALANCE Carefully examine the trigger in Figure 8.41. • The trigger is a row-level trigger that executes after each new LINE row is inserted. • The DECLARE section in the trigger is used to declare any variables used inside the trigger code. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. • You can declare a variable by assigning a name, a data type, and (optionally) an initial value, as in the case of the W_TOT variable. • The first step in the trigger code is to get the customer code (CUS_CODE) from the related INVOICE table. Note that the SELECT statement returns only one attribute (CUS_CODE) from the INVOICE table. Also note that the attribute returns only one value as specified by the use of the WHERE clause, to restrict the query output to a single value. • Note the use of the INTO clause within the SELECT statement. You use the INTO clause to assign a value from a SELECT statement to a variable (W_CUS) used within a trigger. • The second step in the trigger code computes the total of the line by multiplying :NEW. LINE_UNITS by :NEW.LINE_PRICE and assigning the result to the W_TOT variable. • The final step updates the customer balance by using an UPDATE statement and the W_TOT and W_CUS trigger variables. • Double dashes (--) are used to indicate comments within the PL/SQL block. To summarize the triggers created in this section: • TRG_PRODUCT_REORDER is a row-level trigger that updates P_REORDER in PROD- UCT when a new product is added or when the P_QOH or P_MIN columns are updated. • TRG_LINE_PROD is a row-level trigger that automatically reduces the P_QOH in PRODUCT when a new row is added to the LINE table. • TRG_LINE_CUS is a row-level trigger that automatically increases the CUS_ BALANCE in CUSTOMER when a new row is added in the LINE table. The use of triggers facilitates the automation of multiple data management tasks. Although triggers are independent objects, they are associated with database tables. When you delete a table, all its trigger objects are deleted with it. However, if you needed to delete a trigger without deleting the table, you could use the following command: DROP TRIGGER trigger_name Trigger Action Based on Conditional DML Predicates You could also create triggers whose actions depend on the type of DML statement (INSERT, UPDATE, or DELETE) that fires the trigger. For example, you could create a trigger that executes after an INSERT, an UPDATE, or a DELETE on the PRODUCT table. But how do you know which one of the three statements caused the trigger to execute? In those cases, you could use the following syntax: IF INSERTING THEN ... END IF; IF UPDATING THEN ... END IF; IF DELETING THEN ... END IF; 8-7b Stored Procedures A stored procedure is a named collection of procedural and SQL statements. Just like database triggers, stored procedures are stored in the database. One of the major advan- tages of stored procedures is that they can be used to encapsulate and represent business transactions. For example, you can create a stored procedure to represent a product sale, a credit update, or the addition of a new customer. By doing that, you can encapsulate SQL statements within a single stored procedure and execute them as a single transac- tion. There are two clear advantages to the use of stored procedures: • Stored procedures substantially reduce network traffic and increase performance. Because the procedure is stored at the server, there is no transmission of individual stored procedure (1) A named collection of procedural and SQL statements. (2) Business logic stored on a server in the form of SQL code or another DBMS- specific procedural language. Chapter 8 Advanced SQL 401 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 402 Part 3 Advanced Design and Implementation SQL statements over the network. The use of stored procedures improves system performance because all transactions are executed locally on the RDBMS, so each SQL statement does not have to travel over the network. • Stored procedures help reduce code duplication by means of code isolation and code sharing (creating unique PL/SQL modules that are called by application programs), thereby minimizing the chance of errors and the cost of application development and maintenance. To create a stored procedure, you use the following syntax: CREATE OR REPLACE PROCEDURE procedure_name [(argument [IN/OUT] data-type, ... )] [IS/AS] [variable_namedata type[:=initial_value] ] PL/SQL or SQL statements; ... END; Note the following important points about stored procedures and their syntax: • argument specifies the parameters that are passed to the stored procedure. A stored procedure could have zero or more arguments or parameters. • IN/OUT indicates whether the parameter is for input, output, or both. • data-type is one of the procedural SQL data types used in the RDBMS. The data types normally match those used in the RDBMS table creation statement. • Variables can be declared between the keywords IS and BEGIN. You must specify the variable name, its data type, and (optionally) an initial value. To illustrate stored procedures, assume that you want to create a procedure (PRC_PROD_DISCOUNT) to assign an additional 5 percent discount for all products when the quantity on hand is more than or equal to twice the minimum quantity. Figure 8.42 shows how the stored procedure is created. Online Content The source code for the triggers in this section is available at www.cengagebrain. com. BEGIN FIGURE 8.42 CREATING THE PRC_PROD_DISCOUNT STORED PROCEDURE Note in Figure 8.42 that the PRC_PROD_DISCOUNT stored procedure uses the DBMS_OUTPUT.PUT_LINE function to display a message when the procedure executes. (This action assumes that you previously ran SET SERVEROUTPUT ON.) Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. To execute the stored procedure, you must use the following syntax: EXEC procedure_name[(parameter_list)]; For example, to see the results of running the PRC_PROD_DISCOUNT stored procedure, you can use the EXEC PRC_PROD_DISCOUNT command shown in Figure 8.43. Using Figure 8.43 as your guide, you can see how the product discount attribute was increased by 5 percent for all products with a quantity on hand that was more than or equal to twice the minimum quantity. (Compare the first PRODUCT table listing to the second PRODUCT table listing.) One of the main advantages of procedures is that you can pass values to them. For example, the previous PRC_PROD_DISCOUNT procedure worked well, but what if you want to make the percentage increase an input variable? In that case, you can pass an argument to represent the rate of increase to the procedure. Figure 8.44 shows the code for that procedure. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 8 Advanced SQL 403 FIGURE8.43 RESULTSOFTHEPRC_PROD_DISCOUNTSTOREDPROCEDURE 404 Part 3 Advanced Design and Implementation FIGURE8.44 SECONDVERSIONOFTHEPRC_PROD_DISCOUNTSTOREDPROCEDURE Figure 8.45 shows the execution of the second version of the PRC_PROD_ DISCOUNT stored procedure. Note that if the procedure requires arguments, they must be enclosed in parentheses and separated by commas. FIGURE8.45 RESULTSOFTHESECONDVERSIONOFTHEPRC_PROD_ DISCOUNT STORED PROCEDURE Stored procedures are also useful to encapsulate shared code to represent business transactions. For example, you can create a simple stored procedure to add a new cus- tomer. By using a stored procedure, all programs can call it by name each time a new customer is added. Naturally, if new customer attributes are added later, you will need to modify the stored procedure. However, the programs that use the stored procedure will not need to know the name of the newly added attribute; they will need to add only a new parameter to the procedure call. (Notice the PRC_CUS_ADD stored procedure shown in Figure 8.46.) As you examine Figure 8.46, note these features: • The PRC_CUS_ADD procedure uses several parameters, one for each required attribute in the CUSTOMER table. • The stored procedure uses the CUS_CODE_SEQ sequence to generate a new customer code. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 8 Advanced SQL 405 FIGURE 8.46 THE PRC_CUS_ADD STORED PROCEDURE • The required parameters—those specified in the table definition—must be included and can be null only when the table specifications permit nulls for that parameter. For example, note that the second customer addition was unsuccessful because the CUS_AREACODE is a required attribute and cannot be null. • The procedure displays a message in the SQL*Plus console to let the user know that the customer was added. The next two examples further illustrate the use of sequences within stored procedures. In this case, create two stored procedures: 1. The PRC_INV_ADD procedure adds a new invoice. 2. The PRC_LINE_ADD procedure adds a new product line row for a given invoice. Both procedures are shown in Figure 8.47. Note the use of a variable in the PRC_LINE_ADD procedure to get the product price from the PRODUCT table. To test the procedures shown in Figure 8.47: 1. Call the PRC_INV_ADD procedure with the new invoice data as arguments. 2. Call the PRC_LINE_ADD procedure and pass the product line arguments. That process is illustrated in Figure 8.48. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 406 Part 3 Advanced Design and Implementation FIGURE 8.47 THE PRC_INV_ADD AND PRC_LINE_ADD STORED PROCEDURES FIGURE 8.48 TESTING THE PRC_INV_ADD AND PRC_LINE_ADD PROCEDURES Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 8-7c PL/SQL Processing with Cursors Until now, all of the SQL statements you have used inside a PL/SQL block (trigger or stored procedure) have returned a single value. If the SQL statement returns more than one value, you will generate an error. If you want to use a SQL statement that returns more than one value inside your PL/SQL code, you need to use a cursor. A cursor is a special construct used in procedural SQL to hold the data rows returned by a SQL query. You can think of a cursor as a reserved area of memory in which the output of the query is stored, like an array holding columns and rows. Cursors are held in a reserved memory area in the DBMS server, not in the client computer. There are two types of cursors: implicit and explicit. An implicit cursor is automati- cally created in procedural SQL when the SQL statement returns only one value. Up to this point, all of the examples created an implicit cursor. An explicit cursor is created to hold the output of a SQL statement that may return two or more rows (but could return zero rows or only one). To create an explicit cursor, you use the following syntax inside a PL/SQL DECLARE section: CURSOR cursor_name IS select-query; Once you have declared a cursor, you can use specific PL/SQL cursor processing commands (OPEN, FETCH, and CLOSE) anywhere between the BEGIN and END keywords of the PL/SQL block. Table 8.10 summarizes the main use of each command. Cursor-style processing involves retrieving data from the cursor one row at a time. Once you open a cursor, it becomes an active data set. That data set contains a “current” row pointer. Therefore, after opening a cursor, the current row is the first row of the cursor. When you fetch a row from the cursor, the data from the “current” row in the cursor is copied to the PL/SQL variables. After the fetch, the “current” row pointer moves to the next row in the set and continues until it reaches the end of the cursor. How do you know what number of rows are in the cursor? Or how do you know when you have reached the end of the cursor data set? You know because cursors have special attributes that convey important information. Table 8.11 summarizes the cursor attributes. TABLE 8.10 CURSOR PROCESSING COMMANDS CURSOR COMMAND EXPLANATION Chapter 8 Advanced SQL 407 cursor A special construct used in procedural SQL to hold the data rows returned by a SQL query. A cursor may be considered a reserved area of memory in which query output is stored, like an array holding columns and rows. Cursors are held in a reserved memory area in the DBMS server, not in the client computer. implicit cursor A cursor that is automatically created in procedural SQL when the SQL statement returns only one row. explicit cursor In procedural SQL, a cursor created to hold the output of a SQL statement that may return two or more rows, but could return zero or only one row. OPEN Opening the cursor executes the SQL command and populates the cursor with data, opening the cursor for processing. The cursor declaration command only reserves a named memory area for the cursor; it does not populate the cursor with the data. Before you can use a cursor, you need to open it. For example: OPEN cursor_name FETCH Once the cursor is opened, you can use the FETCH command to retrieve data from the cursor and copy it to the PL/SQL variables for processing. The syntax is: FETCH cursor_name INTO variable1 [, variable2, ...] The PL/SQL variables used to hold the data must be declared in the DECLARE section and must have data types compatible with the columns retrieved by the SQL command. If the cursor’s SQL statement returns five columns, there must be five PL/SQL variables to receive the data from the cursor. This type of processing resembles the one-record-at-a-time processing used in previous database models. The first time you fetch a row from the cursor, the first row of data from the cursor is copied to the PL/SQL variables; the second time you fetch a row from the cursor, the second row of data is placed in the PL/SQL variables; and so on. CLOSE The CLOSE command closes the cursor for processing. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 408 Part 3 Advanced Design and Implementation TABLE 8.11 CURSOR ATTRIBUTES ATTRIBUTE DESCRIPTION %ROWCOUNT Returns the number of rows fetched so far. If the cursor is not OPEN, it returns an error. If no FETCH has been done but the cursor is OPEN, it returns 0. %FOUND Returns TRUE if the last FETCH returned a row, and FALSE if not. If the cursor is not OPEN, it returns an error. If no FETCH has been done, it contains NULL. %NOTFOUND Returns TRUE if the last FETCH did not return any row, and FALSE if it did. If the cursor is not OPEN, it returns an error. If no FETCH has been done, it contains NULL. %ISOPEN Returns TRUE if the cursor is open (ready for processing) or FALSE if the cursor is closed. Remember, before you can use a cursor, you must open it. To illustrate the use of cursors, use a simple stored procedure example to list the products that have a greater quantity on hand than the average quantity on hand for all products. The code is shown in Figure 8.49. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE8.49 ASIMPLEPRC_CURSOR_EXAMPLE As you examine the stored procedure code shown in Figure 8.49, note the following important characteristics: • Lines 2 and 3 use the %TYPE data type in the variable definition section. As indi- cated in Table 8.9, the %TYPE data type indicates that the given variable inherits the data type from a previously declared variable or from an attribute of a data- base table. In this case, you are using the %TYPE to indicate that the W_P_CODE and W_P_DESCRIPT will have the same data type as the respective columns in the PRODUCT table. This way, you ensure that the PL/SQL variable will have a compatible data type. • Line 5 declares the PROD_CURSOR cursor. • Line 12 opens the PROD_CURSOR cursor and populates it. • Line 13 uses the LOOP statement to loop through the data in the cursor, fetching one row at a time. • Line 14 uses the FETCH command to retrieve a row from the cursor and place it in the respective PL/SQL variables. • Line 15 uses the EXIT command to evaluate when there are no more rows in the cursor (using the %NOTFOUND cursor attribute) and to exit the loop. • Line 19 uses the %ROWCOUNT cursor attribute to obtain the total number of rows processed. • Line 21 issues the CLOSE PROD_CURSOR command to close the cursor. The use of cursors, combined with standard SQL, makes working with relational databases very desirable because programmers can work in the best of both worlds: set-oriented processing and record-oriented processing. Any experienced programmer knows to use the tool that best fits the job. Sometimes you will be better off manipulating data in a set-oriented environment; at other times, it might be better to use a record- oriented environment. Procedural SQL lets you have your proverbial cake and eat it too. Procedural SQL provides functionality that enhances the capabilities of the DBMS while maintaining a high degree of manageability. 8-7d PL/SQL Stored Functions Using programmable or procedural SQL, you can also create your own stored functions. Stored procedures and functions are very similar. A stored function is basically a named group of procedural and SQL statements that returns a value, as indicated by a RETURN statement in its program code. To create a function, you use the following syntax: CREATE FUNCTION function_name (argument IN data-type, ... ) RETURN data-type [IS] BEGIN PL/SQL statements; ... RETURN (value or expression); END; Stored functions can be invoked only from within stored procedures or triggers, and cannot be invoked from SQL statements unless the function follows some very specific compliance rules. Remember not to confuse built-in SQL functions (such as MIN, MAX, and AVG) with stored functions. stored function A named group of procedural and SQL statements that returns a value, as indicated by a RETURN statement in its program code. Chapter 8 Advanced SQL 409 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 410 Part 3 Advanced Design and Implementation 8-8 Embedded SQL There is little doubt that SQL’s popularity as a data manipulation language is due in part to its ease of use and its powerful data-retrieval capabilities. In the real world, however, database systems are related to other systems and programs, and you still need a conventional programming language such as Visual Basic .NET, C#, or COBOL to integrate database systems with other programs and systems. If you are developing web applications, you are most likely familiar with Visual Studio .NET, Java, ASP, or ColdFusion. Yet, almost regardless of the programming tools you use, if your web appli- cation or Windows-based GUI system requires access to a database such as MS Access, SQL Server, Oracle, or DB2, you will likely need to use SQL to manipulate the data in the database. Embedded SQL is a term used to refer to SQL statements contained within an appli- cation programming language such as Visual Basic .NET, C#, COBOL, or Java. The program being developed might be a standard binary executable in Windows or Linux, or it might be a web application designed to run over the Internet. No matter what lan- guage you use, if it contains embedded SQL statements, it is called the host language. Embedded SQL is still the most common approach to maintaining procedural capa- bilities in DBMS-based applications. However, mixing SQL with procedural languages requires that you understand some key differences between the two. • Run-time mismatch. Remember that SQL is a nonprocedural, interpreted language; that is, each instruction is parsed, its syntax is checked, and it is executed one instruc- tion at a time. (The authors are particularly grateful for the thoughtful comments provided by Emil T. Cipolla.) All of the processing takes place at the server side. Meanwhile, the host language is generally a binary-executable program (also known as a compiled program). The host program typically runs at the client side in its own memory space, which is different from the DBMS environment. • Processing mismatch. Conventional programming languages (COBOL, ADA, FORTRAN, Pascal, C++, and PL/I) process one data element at a time. Although you can use arrays to hold data, you still process the array elements one row at a time. This is especially true for file manipulation, where the host language typically manipulates data one record at a time. However, newer programming environments such as Visual Studio .NET have adopted several object-oriented extensions that help the program- mer manipulate data sets in a cohesive manner. • Data type mismatch. SQL provides several data types, but some of them might not match data types used in different host languages (for example, the DATE and VARCHAR2 data types). To bridge the differences, the embedded SQL standard defines a framework to integrate SQL within several programming languages. The embedded SQL framework defines the following: • A standard syntax to identify embedded SQL code within the host language (EXEC SQL/END-EXEC). • A standard syntax to identify host variables, which are variables in the host language that receive data from the database (through the embedded SQL code) and process the data in the host language. All host variables are preceded by a colon ( : ). • A communication area used to exchange status and error information between SQL and the host language. This communication area contains two variables—SQLCODE and SQLSTATE. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. embedded SQL SQL statements contained within application programming languages such as COBOL, C++, ASP, Java, and ColdFusion. host language Any language that contains embedded SQL statements. Copyright 2017 Cengage Lea Editorial review has deemed that Another way to interface host languages and SQL is through the use of a call-level interface (CLI), in which the programmer writes to an application programming inter- face (API). A common CLI in Windows is provided by the Open Database Connectivity (ODBC) interface. Before continuing, you should explore the process required to create and run an executable program with embedded SQL statements. If you have ever programmed in COBOL or C++, you are familiar with the multiple steps required to generate the final executable program. Although the specific details vary among language and DBMS ven- dors, the following general steps are standard: 1. The programmer writes embedded SQL code within the host language instructions. The code follows the standard syntax required for the host language and embedded SQL. 2. A preprocessor is used to transform the embedded SQL into specialized procedure calls that are DBMS- and language-specific. The preprocessor is provided by the DBMS vendor and is specific to the host language. 3. The program is compiled using the host language compiler. The compiler creates an object code module for the program containing the DBMS procedure calls. 4. The object code is linked to the respective library modules and generates the execut- able program. This process binds the DBMS procedure calls to the DBMS run-time libraries. Additionally, the binding process typically creates an “access plan” module that contains instructions to run the embedded code at run time. 5. The executable is run, and the embedded SQL statement retrieves data from the database. Note that you can embed individual SQL statements or even an entire PL/SQL block. Up to this point in the book, you have used a DBMS-provided application (SQL*Plus) to write SQL statements and PL/SQL blocks in an interpretive mode to address one-time or ad hoc data requests. However, it is extremely difficult and awkward to use ad hoc queries to process transactions inside a host language. Programmers typically embed SQL statements within a host language that is compiled once and executed as often as needed. To embed SQL into a host language, follow this syntax: EXEC SQL SQL statement; END-EXEC. The preceding syntax will work for SELECT, INSERT, UPDATE, and DELETE state- ments. For example, the following embedded SQL code will delete employee 109, George Smith, from the EMPLOYEE table: EXEC SQL DELETE FROM EMPLOYEE WHERE EMP_NUM = 109; END-EXEC. Remember, the preceding embedded SQL statement is compiled to generate an execut- able statement. Therefore, the statement is fixed permanently and cannot change (unless, of course, the programmer changes it). Each time the program runs, it deletes the same row. In short, the preceding code is good only for the first run; all subsequent runs will likely generate an error. Clearly, this code would be more useful if you could specify a variable to indicate the employee number to be deleted. Online Content Additional coverage of CLIs and ODBC is avail- able in Appendix F, Client/Server Systems, and Appendix J, Web Database Development with ColdFusion, at www. cengagebrain.com. Chapter 8 Advanced SQL 411 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 412 Part 3 Advanced Design and Implementation TABLE 8.12 In embedded SQL, all host variables are preceded by a colon ( : ). The host variables may be used to send data from the host language to the embedded SQL, or they may be used to receive the data from the embedded SQL. To use a host variable, you must first declare it in the host language. Common practice is to use similar host variable names as the SQL source attributes. For example, if you are using COBOL, you would define the host variables in the Working Storage section. Then you would refer to them in the embedded SQL section by preceding them with a colon. For example, to delete an employee whose employee number is represented by the host variable W_EMP_NUM, you would write the following code: EXEC SQL DELETE FROM EMPLOYEE WHERE EMP_NUM = :W_EMP_NUM; END-EXEC. At run time, the host variable value will be used to execute the embedded SQL statement. What happens if the employee you are trying to delete does not exist in the database? How do you know that the statement has been completed without errors? As mentioned previously, the embedded SQL standard defines a SQL communication area to hold sta- tus and error information. In COBOL, such an area is known as the SQLCA area and is defined in the Data Division as follows: EXEC SQL INCLUDE SQLCA END-EXEC. The SQLCA area contains two variables for status and error reporting. Table 8.12 shows some of the main values returned by the variables and their meaning. SQL STATUS AND ERROR REPORTING VARIABLES VARIABLE NAME VALUE EXPLANATION SQLCODE Old-style error reporting supported for backward compatibility only; returns an integer value (positive or negative) 0 Successful completion of command 100 No data; the SQL statement did not return any rows and did not select, update, or delete any rows −999 Any negative value indicates that an error occurred SQLSTATE Added by SQL-92 standard to provide predefined error codes; defined as a character string (5 characters long) 00000 Successful completion of command Multiple values in the format XXYYY where: XX-> represents the class code
YYY-> represents the subclass code
The following embedded SQL code illustrates the use of the SQLCODE within a COBOL program.
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EXEC SQL
SELECT EMP_LNAME, EMP_LNAME INTO :W_EMP_FNAME, :W_EMP_LNAME WHERE EMP_NUM = :W_EMP_NUM;
END-EXEC.
IF SQLCODE = 0 THEN
PERFORM DATA_ROUTINE ELSE
PERFORM ERROR_ROUTINE END-IF.
In this example, the SQLCODE host variable is checked to determine whether the query completed successfully. If it did, the DATA_ROUTINE is performed; otherwise, the ERROR_ROUTINE is performed.
Just as with PL/SQL, embedded SQL requires the use of cursors to hold data from a query that returns more than one value. If COBOL is used, the cursor can be declared either in the Working Storage section or in the Procedure Division. The cursor must be declared and processed, as you learned earlier in Section 8-7c. To declare a cursor, you use the syntax shown in the following example:
EXEC SQL
DECLARE PROD_CURSOR FOR
SELECT P_CODE, P_DESCRIPT FROM PRODUCT
WHERE P_QOH > (SELECT AVG(P_QOH) FROM PRODUCT);
END-EXEC.
Next, you must open the cursor to make it ready for processing:
EXEC SQL
OPEN PROD_CURSOR;
END-EXEC.
To process the data rows in the cursor, you use the FETCH command to retrieve one row of data at a time and place the values in the host variables. The SQLCODE must be checked to ensure that the FETCH command completed successfully. This section of code typically constitutes part of a routine in the COBOL program. Such a routine is executed with the PERFORM command. For example:
EXEC SQL
FETCH PROD_CURSOR INTO :W_P_CODE, :W_P_DESCRIPT;
END-EXEC.
IF SQLCODE = 0 THEN
PERFORM DATA_ROUTINE ELSE
PERFORM ERROR_ROUTINE END-IF.
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When all rows have been processed, you close the cursor as follows:
EXEC SQL
CLOSE PROD_CURSOR;
END-EXEC.
Thus far, you have seen examples of embedded SQL in which the programmer used predefined SQL statements and parameters. Therefore, the end users of the programs are limited to the actions that were specified in the application programs. That style of embedded SQL is known as static SQL, meaning that the SQL statements will not change while the application is running. For example, the SQL statement might read like this:
SELECT P_CODE, P_DESCRIPT, P_QOH, P_PRICE FROM PRODUCT
WHERE P_PRICE > 100;
Note that the attributes, tables, and conditions are known in the preceding SQL statement. Unfortunately, end users seldom work in a static environment. They are more likely to require the flexibility of defining their data access requirements on the fly. Therefore, the end user requires that SQL be as dynamic as the data access requirements.
Dynamic SQL is a term used to describe an environment in which the SQL statement is not known in advance; instead, the SQL statement is generated at run time. At run time in a dynamic SQL environment, a program can generate the SQL statements that are required to respond to ad hoc queries. In such an environment, neither the program- mer nor the end user is likely to know precisely what kind of queries will be generated or how they will be structured. For example, a dynamic SQL equivalent of the preceding example could be:
SELECT FROM WHERE
:W_ATTRIBUTE_LIST :W_TABLE :W_CONDITION;
Note that the attribute list and the condition are not known until the end user specifies them. W_TABLE, W_ATTRIBUTE_LIST, and W_CONDITION are text variables that contain the end-user input values used in the query generation. Because the program uses the end-user input to build the text variables, the end user can run the same program multiple times to generate varying outputs. For example, in one instance, the end user might want to know which products cost less than $100; in another case, the end user might want to know how many units of a given product are available for sale at any given moment.
Although dynamic SQL is clearly flexible, such flexibility carries a price. Dynamic SQL tends to be much slower than static SQL. Dynamic SQL also requires more computer resources (overhead). Finally, you are more likely to find inconsistent levels of support and incompatibilities among DBMS vendors.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
static SQL
A style of embedded SQL in which the SQL statements do not change while the application is running.
dynamic SQL
An environment
in which the SQL statement is not known in advance, but instead is generated at run time. In a dynamic SQL environment, a program can generate the SQL statements that are required to respond to ad hoc queries.
Copyright 2017 Cengage Lea Editorial review has deemed that

• Operations that join tables can be classified as inner joins and outer joins. An inner join is the traditional join in which only rows that meet a given criterion are selected. An outer join returns the matching rows as well as the rows with unmatched attribute values for one table or both tables to be joined.
• A natural join returns all rows with matching values in the matching columns and eliminates duplicate columns. This style of query is used when the tables share a com- mon attribute with a common name. One important difference between the syntax for a natural join and for the old-style join is that the natural join does not require the use of a table qualifier for the common attributes. In practice, natural joins are often discouraged because the common attribute is not specified within the command, making queries more difficult to understand and maintain.
• Joins may use keywords such as USING and ON. If the USING clause is used, the query will return only the rows with matching values in the column indicated in the USING clause; that column must exist in both tables. If the ON clause is used, the query will return only the rows that meet the specified join condition.
• Subqueries and correlated queries are used when it is necessary to process data based on other processed data. That is, the query uses results that were previously unknown and that are generated by another query. Subqueries may be used with the FROM, WHERE, IN, and HAVING clauses in a SELECT statement. A subquery may return a single row or multiple rows.
• Most subqueries are executed in a serial fashion. That is, the outer query initiates the data request, and then the inner subquery is executed. In contrast, a correlated subquery is a subquery that is executed once for each row in the outer query. That process is similar to the typical nested loop in a programming language. A correlated subquery is so named because the inner query is related to the outer query—the inner query references a column of the outer subquery.
• SQL functions are used to extract or transform data. The most frequently used functions are date and time functions. The results of the function output can be used to store values in a database table, to serve as the basis for the computation of derived variables, or to serve as a basis for data comparisons. Function formats can be vendor-specific. Aside from time and date functions, there are numeric and string functions as well as conversion functions that convert one data format to another.
• SQL provides relational set operators to combine the output of two queries to generate a new relation. The UNION and UNION ALL set operators combine the output of two or more queries and produce a new relation with all unique (UNION) or dupli- cate (UNION ALL) rows from both queries. The INTERSECT relational set operator selects only the common rows. The EXCEPT (MINUS) set operator selects only the rows that are different. UNION, INTERSECT, and EXCEPT require union-compatible relations.
• In Oracle and SQL Server, sequences may be used to generate values to be assigned to a record. For example, a sequence may be used to number invoices automatically. MS Access uses an AutoNumber data type to generate numeric sequences, and MySQL uses the AUTO_INCREMENT property during table creation. Oracle and SQL Server can use the Identity column property to designate the column that will have
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Summary

Key Terms
anonymous PL/SQL block base table
batch update routine correlated subquery CREATE VIEW
cross join cursor
dynamic SQL embedded SQL explicit cursor
host language
implicit cursor
inner join
outer join
persistent stored module (PSM)
Procedural Language SQL (PL/SQL)
row-level trigger sequence
set-oriented statement-level trigger static SQL
stored function
stored procedure trigger union-compatible updatable view
view
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sequential numeric values automatically assigned to it. There can only be one Identity column per table.
• Procedural Language SQL (PL/SQL) can be used to create triggers, stored procedures, and PL/SQL functions. A trigger is procedural SQL code that is automatically invoked by the DBMS upon the occurrence of a specified data manipulation event (UPDATE, INSERT, or DELETE). Triggers are critical to proper database operation and manage- ment. They help automate various transaction and data management processes, and they can be used to enforce constraints that are not enforced at the DBMS design and implementation levels.
• A stored procedure is a named collection of SQL statements. Just like database trig- gers, stored procedures are stored in the database. One of the major advantages of stored procedures is that they can be used to encapsulate and represent complete business transactions. Use of stored procedures substantially reduces network traffic and increases system performance. Stored procedures also help reduce code duplica- tion by creating unique PL/SQL modules that are called by the application programs, thereby minimizing the chance of errors and the cost of application development and maintenance.
• When SQL statements are designed to return more than one value inside the PL/SQL code, a cursor is needed. You can think of a cursor as a reserved area of memory in which the output of the query is stored, like an array holding columns and rows. Cur- sors are held in a reserved memory area in the DBMS server, rather than in the client computer. There are two types of cursors: implicit and explicit.
• Embedded SQL refers to the use of SQL statements within an application program- ming language such as Visual Basic .NET, C#, COBOL, or Java. The language in which the SQL statements are embedded is called the host language. Embedded SQL is still the most common approach to maintaining procedural capabilities in DBMS-based applications.
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1. What is a CROSS JOIN? Give an example of its syntax.
2. What three join types are included in the OUTER JOIN classification?
3. Using tables named T1 and T2, write a query example for each of the three join types you described in Question 2. Assume that T1 and T2 share a common column named C1.
4. What is a subquery, and what are its basic characteristics?
5. What are the three types of results that a subquery can return?
6. What is a correlated subquery? Give an example.
7. Explain the difference between a regular subquery and a correlated subquery.
8. What does it mean to say that SQL operators are set-oriented?
9. The relational set operators UNION, INTERSECT, and EXCEPT (MINUS) work properly only when the relations are union-compatible. What does union-compati- ble mean, and how would you check for this condition?
10. WhatisthedifferencebetweenUNIONandUNIONALL?Writethesyntaxforeach.
11. Suppose you have two tables: EMPLOYEE and EMPLOYEE_1. The EMPLOYEE table contains the records for three employees: Alice Cordoza, John Cretchakov, and Anne McDonald. The EMPLOYEE_1 table contains the records for employees John Cretchakov and Mary Chen. Given that information, list the query output for the UNION query.
12. GiventheemployeeinformationinQuestion11,listthequeryoutputfortheUNION ALL query.
13. Given the employee information in Question 11, list the query output for the INTERSECT query.
14. Given the employee information in Question 11, list the query output for the EXCEPT (MINUS) query of EMPLOYEE to EMPLOYEE_1.
15. Whydoestheorderoftheoperands(tables)matterinanEXCEPT(MINUS)query but not in a UNION query?
16. What MS Access and SQL Server function should you use to calculate the number of days between your birth date and the current date?
17. WhatOraclefunctionshouldyouusetocalculatethenumberofdaysbetweenyour birth date and the current date?
18. Suppose a PRODUCT table contains two attributes, PROD_CODE and VEND_ CODE. Those two attributes have values of ABC, 125, DEF, 124, GHI, 124, and JKL, 123, respectively. The VENDOR table contains a single attribute, VEND_CODE, with values 123, 124, 125, and 126, respectively. (The VEND_CODE attribute in the PRODUCT table is a foreign key to the VEND_CODE in the VENDOR table.) Given that information, what would be the query output for:
a. A UNION query based on the two tables?
b. A UNION ALL query based on the two tables?
c. An INTERSECT query based on the two tables?
d. An EXCEPT (MINUS) query based on the two tables?
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Review Questions

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19. Whatstringfunctionshouldyouusetolistthefirstthreecharactersofacompany’s EMP_LNAME values? Give an example using a table named EMPLOYEE. Provide examples for Oracle and SQL Server.
20. What is a sequence? Write its syntax.
21. What is a trigger, and what is its purpose? Give an example.
22. What is a stored procedure, and why is it particularly useful? Give an example.
23. What is embedded SQL and how is it used?
24. What is dynamic SQL, and how does it differ from static SQL?
Problems
Use the database tables in Figure P8.1 as the basis for Problems 1–18.
FIGUREP8.1 CH08_SIMPLECODATABASETABLES
Database name: Ch08_SimpleCo Table name: CUSTOMER Table name: INVOICE
Table name: CUSTOMER_2
1. Create the tables. (Use the MS Access example shown in Figure P8.1 to see what table names and attributes to use.)
2. Insert the data into the tables you created in Problem 1.
3. Write the query that will generate a combined list of customers from the tables CUSTOMER and CUSTOMER_2 that do not include the duplicate customer records. Only the customer named Juan Ortega shows up in both customer tables. (Figure P8.3)
FIGUREP8.3 COMBINEDLISTOFCUSTOMERSWITHOUTDUPLICATES
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4. Write the query that will generate a combined list of customers to include the duplicate customer records. (Figure P8.4)
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FIGUREP8.4 COMBINEDLISTOFCUSTOMERSWITHDUPLICATES
5. Write the query that will show only the duplicate customer records. (Figure P8.5)
6. Write the query that will generate only the records that are unique to the CUSTOMER_2 table. (Figure P8.6)
7. Write the query to show the invoice number, customer number, customer name, invoice date, and invoice amount for all customers in the CUSTOMER table with a balance of $1,000 or more. (Figure P8.7)
8. Write the query for all the invoices that will show the invoice number, invoice amount, average invoice amount, and difference between the average invoice amount and the actual invoice amount. (Figure P8.8)
FIGUREP8.8 INVOICEAMOUNTSCOMPAREDTOTHEAVERAGE INVOICE AMOUNT
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FIGURE P8.5 DUPLICATE CUSTOMER RECORD
FIGUREP8.6 CUSTOMERSUNIQUETOTHECUSTOMER_2TABLE
FIGUREP8.7 INVOICESOFCUSTOMERSWITHABALANCEOVER$1000

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9. Write the query that will write Oracle sequences to produce automatic customer number and invoice number values. Start the customer numbers at 1000 and the invoice numbers at 5000.
10. Modify the CUSTOMER table to include two new attributes: CUST_DOB and CUST_AGE. Customer 1000 was born on March 15, 1979, and customer 1001 was born on December 22, 1988.
11. Assuming that you completed Problem 10, write the query that will list the names and ages of your customers.
12. Assuming that the CUSTOMER table contains a CUST_AGE attribute, write the query to update the values in that attribute. (Hint: Use the results of the previous query.)
13. Write the query that lists the average age of your customers. (Assume that the CUSTOMER table has been modified to include the CUST_DOB and the derived CUST_AGE attribute.)
14. WritethetriggertoupdatetheCUST_BALANCEintheCUSTOMERtablewhena new invoice record is entered. (Assume that the sale is a credit sale.) Test the trigger using the following new INVOICE record:
8005, 1001, ’27-APR-16′, 225.40
Name the trigger trg_updatecustbalance.
15. WriteaproceduretoaddanewcustomertotheCUSTOMERtable.Usethefollowing
values in the new record: 1002, ‘Rauthor’, ‘Peter’, 0.00
Name the procedure prc_cust_add. Run a query to see if the record has been added.
16. Write a procedure to add a new invoice record to the INVOICE table. Use the following values in the new record:
8006, 1000, ’30-APR-16′, 301.72
Name the procedure prc_invoice_add. Run a query to see if the record has been added.
17. Writeatriggertoupdatethecustomerbalancewhenaninvoiceisdeleted.Namethe trigger trg_updatecustbalance2.
18. Write a procedure to delete an invoice, giving the invoice number as a parameter. Name the procedure prc_inv_delete. Test the procedure by deleting invoices 8005 and 8006.
Use the Ch08_LargeCo database shown in Figure P8.19 to work Problems 19–27. For problems with very large result sets, only the first several rows of output are shown in the following figures.
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19. Write a query to display the products that have a price greater than $50.
20. Write a query to display the current salary for each employee in department 300. Assume that only current employees are kept in the system, and therefore the most current salary for each employee is the entry in the salary history with a NULL end date. Sort the output in descending order by salary amount. (Figure P8.20)
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FIGURE P8.19 THE LARGECO ERD
FIGUREP8.20 CURRENTSALARYFOREMPLOYEESINDEPARTMENT300

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21. Write a query to display the starting salary for each employee. The starting salary would be the entry in the salary history with the oldest salary start date for each employee. Sort the output by employee number. (Figure P8.21)
FIGURE P8.21 STARTING SALARY FOR EACH EMPLOYEE
22. Write a query to display the invoice number, line numbers, product SKUs, product descriptions, and brand ID for sales of sealer and top coat products of the same brand on the same invoice. (Figure P8.22)
FIGUREP8.22 INVOICESFORSEALERANDTOPCOATOFTHESAMEBRAND
23. TheBinderPrimeCompanywantstorecognizetheemployeewhosoldthemostof its products during a specified period. Write a query to display the employee number, employee first name, employee last name, email address, and total units sold for the employee who sold the most Binder Prime brand products between November 1, 2015, and December 5, 2015. If there is a tie for most units sold, sort the output by employee last name. (Figure P8.23)
24. Writeaquerytodisplaythecustomercode,firstname,andlastnameofallcustom- ers who have had at least one invoice completed by employee 83649 and at least one invoice completed by employee 83677. Sort the output by customer last name and then first name. (Figure P8.24)
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FIGUREP8.23 EMPLOYEESWITHMOSTBINDERPRIMEUNITSSOLD

25. LargeCo is planning a new promotion in Alabama (AL) and wants to know about the largest purchases made by customers in that state. Write a query to display the customer code, customer first name, last name, full address, invoice date, and invoice total of the largest purchase made by each customer in Alabama. Be certain to include any customers in Alabama who have never made a purchase; their invoice dates should be NULL and the invoice totals should display as 0. (Figure P8.25)
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FIGUREP8.24 CUSTOMERSWITHINVOICESFILLEDBYEMPLOYEES 83649 AND 83677
FIGURE P8.25 LARGEST PURCHASES OF CUSTOMERS IN AL
26. Oneofthepurchasingmanagersisinterestedintheimpactofproductpricesonthe sale of products of each brand. Write a query to display the brand name, brand type, average price of products of each brand, and total units sold of products of each brand. Even if a product has been sold more than once, its price should only be included once in the calculation of the average price. However, you must be careful because multiple products of the same brand can have the same price, and each of those prod- ucts must be included in the calculation of the brand’s average price. (Figure P8.26)
FIGUREP8.26 AVERAGEPRICEANDTOTALUNITSSOLDOFPRODUCTS BY BRAND
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27. Thepurchasingmanagerisstillconcernedabouttheimpactofpriceonsales.Write a query to display the brand name, brand type, product SKU, product description, and price of any products that are not a premium brand, but that cost more than the most expensive premium brand products. (Figure P8.27)
FIGUREP8.27 NON-PREMIUMPRODUCTSTHATAREMOREEXPENSIVE THAN PREMIUM PRODUCTS
Use the Ch08_SaleCo2 database shown in Figure P8.28 to work Problems 28–31.
FIGUREP8.28 CH08_SALECO2DATABASETABLES
Table name: CUSTOMER
Table name: PRODUCT
Table name: VENDOR
Database name: Ch08_SaleCo2 Table name: INVOICE
Table name: LINE
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28. Createatriggernamedtrg_line_totaltowritetheLINE_TOTALvalueintheLINE table every time you add a new LINE row. (The LINE_TOTAL value is the product of the LINE_UNITS and LINE_PRICE values.)
29. Create a trigger named trg_line_prod that automatically updates the quantity on hand for each product sold after a new LINE row is added.
30. Create a stored procedure named prc_inv_amounts to update the INV_SUB- TOTAL, INV_TAX, and INV_TOTAL. The procedure takes the invoice number as a parameter. The INV_SUBTOTAL is the sum of the LINE_TOTAL amounts for the invoice, the INV_TAX is the product of the INV_SUBTOTAL and the tax rate (8 percent), and the INV_TOTAL is the sum of the INV_SUBTOTAL and the INV_TAX.
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31. Create a procedure named prc_cus_balance_update that will take the invoice number as a parameter and update the customer balance. (Hint: You can use the DECLARE section to define a TOTINV numeric variable that holds the computed invoice total.)
Use the Ch08_AviaCo database shown in Figure P8.32 to work Problems 32–43.
32. Modify the MODEL table to add the attribute and insert the values shown in the following table.
33. Write the queries to update the MOD_WAIT_CHG attribute values based on Problem 32.
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FIGUREP8.32 CH08_AVIACODATABASETABLES
Table name: CHARTER
Table name: CREW
Table name: AIRCRAFT
Table name: CUSTOMER
Table name: EMPLOYEE
Table name: PILOT
Database name: Ch08_AviaCo Table name: EARNEDRATING
Table name: RATING
Table name: MODEL
ATTRIBUTE NAME
ATTRIBUTE DESCRIPTION
ATTRIBUTE TYPE
ATTRIBUTE VALUES
MOD_WAIT_CHG
Waiting charge per hour for each model
Numeric
$100 for C-90A $50 for PA23-250 $75 for PA31-350
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34. Modify the CHARTER table to add the attributes shown in the following table.
ATTRIBUTE NAME
ATTRIBUTE DESCRIPTION
ATTRIBUTE TYPE
CHAR_WAIT_CHG
Waiting charge for each model (copied from the MODEL table)
Numeric
CHAR_FLT_CHG_HR
Flight charge per mile for each model (copied from the MODEL table using the MOD_CHG_MILE attribute)
Numeric
CHAR_FLT_CHG
Flight charge (calculated by CHAR_HOURS_FLOWN × CHAR_FLT_CHG_HR)
Numeric
CHAR_TAX_CHG
CHAR_FLT_CHG × tax rate (8%)
Numeric
CHAR_TOT_CHG
CHAR_FLT_CHG + CHAR_TAX_CHG
Numeric
CHAR_PYMT
Amount paid by customer
Numeric
CHAR_BALANCE
Balance remaining after payment
Numeric
35. Write the sequence of commands required to update the CHAR_WAIT_CHG attribute values in the CHARTER table. (Hint: Use either an updatable view or a stored procedure.)
36. Write the sequence of commands required to update the CHAR_FLT_CHG_HR attribute values in the CHARTER table. (Hint: Use either an updatable view or a stored procedure.)
37. Write the command required to update the CHAR_FLT_CHG attribute values in the CHARTER table.
38. Write the command required to update the CHAR_TAX_CHG attribute values in the CHARTER table.
39. Write the command required to update the CHAR_TOT_CHG attribute values in the CHARTER table.
40. Modify the PILOT table to add the attribute shown in the following table.
41. Createatriggernamedtrg_char_hoursthatautomaticallyupdatestheAIRCRAFT table when a new CHARTER row is added. Use the CHARTER table’s CHAR_ HOURS_FLOWN to update the AIRCRAFT table’s AC_TTAF, AC_TTEL, and AC_TTER values.
42. Create a trigger named trg_pic_hours that automatically updates the PILOT table when a new CREW row is added and the CREW table uses a Pilot CREW_JOB entry. Use the CHARTER table’s CHAR_HOURS_FLOWN to update the PILOT table’s PIL_PIC_HRS only when the CREW table uses a Pilot CREW_JOB entry.
43. Create a trigger named trg_cust_balance that automatically updates the CUS- TOMER table’s CUS_BALANCE when a new CHARTER row is added. Use the CHARTER table’s CHAR_TOT_CHG as the update source. (Assume that all charter charges are charged to the customer balance.)
Problems 44–67 use the Ch08_Fact database shown in Figure P8.44. For problems with very large results sets, only the first several rows of output are shown in the following figures.
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ATTRIBUTE NAME
ATTRIBUTE DESCRIPTION
ATTRIBUTE TYPE
PIL_PIC_HRS
Pilot in command (PIC) hours; updated by adding the CHARTER table’s CHAR_HOURS_FLOWN to the PIL_PIC_HRS when the CREW table shows the CREW_JOB to be Pilot
Numeric

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FIGURE P8.44 THE CH08_FACT ERD
44. Write a query to display all rows in the PATRON table.
45. WriteaquerytodisplaythepatronID,booknumber,anddayskeptforeachcheck- out. “Days Kept” is the difference from the date on which the book is returned to the date it was checked out. (Figure P8.45)
46. Write a query to display the patron ID, patron full name, and patron type for each patron. (Figure P8.46)
FIGURE P8.45 DAYS KEPT FIGURE P8.46 PATRON AND PATRON TYPE
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47. Writeaquerytodisplaythebooknumber,titlewithyear,andsubjectforeachbook. (Figure P8.47)
48. Write a query to display the author last name, author first name, and book number for each book written by that author. (Figure P8.48)
49. Write a query to display the author ID, book number, title, and year for each book. (Figure P8.49)
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FIGUREP8.47 BOOKTITLEWITHYEAR
FIGUREP8.48 BOOKSWRITTENBYAUTHOR
FIGURE P8.49 AUTHORS OF BOOKS

50. Write a query to display the author last name, first name, book title, and year for each book. (Figure P8.50)
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FIGUREP8.50 AUTHORNAMEANDBOOKTITLE
51. Write a query to display the patron ID, book number, patron first name and last name, and book title for all currently checked out books. (Remember to use the redundant relationship described in the assignment instructions for current check- outs.) Sort the output by patron last name and book title. (Figure P8.51)
FIGURE P8.51 CURRENTLY CHECKED OUT BOOKS
52. WriteaquerytodisplaythepatronID,fullname(firstandlast),andpatrontypefor all patrons. Sort the results by patron type, then by last name and first name. Ensure that all sorting is case insensitive. (Figure P8.52)
FIGUREP8.52 SORTEDPATRONSWITHFULLNAMES
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53. Write a query to display the book number and the number of times each book has been checked out. Do not include books that have never been checked out. (Figure P8.53)
54. WriteaquerytodisplaytheauthorID,firstandlastname,booknumber,andbook title of all books in the subject “Cloud”. Sort the results by book title and then by author last name. (Figure P8.54)
55. Writeaquerytodisplaythebooknumber,title,authorlastname,authorfirstname, patron ID, last name, and patron type for all books currently checked out to a patron. Sort the results by book title. (Figure P8.55)
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FIGURE P8.53 TIMES CHECKED OUT
FIGURE P8.54 BOOKS ON CLOUD COMPUTING
FIGUREP8.55 CURRENTLYCHECKEDOUTBOOKSWITHAUTHOR

56. Writeaquerytodisplaythebooknumber,title,andnumberoftimeseachbookhas been checked out. Include books that have never been checked out. Sort the results in descending order by the number times checked out, then by title. (Figure P8.56)
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FIGURE P8.56 NUMBER OF CHECKOUTS FOR EVERY BOOK
57. Writeaquerytodisplaythebooknumber,title,andnumberoftimeseachbookhas been checked out. Limit the results to books that have been checked out more than five times. Sort the results in descending order by the number of times checked out, and then by title. (Figure P8.57)
FIGUREP8.57 BOOKSWITHMORETHAN5CHECKOUTS
58. Write a query to display the author ID, author last name, book title, checkout date, and patron last name for all the books written by authors with the last name “Bruer” that have ever been checked out by patrons with the last name “Miles”. (Figure P8.58)
FIGUREP8.58 BOOKSBYAUTHORFORPATRON“MILES”
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59. Write a query to display the patron ID, first and last name of all patrons that have never checked out any book. Sort the result by patron last name then first name. (Figure P8.59)
FIGUREP8.59 PATRONSTHATNEVERCHECKEDOUTABOOK
60. Write a query to display the patron ID, last name, number of times that patron has ever checked out a book, and the number of different books the patron has ever checked out. For example, if a given patron has checked out the same book twice, that would count as two checkouts but only one book. Limit the results to only patrons that have made at least three checkouts. Sort the results in descending order by number of books, then in descending order by number of checkouts, then in ascending order by patron ID. (Figure P8.60)
FIGURE P8.60 CHECKOUTS AND BOOKS BY PATRON
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61. Write a query to display the average number of days a book is kept during a check- out. (Figure P8.61)
62. Write a query to display the patron ID and the average number of days that patron keeps books during a checkout. Limit the results to only patrons that have at least three checkouts. Sort the results in descending order by the average days the book is kept. (Figure P8.62)
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FIGURE P8.61 AVERAGE DAYS KEPT
FIGURE P8.62 AVERAGE DAYS KEPT BY PATRON
63. Writeaquerytodisplaythebooknumber,title,andcostofbooksthathavethelow- est cost of any books in the system. Sort the results by book number. (Figure P8.63)
FIGURE P8.63 LEAST EXPENSIVE BOOKS
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64. Write a query to display the author ID, first and last name for all authors that have never written a book with the subject Programming. Sort the results by author last name. (Figure P8.64)
65. Writeaquerytodisplaythebooknumber,title,subject,averagecostofbookswithin that subject, and the difference between each book’s cost and the average cost of books in that subject. Sort the results by book title. (Figure P8.65)
66. Write a query to display the book number, title, subject, author last name, and the number of books written by that author. Limit the results to books in the Cloud subject. Sort the results by book title and then author last name. (Figure P8.66)
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FIGURE P8.64 AUTHORS THAT HAVE NEVER WRITTEN ON PROGRAMMING
FIGUREP8.65 BOOKSWITHAVERAGECOSTBYSUBJECT
FIGURE P8.66 NUMBER OF BOOKS BY CLOUD AUTHORS

67. Write a query to display the lowest average cost of books within a subject and the highest average cost of books within a subject. (Figure P8.67)
The following problems expand on the EliteVideo case from Chapter 7. To complete the following problems, you must have first completed the table creation and data-entry requirements specified in Problems 96 and 97 in Chapter 7.
68. Alter the DETAILRENTAL table to include a derived attribute named DETAIL_ DAYSLATE to store integers of up to three digits. The attribute should accept null values.
69. UpdatetheDETAILRENTALtabletosetthevaluesinDETAIL_RETURNDATEto include a time component. Make each entry match the values shown in the follow- ing table.
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FIGURE P8.67 LOWEST AND HIGHEST AVERAGE SUBJECT COSTS
RENT_NUM VID_NUM DETAIL_RETURNDATE
1001 34342 02-MAR-16 10:00am
1001 61353 03-MAR-16 11:30am
1002 59237 04-MAR-16 03:30pm
1003 54325 09-MAR-16 04:00pm
1003 61369 09-MAR-16 04:00pm
1003 61388 09-MAR-16 04:00pm
1004 44392 07-MAR-16 09:00am
1004 34367 07-MAR-16 09:00am
1004 34341 07-MAR-16 09:00am
1005 34342 05-MAR-16 12:30pm
1005 44397 05-MAR-16 12:30pm
1006 34366 04-MAR-16 10:15pm
1006 61367
1007 34368
1008 34369 05-MAR-16 09:30pm
1009 54324
1001 34366 02-MAR-16 10:00am
70. AltertheVIDEOtabletoincludeanattributenamedVID_STATUStostorecharac- ter data up to four characters long. The attribute should not accept null values. The attribute should have a constraint to enforce the domain (“IN”, “OUT”, and “LOST”) and have a default value of “IN”.
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Cases

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71. Update the VID_STATUS attribute of the VIDEO table using a subquery to set the VID_STATUS to “OUT” for all videos that have a null value in the DETAIL_ RETURNDATE attribute of the DETAILRENTAL table.
72. Alter the PRICE table to include an attribute named PRICE_RENTDAYS to store integers of up to two digits. The attribute should not accept null values, and it should have a default value of 3.
73. Update the PRICE table to place the values shown in the following table in the PRICE_RENTDAYS attribute.
74. Createatriggernamedtrg_late_returnthatwillwritethecorrectvaluetoDETAIL_ DAYSLATE in the DETAILRENTAL table whenever a video is returned. The trigger should execute as a BEFORE trigger when the DETAIL_RETURNDATE or DETAIL_DUEDATE attributes are updated. The trigger should satisfy the following conditions:
a. If the return date is null, then the days late should also be null.
b. If the return date is not null, then the days late should determine if the video is returned late.
c. If the return date is noon of the day after the due date or earlier, then the video is not considered late, and the days late should have a value of zero (0).
d. If the return date is past noon of the day after the due date, then the video is considered late, so the number of days late must be calculated and stored.
75. Create a trigger named trg_mem_balance that will maintain the correct value in the membership balance in the MEMBERSHIP table when videos are returned late. The trigger should execute as an AFTER trigger when the due date or return date attributes are updated in the DETAILRENTAL table. The trigger should satisfy the following conditions:
a. Calculate the value of the late fee prior to the update that triggered this execu- tion of the trigger. The value of the late fee is the days late multiplied by the daily late fee. If the previous value of the late fee was null, then treat it as zero (0).
b. Calculate the value of the late fee after the update that triggered this execution of the trigger. If the value of the late fee is now null, then treat it as zero (0).
c. Subtract the prior value of the late fee from the current value of the late fee to determine the change in late fee for this video rental.
d. If the amount calculated in Part c is not zero (0), then update the membership balance by the amount calculated for the membership associated with this rental.
76. Create a sequence named rent_num_seq to start with 1100 and increment by 1. Do not cache any values.
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PRICE_CODE
PRICE_RENTDAYS
1
5
2
3
3
5
4
7

77. Createastoredprocedurenamedprc_new_rentaltoinsertnewrowsintheRENTAL table. The procedure should satisfy the following conditions:
a. The membership number will be provided as a parameter.
b. Use a Count() function to verify that the membership number exists in the MEMBERSHIP table. If it does not exist, then a message should be displayed that the membership does not exist and no data should be written to the database.
c. If the membership does exist, then retrieve the membership balance and display a message that the balance amount is the previous balance. (For example, if the membership has a balance of $5.00, then display “Previous balance: $5.00”.)
d. Insert a new row in the rental table using the sequence created in Case Question 76 to generate the value for RENT_NUM, the current system date for the RENT_DATE value, and the membership number provided as the value for MEM_NUM.
78. Create a stored procedure named prc_new_detail to insert new rows in the DETAILRENTAL table. The procedure should satisfy the following requirements:
a. The video number will be provided as a parameter.
b. Verify that the video number exists in the VIDEO table. If it does not exist, then display a message that the video does not exist, and do not write any data to the database.
c. If the video number does exist, then verify that the VID_STATUS for the video is “IN”. If the status is not “IN”, then display a message that the video’s return must be entered before it can be rented again, and do not write any data to the database.
d. If the status is “IN”, then retrieve the values of the video’s PRICE_RENTFEE, PRICE_DAILYLATEFEE, and PRICE_RENTDAYS from the PRICE table.
e. Calculate the due date for the video rental by adding the number of days in PRICE_RENTDAYS to 11:59:59PM (hours:minutes:seconds) in the current system date.
f. Insert a new row in the DETAILRENTAL table using the previous value returned by RENT_NUM_SEQ as the RENT_NUM, the video number pro- vided in the parameter as the VID_NUM, the PRICE_RENTFEE as the value for DETAIL_FEE, the due date calculated above for the DETAIL_DUEDATE, PRICE_DAILYLATEFEE as the value for DETAIL_DAILYLATEFEE, and null for the DETAIL_RETURNDATE.
79. Create a stored procedure named prc_return_video to enter data about the return of videos that have been rented. The procedure should satisfy the following requirements.
a. The video number will be provided as a parameter.
b. Verify that the video number exists in the VIDEO table. If it does not exist, display a message that the video number provided was not found and do not write any data to the database.
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c. If the video number does exist, then use a Count() function to ensure that the video has only one record in DETAILRENTAL for which it does not have a return date. If more than one row in DETAILRENTAL indicates that the video is rented but not returned, display an error message that the video has multiple outstanding rentals and do not write any data to the database.
d. If the video does not have any outstanding rentals, then update the video status to “IN” for the video in the VIDEO table, and display a message that the video had no outstanding rentals but is now available for rental. If the video has only one outstanding rental, then update the return date to the current system date, and update the video status to “IN” for that video in the VIDEO table. Then display a message that the video was successfully returned.
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Chapter 9
Database Design
In this chapter, you will learn:
• That a sound database design is the foundation for a successful information system, and that the database design must reflect the information system of which the database is a part
• That successful information systems are developed within a framework known as the Systems Development Life Cycle (SDLC)
• That within the information system, the most successful databases are subject to frequent evaluation and revision within a framework known as the Database Life Cycle (DBLC)
• How to conduct evaluation and revision within the SDLC and DBLC frameworks
• About database design strategies: top-down versus bottom-up design and centralized versus
decentralized design
Preview
Databases are a part of a larger picture called an information system. Database designs that fail to recognize this fact are not likely to be successful. Database designers must rec- ognize that the database is a critical means to an end rather than an end in itself. Managers want the database to serve their management needs, but too many databases seem to force managers to alter their routines to fit the database requirements.
Information systems don’t just happen; they are the product of a carefully staged devel- opment process. Systems analysis is used to determine the need for an information system and to establish its limits. Within systems analysis, the actual information system is cre- ated through a process known as systems development.
The creation and evolution of information systems follows an iterative pattern called the Systems Development Life Cycle (SDLC), which is a continuous process of creation, maintenance, enhancement, and replacement of the information system. A similar cycle applies to databases: the database is created, maintained, enhanced, and eventually replaced. The Database Life Cycle (DBLC) is carefully traced in this chapter, and is shown in the context of the larger Systems Development Life Cycle.
At the end of the chapter, you will be introduced to some classical approaches to data- base design: top-down versus bottom-up and centralized versus decentralized.
Note
Data Files Available on cengagebrain.com
Because it is purely conceptual, this chapter does not reference any data files.
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9-1 The Information System
Basically, a database is a carefully designed and constructed repository of facts. The database is part of a larger whole known as an information system, which provides for data collection, storage, transformation, and retrieval. The information system also helps transform data into information, and it allows for the management of both data and information. Thus, a complete information system is composed of people, hardware, software, the database(s), application programs, and procedures. Systems analysis is the process that establishes the need for an information system and its extent. The process of creating an information system is known as systems development.
One key characteristic of current information systems is the strategic value of infor- mation in the age of global business. Therefore, information systems should always be aligned with strategic business mission and goals; the view of isolated and independent information systems is no longer valid. Current information systems should always be integrated with the company’s enterprise-wide information systems architecture.
information system (IS)
A system that provides for data collection, storage, and retrieval; facilitates the transformation of data into information; and manages both data
and information. An information system is composed of hardware, the DBMS and other software, database(s), people, and procedures.
Note
This chapter does not mean to cover all aspects of systems analysis and development, which are usually covered in a separate course or book. However, this chapter should help you better understand the issues associated with database design, implementation, and management, all of which are affected by the information system in which the database is a critical component.
systems analysis
The process that establishes the need for an information system and its extent.
systems development
The process of creating an information system.
Within the framework of systems development, applications transform data into the information that forms the basis for decision making. Applications usually generate formal reports, tabulations, and graphic displays designed to produce insight from the informa- tion. Figure 9.1 illustrates that every application is composed of two parts: the data and the code (program instructions) by which the data is transformed into information. The data and code work together to represent real-world business functions and activities. At any given moment, physically stored data represents a snapshot of the business, but the picture is not complete without an understanding of the business activities represented by the code.
The performance of an information system depends on three factors:
• Database design and implementation
• Application design and implementation
• Administrative procedures
FIGURE9.1 GENERATINGINFORMATIONFORDECISIONMAKING
Information
Data Application code
Decisions
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This book emphasizes the database design and implementation segment of the triad—arguably the most important of the three. However, failure to address the other two segments will likely yield a poorly functioning information system. Creating a sound information system is hard work: systems analysis and development require extensive planning to ensure that all of the activities will interface with each other, that they will complement each other, and that they will be completed on time.
In a broad sense, the term database development describes the process of database design and implementation. The primary objective in database design is to create com- plete, normalized, nonredundant (to the greatest extent possible), and fully integrated conceptual, logical, and physical database models. The implementation phase includes creating the database storage structure, loading data into the database, and providing for data management. Consideration should be taken to design and implement a database that is flexible and scalable over time. Although most designs typically focus on solving current problems, it is important to create a design that is flexible enough to adapt to future changes (such as performance, size, or reporting requirements).
To make the procedures discussed in this chapter broadly applicable, the chapter focuses on the elements that are common to all information systems. Most of the processes and procedures described in this chapter do not depend on the size, type, or complexity of the database being implemented. However, the procedures that would be used to design a small database, such as one for a neighborhood shoe store, do not precisely scale up to the procedures that would be needed to design a database for a large corporation or even a segment of such a corporation. To use an analogy, building a small house requires a blueprint, just as building the Golden Gate Bridge did, but the bridge required far more complex planning, analysis, and design.
The next sections will trace the overall Systems Development Life Cycle and the related Database Life Cycle. Once you are familiar with those processes and procedures, you will learn about various approaches to database design, such as top-down versus bottom-up and centralized versus decentralized design.
Note
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1 See Rapid Application Development, James Martin, Prentice-Hall, Macmillan College Division, 1991. 2 For more information about Agile Software Development, go to www.agilealliance.org.
database development
The process of database design and implementation.
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The Systems Development Life Cycle is a general framework through which you can track and understand the activities required to develop and maintain information systems. Within that framework, there are several ways to complete various tasks specified in the SDLC. For example, this book focuses on ER modeling and on relational database design and implementation, and that focus is maintained in this chapter. However, there are alternative methodologies:
• Unified Modeling Language (UML) provides object-oriented tools to support the tasks associated with the development of information systems. UML is covered in Appendix H, Unified Modeling Language (UML), at www.cengagebrain.com.
• Rapid Application Development (RAD)1 is an iterative software development methodol- ogy that uses prototypes, CASE tools, and flexible management to develop application systems. RAD started as an alternative to traditional structured development, which suffered from long deliverable times and unfulfilled requirements.
• Agile Software Development2 is a framework for developing software applications that divides the work into smaller subprojects to obtain valuable deliverables in shorter times and with better cohesion. This method emphasizes close communi- cation among all users and continuous evaluation with the purpose of increasing customer satisfaction.
Although the development methodologies may change, the basic framework within which they are used does not change.

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9-2 The Systems Development Life Cycle
The Systems Development Life Cycle (SDLC) traces the history of an information system. Perhaps more important to the system designer, the SDLC provides the big picture within which the database design and application development can be mapped out and evaluated.
As illustrated in Figure 9.2, the traditional SDLC is divided into five phases: planning, analysis, detailed systems design, implementation, and maintenance. The SDLC is an iterative process rather than a sequential process. For example, the details of the feasibil- ity study might help refine the initial assessment, and the details discovered during the user requirements portion of the SDLC might help refine the feasibility study.
Because the Database Life Cycle fits into and resembles the SDLC, a brief description of the SDLC is in order.
9-2a Planning
The SDLC planning phase yields a general overview of the company and its objectives. An initial assessment of the information flow-and-extent requirements must be made during this discovery portion of the SDLC. Such an assessment should answer some important questions:
• Should the existing system be continued? If the information generator does its job well, there is no point in modifying or replacing it. To quote an old saying, “If it ain’t broke, don’t fix it.”
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
FIGURE 9.2 THE SYSTEMS DEVELOPMENT LIFE CYCLE (SDLC)
Phase
Planning
Analysis
Detailed systems design
Implementation
Maintenance
Action(s) Section
Initial assessment 9-2a Feasibility study
User requirements 9-2b Existing system evaluation
Logical system design
Detailed system specification 9-2c
Coding, testing, and debugging Installation, fine-tuning
9-2d
Evaluation 9-2e Maintenance
Enhancement
Systems Development Life Cycle (SDLC)
The cycle that traces the history of an information system. The SDLC provides the big picture within which database design and application development can
be mapped out and evaluated.
Copyright 2017 Cengage Lea Editorial review has deemed that

• Should the existing system be modified? If the initial assessment indicates deficiencies in the extent and flow of the information, minor (or even major) modifications might be needed. When considering modifications, the participants in the initial assessment must remember the distinction between wants and needs.
• Should the existing system be replaced? The initial assessment might indicate that the current system’s flaws are beyond fixing. Given the effort required to create a new sys- tem, a careful distinction between wants and needs is perhaps even more important in this case than it is when modifying the system.
Participants in the SDLC’s initial assessment must begin to study and evaluate alter- native solutions. If a new system is necessary, the next question is whether it is feasible. The feasibility study must address the following:
• The technical aspects of hardware and software requirements. The decisions might not yet be vendor-specific, but they must address the nature of the hardware requirements (desktop computer, multiprocessor computer, mainframe, or supercomputer) and the software requirements (single-user or multiuser operating systems, database type and software, programming languages to be used by the applications, and so on).
• The system cost. The admittedly mundane question “Can we afford it?” is crucial. The answer might force a careful review of the initial assessment. A million-dollar solu- tion to a thousand-dollar problem is not defensible. At some point, the decision may be between building a system “in-house” or buying (and customizing) a third-party vendor system. In the long run, you need to find a cost-effective solution that best serves the needs (present and future) of the organization.
• The operational cost. Does the company possess the human, technical, and finan- cial resources to keep the system operational? Should the feasibility study include the cost of management and end-user support needed to implement operational procedures to ensure the success of this system? What would be the impact of this new system in the company’s culture? People’s resistance to change should never be underestimated.3
Even if you choose to “buy” rather than to “build,” the system implementation must
be carefully planned for it to be successful. Whatever the chosen option (build or buy), an analysis must be done to deploy the solution across the organization in ways that min- imize cost and culture changes, while maximizing value. The SDLC provides a frame- work for sound planning and implementation.
9-2b Analysis
Problems defined during the planning phase are examined in greater detail during the analysis phase. A macro analysis must be made both of individual needs and organiza- tional needs, addressing questions such as:
• What are the requirements of the current system’s end users?
• Do those requirements fit into the overall information requirements?
The analysis phase of the SDLC is, in effect, a thorough audit of user requirements.
The existing hardware and software systems are also studied during the analysis phase. The result of the analysis should be a better understanding of the system’s functional areas, actual and potential problems, and opportunities.
3 “At Zappos, 210 employees decide to leave rather than work with ’no bosses,’” Jena McGregor, Washington Post, May 8, 2015.
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Note
End users and the system designer(s) must work together to identify processes and uncover potential problem areas. Such cooperation is vital to defining the appropriate performance objectives by which the new system can be judged.
Along with a study of user requirements and the existing systems, the analysis phase also includes the creation of a logical systems design. The logical design must specify the appropriate conceptual data model, inputs, processes, and expected output requirements.
When creating a logical design, the designer might use tools such as data flow dia- grams (DFDs), hierarchical input process output (HIPO) diagrams, entity relation- ship (ER) diagrams, and even some application prototypes. The database design’s data-modeling activities take place at this point to discover and describe all entities and their attributes, and the relationships among the entities within the database.
Defining the logical system also yields functional descriptions of the system’s com- ponents (modules) for each process within the database environment. All data transfor- mations (processes) are described and documented, using systems analysis tools such as DFDs. The conceptual data model is validated against those processes.
9-2c Detailed Systems Design
In the detailed systems design phase, the designer completes the design of the system’s processes. The design includes all the necessary technical specifications for the screens, menus, reports, and other devices that might help make the system a more efficient infor- mation generator. The steps are laid out for conversion from the old system to the new system. Training principles and methodologies are also planned and must be submitted for management’s approval.
Because this book has focused on the details of systems design, it has not explicitly rec- ognized until now that management approval is needed at all stages of the process. Such approval is needed because a “go” decision requires funding. There are many “go” and “no go” decision points along the way to a completed systems design!
9-2d Implementation
During the implementation phase, the hardware, DBMS software, and application programs are installed, and the database design is implemented. During the initial stages of the implementation phase, the system enters into a cycle of coding, testing, and debugging until it is ready to be delivered. The actual database is created, and the system is customized by the creation of tables and views, user authorizations, and so on.
The database contents might be loaded interactively or in batch mode, using a variety of methods and devices:
• Customized user programs
• Database interface programs
• Conversion programs that import the data from a different file structure, using batch programs, a database utility, or both
The system is subjected to exhaustive testing until it is ready for use. Traditionally, the implementation and testing of a new system took 50 to 60 percent of the total
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development time. However, the advent of sophisticated application generators and debugging tools has substantially decreased coding and testing time. After testing is concluded, the final documentation is reviewed and printed and end users are trained. The system is in full operation at the end of this phase, but it will be continuously eval- uated and fine-tuned.
9-2e Maintenance
Almost as soon as the system is operational, end users begin to request changes in it. Those changes generate system maintenance activities, which can be grouped into three types:
• Corrective maintenance in response to systems errors
• Adaptive maintenance due to changes in the business environment
• Perfective maintenance to enhance the system
Because every request for structural change requires retracing the SDLC steps, the system is, in a sense, always at some stage of the SDLC.
Each system has a predetermined operational life span, but its actual life span depends on its perceived utility. There are several reasons for reducing the operational life of cer- tain systems. Rapid technological change is one reason, especially for systems based on processing speed and expandability. Another common reason is the cost of maintaining a system.
If the system’s maintenance cost is high, its value becomes suspect. Computer-aided software engineering (CASE) tools, such as System Architect or Visio Professional, help produce better systems within a reasonable amount of time and at a reasonable cost. In addition, CASE-produced applications are more structured, better documented, and especially standardized, which tends to prolong the operational life of systems by making them easier and cheaper to update and maintain.
9-3 The Database Life Cycle
Within the larger information system, the database is subject to a life cycle as well. The Database Life Cycle (DBLC) contains six phases, as shown in Figure 9.3: database initial study, database design, implementation and loading, testing and evaluation, operation, and maintenance and evolution.
9-3a The Database Initial Study
If a designer has been called in, chances are that the current system has failed to perform functions deemed vital by the company. (You don’t call the plumber unless the pipes leak.) Therefore, in addition to examining the current system’s operation within the com- pany, the designer must determine how and why the current system fails. That means spending a lot of time talking and listening to end users. Although database design is a technical business, it is also people-oriented. Database designers must be excellent com- municators and must have finely tuned interpersonal skills.
Depending on the complexity and scope of the database environment, the database designer might be a lone operator or part of a systems development team composed of a project leader, one or more senior systems analysts, and one or more junior systems analysts. The word designer is used generically here to cover a wide range of design team compositions.
computer-aided systems engineering (CASE)
Tools used to automate part or all of the Systems Development Life Cycle.
Database Life Cycle (DBLC)
A cycle that traces the history of a database within an information system. The cycle is divided into six phases: initial study, design, implementation and loading, testing and evaluation, operation and maintenance, and evolution.
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FIGURE 9.3 THE DATABASE LIFE CYCLE (DBLC)
Phase
Database initial study
Database design
Implementation and loading
Testing and evaluation
Operation
Maintenance and evolution
Action(s)
Analyze the company situation Define problems and constraints Define objectives
Define scope and boundaries
Create the conceptual design DBMS software selection Create the logical design Create the physical design
Install the DBMS
Create the database(s) Load or convert the data
Section 9-3a
9-3b
9-3c
9-3d
Test the database
Fine-tune the database
Evaluate the database and its application programs
Produce the required information flow 9-3e
Introduce changes 9-3f Make enhancements
The overall purpose of the database initial study is to:
• Analyze the company situation
• Define problems and constraints
• Define objectives
• Define scope and boundaries
Figure 9.4 depicts the interactive and iterative processes required to complete the first phase of the DBLC successfully. Note that the database initial study phase leads to the development of database system objectives. Using Figure 9.4 as a discussion template, examine each of its components in greater detail.
AnalyzetheCompanySituation Thecompanysituationdescribesthegeneralcondi- tions in which a company operates, its organizational structure, and its mission. To ana- lyze the company situation, the database designer must learn the company’s operational components, how they function, and how they interact.
The following issues must be resolved:
• What is the organization’s general operating environment, and what is its mission within that environment? The design must satisfy the operational demands created by the
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FIGURE9.4 ASUMMARYOFACTIVITIESINTHEDATABASEINITIALSTUDY
Company objectives
Analysis of the company situation
Company operations
Definition of problems and constraints
Database system specifications
Scope
Company structure
Objectives
Boundaries
organization’s mission. For example, a mail-order business probably has operational requirements for its database that are quite different from those of a manufacturing business.
• What is the organization’s structure? Knowing who controls what and who reports to whom is quite useful when you need to define required information flows, specific report and query formats, and so on.
Define Problems and Constraints The designer has both formal and informal sources of information. If the company has existed for any length of time, it already has a system in place (either manual or computer-based). How does the existing system function? What input does the system require? What documents does the system gener- ate? By whom and how is the system output used? Studying the paper trail can be very informative. In addition to the official version of the system’s operation, there is also the more informal, perhaps more real version; the designer must be shrewd enough to see how these differ.
The process of defining problems might initially appear to be unstructured. Company end users often cannot precisely describe the larger scope of company operations or identify the real problems encountered during company operations. Often the mana- gerial view of a company’s operation and its problems is different from that of the end users, who perform the actual routine work.
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During the initial problem definition process, the designer is likely to collect very broad problem descriptions. For example, note the following concerns expressed by the president of a fast-growing, transnational manufacturing company:
Although the rapid growth is gratifying, members of the management team are con- cerned that such growth is beginning to undermine the ability to maintain a high customer service standard, and perhaps worse, to diminish manufacturing standards control.
The problem definition process quickly leads to a host of general problem descrip- tions. For example, the marketing manager comments:
I’m working with an antiquated filing system. We manufacture more than 1,700 spe- cialty machine parts. When a regular customer calls in, we can’t get a very quick inventory scan. If a new customer calls in, we can’t do a current parts search by using a simple description, so we often do a machine setup for a part that we have in inven- tory. That’s wasteful. And of course, some new customers get irritated when we can’t give a quick response.
The production manager comments:
At best, it takes hours to generate the reports I need for scheduling purposes. I don’t have hours for quick turnarounds. It’s difficult to manage what I don’t have informa- tion about.
I don’t get quick product request routing. Take machine setup. Right now I’ve got operators either waiting for the right stock or getting it themselves when a new part is scheduled for production. I can’t afford to have an operator doing chores that a much lower-paid worker ought to be doing. There’s just too much waiting around with the current scheduling. I’m losing too much time, and my schedules back up. Our over- time bill is ridiculous.
I sometimes produce parts that are already in inventory because we don’t seem to be able to match what we’ve got in inventory with what we have scheduled. Shipping yells at me because I can’t turn out the parts, and often they’ve got them in inventory one bay down. That’s costing us big bucks sometimes.
New reports can take days or even weeks to get to my office. And I need a ton of reports to schedule personnel, downtime, training, etc. I can’t get new reports that I need NOW. What I need is the ability to get quick updates on percent defectives, percent rework, the effectiveness of training, you name it. I need such reports by shift, by date, by any characteristic I can think of to help me manage scheduling, training, you name it.
A machine operator comments:
It takes a long time to set my stuff up. If I get my schedule banged up because John doesn’t get the paperwork on time, I wind up looking for setup specs, startup material, bin assignments, and other stuff. Sometimes I spend two or three hours just setting up. Now you know why I can’t meet schedules. I try to be productive, but I’m spending too much time getting ready to do my job.
After the initial declarations, the database designer must continue to probe carefully to generate additional information that will help define the problems within the larger framework of company operations. How does the problem of the marketing manager’s
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customer fit within the broader set of marketing department activities? How does the solution to the customer’s problem help meet the objectives of the marketing depart- ment and the rest of the company? How do the marketing department’s activities relate to those of the other departments? That last question is especially important. Note that there are common threads in the problems described by the marketing and production department managers. If the inventory query process can be improved, both depart- ments are likely to find simple solutions to at least some of their problems.
Finding precise answers is important, especially concerning the operational relation- ships among business units. If a proposed system will solve the marketing department’s problems but exacerbate those of the production department, not much progress will have been made. Using an analogy, suppose that your home water bill is too high. You have determined the problem: the faucets leak. The solution? You step outside and cut off the water supply to the house. However, is that an adequate solution, or would the replacement of faucet washers do a better job of solving the problem? You might find this scenario simplistic, yet almost any experienced database designer can find similar instances of database problem solving, although they are admittedly more complicated.
Even the most complete and accurate problem definition does not always lead to the perfect solution. The real world usually intrudes to limit the design of even the most ele- gant database by imposing constraints such as time, budget, and personnel. If you must have a solution within a month and within a $12,000 budget, you cannot take two years to develop a database at a cost of $100,000. The designer must learn to distinguish between what’s perfect and what’s possible.
Define Objectives A proposed database system must be designed to help solve at least the major problems identified during the problem discovery process. As the list of problems unfolds, several common sources are likely to be discovered. In the previous example, both the marketing manager and the production manager seem to be plagued by inventory inefficiencies. If the designer can create a database that sets the stage for more efficient parts management, both departments gain. The initial objective, therefore, might be to create an efficient inventory query and management system.
Note
Note that the initial study phase also yields proposed problem solutions. The designer’s job is to make sure that his or her database system objectives correspond to those envi- sioned by the end user(s). In any case, the database designer must begin to address the following questions:
• What is the proposed system’s initial objective?
• Will the system interface with other existing or future systems in the company?
• Will the system share the data with other systems or users?
scope
The part of a system that defines the
extent of the design, according to operational requirements.
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When trying to develop solutions, the database designer must look for the source of the problems. Many database systems have failed to satisfy the end users because they were designed to treat the symptoms of the problems rather than their source.
Define Scope and Boundaries The designer must recognize two sets of limits: scope and boundaries. The system’s scope defines the extent of the design according to operational requirements. Will the database design encompass the entire organization, one or more departments within the organization, or one or more functions of a single
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department? The designer must know the “size of the ballpark.” Knowing the scope helps define the required data structures, the type and number of entities, the physical size of the database, and so on.
The proposed system is also subject to limits known as boundaries, which are external to the system. Has any designer ever been told, “We have all the time in the world” or “Use an unlimited budget and as many people as needed to make the design come together”? Boundaries are also imposed by existing hardware and software. Ideally, the designer can choose the hardware and software that will best accomplish the system goals. In fact, software selection is an important aspect of the Systems Development Life Cycle. Unfortunately, in the real world, a system must often be designed around existing hardware. Thus, the scope and boundaries become the factors that force the design into a specific mold, and the designer’s job is to design the best system possible within those constraints. (Note that prob- lem definitions and the objectives must sometimes be reshaped to meet the system scope and boundaries.)
9-3b Database Design
The second phase of the DBLC focuses on the design of the database model that will support company operations and objectives. This is arguably the most critical DBLC phase: making sure that the final product meets user and system requirements. In the process of database design, you must concentrate on the data characteristics required to build the database model. At this point, there are two views of the data within the system: the business view of data as a source of information and the designer’s view of the data structure, its access, and the activities required to transform the data into information. Figure 9.5 contrasts those views. Note that you can summarize the dif- ferent views by looking at the terms what and how. Defining data is an integral part of the DBLC’s second phase.
As you examine the procedures required to complete the design phase in the DBLC, remember these points:
• The process of database design is loosely related to the analysis and design of a larger system. The data component is only one element of a larger information system.
• The systems analysts or systems programmers are in charge of designing the other system components. Their activities create the procedures that will help transform the data within the database into useful information.
• The database design does not constitute a sequential process. Rather, it is an iterative process that provides continuous feedback designed to trace previous steps.
The database design process is depicted in Figure 9.6. The figure shows that there are three essential stages: conceptual, logical, and physical design, plus the DBMS selec- tion decision, which is critical to determine the type of logical and physical designs to be created. The design process starts with conceptual design and moves to the logical and physical design stages. At each stage, more details about the data model design are determined and documented. You could think of the conceptual design as the overall data as seen by the end user, the logical design as the data as seen by the DBMS, and the physical design as the data as seen by the operating system’s storage management devices.
It is important to note that the overwhelming majority of database designs and imple- mentations are based on the relational model, and therefore use the relational model constructs and techniques. When you finish the design activities, you will have a com- plete database design ready to be implemented.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
boundaries
The external limits to which any proposed system is subjected. These limits include budgets, personnel, and existing hardware and software.
Copyright 2017 Cengage Lea Editorial review has deemed that

Database design activities are covered in detail in Sections 9-4 (Conceptual Design), 9-5 (DBMS Software Selection), 9-6 (Logical Design), and 9-7 (Physical Design).
9-3c Implementation and Loading
The output of the database design phase is a series of instructions detailing the cre- ation of tables, attributes, domains, views, indexes, security constraints, and storage and performance guidelines. In this phase, you actually implement all these design specifications.
Install the DBMS This step is required only when a new dedicated instance of the DBMS is necessary for the system. In many cases, the organization will have made a particular DBMS the standard to leverage investments in the technology and the skills that employees have already developed. The DBMS may be installed on a new server or on existing servers. One current trend is called virtualization. Virtualization is a technique that creates logical representations of computing resources that are inde- pendent of the underlying physical computing resources. This technique is used in many areas of computing, such as the creation of virtual servers, virtual storage, and virtual private networks. In a database environment, database virtualization refers to
virtualization
A technique that creates logical representations of computing resources that are independent of the underlying physical computing resources.
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FIGURE 9.5 TWO VIEWS OF DATA: BUSINESS MANAGER AND DESIGNER
Engineering
Company
Purchasing
Shared information
Manufacturing
Manager’s view
What are the problems?
What are the solutions?
What information is needed to implement the solutions?
What data is required to
generate the desired information?
Designer’s view
How must the data be structured? How will the data be accessed?
How is the data transformed into information?
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e
s
a
b
a
t
a
D
C
o
m
p
a
n
y

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FIGURE9.6 DATABASEDESIGNPROCESS

+

* See Chapter 12, Distributed Database Management Systems
+ See Chapter 11, Database Performance Tuning and Query Optimization
Two
Online
Content
appendixes at
the installation of a new instance of the DBMS on a virtual server running on shared hardware. This is normally a task that involves system and network administrators to create appropriate user groups and services in the server configuration and network routing. Another common trend is the use of cloud database services such Microsoft SQL Database Service or Amazon Relational Database Services (RDS). This new gen- eration of services allows user to create databases that could be easily managed, tested, and scaled up as needed.
Create the Database(s) In most modern relational DBMSs, a new database imple- mentation requires the creation of special storage-related constructs to house the end- user tables. The constructs usually include the storage group (or file groups), the table spaces, and the tables. Figure 9.7 shows that a storage group can contain more than one table space and that a table space can contain more than one table.
For example, the implementation of the logical design in IBM’s DB2 would require the following:
1. The system administrator (SYSADM) would create the database storage group. This step is mandatory for such mainframe databases as DB2. Other DBMS software may create equivalent storage groups automatically when a database is created. (See Step 2.) Consult your DBMS documentation to see if you must create a storage group, and if so, what the command syntax must be.
2. The SYSADM creates the database within the storage group.
3. TheSYSADMassignstherightstousethedatabasetoadatabaseadministrator(DBA).
4. The DBA creates the table space(s) within the database.
www.cengagebrain.com
provide a concise exam- ple of simple real-world database development: Appendix B, The Uni- versity Lab: Conceptual Design, and Appendix C, The University Lab: Con- ceptual Design Verifica- tion, Logical Design, and Implementation.
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FIGURE9.7 PHYSICALORGANIZATIONOFADB2DATABASEENVIRONMENT
Table Table space
Table
Table
Table Table
Table space
Table space
Table space
Table Table
Table space
Storage group Database
5. The DBA creates the table(s) within the table space(s).
6. The DBA assigns access rights to the table spaces and to the tables within specified table spaces. Access rights may be limited to views rather than to whole tables. The creation of views is not required for database access in the relational environment, but views are desirable from a security standpoint. For example, using the following command, access rights to a table named PROFESSOR may be granted to the user Lynn Eilers, whose identification code is LEILERS:
GRANT SELECT ON PROFESSOR TO USER LEILERS;
Load or Convert the Data After the database has been created, the data must be loaded into the database tables. Typically, the data will have to be migrated from the prior version of the system. Often, data to be included in the system must be aggre- gated from multiple sources. In a best-case scenario, all of the data will be in a relational database so that it can be readily transferred to the new database. However, in some cases data may have to be imported from other relational databases, nonrelational databases, flat files, legacy systems, or even manual paper-and-pencil systems. If the data format does not support direct importing into the new database, conversion programs may have to be created to reformat the data for importing. In a worst-case scenario, much of the data may have to be manually entered into the database. Once the data has been loaded, the DBA works with the application developers to test and evaluate the database.
Loading existing data into a cloud-based database service sometimes can be expen- sive. The reason for this is that most cloud services are priced based not only on the volume of data to be stored but also on the amount of data that travels over the network. In such cases, loading a 1 TB database could be a very expensive proposition. Therefore,
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system administrators must be very careful in reading and negotiating the terms of cloud service contracts to ensure that there will be no “hidden” costs.
9-3d Testing and Evaluation
In the design phase, decisions were made to ensure integrity, security, performance, and recoverability of the database. During implementation and loading, these plans were put into place. In testing and evaluation, the DBA tests and fine-tunes the database to ensure that it performs as expected. This phase occurs in conjunction with application program- ming. Programmers use database tools to prototype the applications during coding of the programs. Tools such as report generators, screen painters, and menu generators are especially useful to application programmers.
Test the Database During this step, the DBA tests the database to ensure that it maintains the integrity and security of the data. Data integrity is enforced by the DBMS through the proper use of primary and foreign key rules. Many DBMSs also support the creation of domain constraints and database triggers. Testing will ensure that these constraints were properly designed and implemented. Data integrity is also the result of properly implemented data management policies, which are part of a comprehensive data administration framework. For a more detailed study of this topic, see The DBA’s Managerial Role section in Chapter 16, Database Administration and Security.
Previously, users and roles were created to grant users access to the data. In this stage, not only must those privileges be tested, but the broader view of data privacy and secu- rity must be addressed. Data stored in the company database must be protected from access by unauthorized users. (It does not take much imagination to predict the likely results if students have access to a student database or if employees have access to payroll data!) Consequently, you must test for at least the following:
• Physical security allows only authorized personnel physical access to specific areas. Depending on the type of database implementation, however, establishing physical security might not always be practical. For example, a university student research database is not a likely candidate for physical security.
• Password security allows the assignment of access rights to specific authorized users. Password security is usually enforced at login time at the operating system level.
• Access rights can be established through the use of database software. The assignment of access rights may restrict operations (CREATE, UPDATE, DELETE, and so on) on predetermined objects such as databases, tables, views, queries, and reports.
• Audit trails are usually provided by the DBMS to check for access violations. Although the audit trail is an after-the-fact device, its mere existence can discourage unautho- rized use.
• Data encryption can render data useless to unauthorized users who might have violated some of the database security layers.
• Diskless workstations allow end users to access the database without being able to download the information from their workstations.
For a more detailed discussion of security issues, refer to Chapter 16, Database Administration and Security.
Fine-Tune the Database Database performance can be difficult to evaluate because there are no standards for measuring it, but it is typically one of the most important factors in database implementation. Different systems will place different performance
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requirements on the database. Systems that support rapid transactions will require the database to be implemented so that they provide superior performance during high vol- umes of inserts, updates, and deletes. Other systems, like decision support systems, may require superior performance for complex data retrieval tasks. Many factors can affect the database’s performance on various tasks, including the hardware and software envi- ronment in which the database exists. Naturally, the characteristics and volume of the data also affect database performance: a search of 10 tuples is faster than a search of 100,000 tuples. Other important factors in database performance include system and database configuration parameters such as data placement, access path definition, the use of indexes, and buffer size. For a more in-depth discussion of database performance issues, see Chapter 11, Database Performance Tuning and Query Optimization.
Evaluate the Database and Its Application Programs As the database and appli- cation programs are created and tested, the system must also be evaluated using a more holistic approach. Testing and evaluation of the individual components should culmi- nate in a variety of broader system tests to ensure that all of the components interact properly to meet the needs of the users. At this stage, integration issues and deployment plans are refined, user training is conducted, and system documentation is finalized. Once the system receives final approval, it must be a sustainable resource for the organi- zation. To ensure that the data contained in the database is protected against loss, backup and recovery plans are tested.
Timely data availability is crucial for almost every database. Unfortunately, the data- base can lose data through unintended deletions, power outages, and other causes. Data backup and recovery procedures create a safety valve, ensuring the availability of con- sistent data. Typically, database vendors encourage the use of fault-tolerant components such as uninterruptible power supply (UPS) units, RAID storage devices, clustered serv- ers, and data replication technologies to ensure the continuous operation of the database in case of a hardware failure. Even with these components, backup and restore functions constitute a very important part of daily database operations. Some DBMSs provide functions that allow the database administrator to schedule automatic database backups to permanent storage devices such as disks, DVDs, tapes, and online storage. Database backups can be performed at different levels:
• A full backup, or dump, of the entire database. In this case, all database objects are backed up in their entirety.
• A differential backup of the database, in which only the objects that have been updated or modified since the last full backup are backed up.
• A transaction log backup, which backs up only the transaction log operations that are not reflected in a previous backup copy of the database. In this case, no other database objects are backed up. (For a complete explanation of the transaction log, see Chapter 10, Transaction Management and Concurrency Control.)
The database backup is stored in a secure place, usually in a different building from the database itself, and is protected against dangers such as fire, theft, flood, and other potential calamities. The main purpose of the backup is to guarantee database restoration following a hardware or software failure.
Failures that plague databases and systems are generally induced by software, hard- ware, programming exemptions, transactions, or external factors. Table 9.1 summarizes the most common sources of database failure.
Depending on the type and extent of the failure, the recovery process ranges from a minor short-term inconvenience to a major long-term rebuild. Regardless of the extent of the required recovery process, recovery is not possible without a usable backup.
full backup (database dump)
A complete copy of an entire database saved and periodically updated in a separate location. A full backup ensures a full recovery of all data after a physical disaster or database integrity failure.
differential backup
A level of database backup in which only the last modifications to the database are copied.
transaction log backup
A backup of only
the transaction log operations that are not reflected in a previous backup copy of the database.
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TABLE 9.1
COMMON SOURCES OF DATABASE FAILURE
SOURCE
DESCRIPTION
EXAMPLE
Software
Software-induced failures may be traceable to the operating system, the DBMS software, application programs, or viruses and other malware.
In January 2015, a security vulnerability was found for Oracle E-Business Suite that could cause serious data compromise.4
Hardware
Hardware-induced failures may include memory chip errors, disk crashes, bad disk sectors, and disk-full errors.
A bad memory module or a multiple hard disk failure in a database system can bring it to an abrupt stop.
Programming exemptions
Application programs or end users may roll back transactions when certain conditions are defined. Programming exemptions can also be caused by malicious or improperly tested code that can be exploited by hackers.
Hackers constantly search for ways to exploit unprotected web database systems. For example, in February 2015, Anthem, the second largest health insurer, announced that it was hacked and data for 80 million customers might have been exposed.5
Transactions
The system detects deadlocks and aborts one of the transactions. (See Chapter 10.)
Deadlock occurs when executing multiple simultaneous transactions.
External factors
Backups are especially important when a system suffers complete destruction from fire, earthquake, flood, or other natural disaster.
In 2012, Hurricane Sandy hit the northeastern United States, causing data and service losses worth billions of dollars across multiple states.
Database recovery generally follows a predictable scenario. First, the type and extent of the required recovery are determined. If the entire database needs to be recovered to a consistent state, the recovery uses the most recent backup copy of the database in a known consistent state. The backup copy is then rolled forward to restore all subsequent transactions by using the transaction log information. If the database needs to be recov- ered but the committed portion of the database is still usable, the recovery process uses the transaction log to “undo” all of the transactions that were not committed (see Chap- ter 10, Transaction Management and Concurrency Control).
At the end of this phase, the database completes an iterative process of testing, evalu- ation, and modification that continues until the system is certified as ready to enter the operational phase.
9-3e Operation
Once the database has passed the evaluation stage, it is considered operational. At that point, the database, its management, its users, and its application programs constitute a complete information system.
The beginning of the operational phase invariably starts the process of system evolu- tion. As soon as all of the targeted end users have entered the operations phase, problems that could not have been foreseen during the testing phase begin to surface. Some of the problems are serious enough to warrant emergency “patchwork,” while others are merely minor annoyances. For example, if the database design is implemented to interface with the web, the sheer volume of transactions might cause even a well-designed system to
4“Oracle Patches Backdoor Vulnerability, Recommends Disabling SSL,” January 21, 2015. Url: https:// threatpost.com/oracle-patches-backdoor-vulnerability-recommends-disabling-ssl/110555
5 “Massive data hack of health insurer Anthem potentially exposes millions,” Fred Barbash and Abby Phillip, February 5, 2015, Washington Post. http://www.washingtonpost.com/blogs/the-switch/wp/2015/03/20/2015 -is-already-the-year-of-the-health-care-hack-and-its-only-going-to-get-worse/
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bog down. In that case, the designers have to identify the source of the bottleneck and produce alternative solutions. Those solutions may include using load-balancing soft- ware to distribute the transactions among multiple computers, increasing the available cache for the DBMS, and so on. The demand for change is the designer’s constant con- cern, which leads to phase 6, maintenance and evolution.
9-3f Maintenance and Evolution
The database administrator must be prepared to perform routine maintenance activities
within the database. Some of the required periodic maintenance activities include:
• Preventive maintenance (backup)
• Corrective maintenance (recovery)
• Adaptive maintenance (enhancing performance, adding entities and attributes, and so on)
• Assignment of access permissions and their maintenance for new and old users
• Generation of database access statistics to improve the efficiency and usefulness of
system audits and to monitor system performance
• Periodic security audits based on the system-generated statistics
• Monthly, quarterly, or yearly system usage summaries for internal billing or budget- ing purposes
The likelihood of new information requirements and the demand for additional reports and new query formats require application changes and possible minor changes in the database components and contents. These changes can be easily implemented only when the database design is flexible and when all documentation is updated and online. Eventually, even the best-designed database environment will no longer be capable of incorporating such evolutionary changes, and then the whole DBLC process begins anew.
As you can see, many of the activities described in the DBLC are similar to those in the SDLC. This should not be surprising because the SDLC is the framework within which the DBLC activities take place. A summary of the parallel activities that occur within the SDLC and DBLC is shown in Figure 9.8.
9-4 Conceptual Design
Recall that the second phase of the DBLC is database design, which comprises three stages: conceptual design, logical design, and physical design, plus the critical decision of DBMS selection. Conceptual design is the first stage in the database design process. The goal at this stage is to design a database that is independent of database software and physical details. The output of this process is a conceptual data model that describes the main data entities, attributes, relationships, and constraints of a given problem domain. This design is descriptive and narrative in form. In other words, it is generally composed of a graphical representation as well as textual descriptions of the main data elements, relationships, and constraints.
In this stage, data modeling is used to create an abstract database structure that rep- resents real-world objects in the most realistic way possible. The conceptual model must embody a clear understanding of the business and its functional areas. At this level of abstraction, the type of hardware and database model to be used might not have been identified yet. Therefore, the design must be software- and hardware-independent so that the system can be set up within any platform chosen later.
conceptual design
A process that uses data- modeling techniques
to create a model of a database structure that represents real-world objects as realistically
as possible. The design is both software- and hardware-independent.
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FIGURE9.8 PARALLELACTIVITIESINTHEDBLCANDTHESDLC
System design
System implementation
DBLC
Database initial study
Database design
Implementation and loading
Testing and evaluation
Conceptual Logical Physical
Creation Loading Fine-tuning
Operation
SDLC
Analysis
Detailed design
Coding
Testing and evaluation
Application program maintenance
Screens Reports Procedures
Prototyping
Debugging
Database maintenance and evolution
Keep in mind the following minimal data rule:
All that is needed is there, and all that is there is needed.
In other words, make sure that all data needed is in the model and that all data in the model is needed. All data elements required by the database transactions must be defined in the model, and all data elements defined in the model must be used by at least one database transaction.
However, as you apply the minimal data rule, avoid excessive short-term bias. Focus not only on the immediate data needs of the business but on future data needs. Thus, the database design must leave room for future modifications and additions, ensuring that the business’s investment in information resources will endure.
The conceptual design has four steps, which are listed in Table 9.2.
TABLE 9.2
CONCEPTUAL DESIGN STEPS
STEP
ACTIVITY
1
Data analysis and requirements
2
Entity relationship modeling and normalization
3
Data model verification
4
Distributed database design
The following sections cover these steps in more detail.
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minimal data rule
Defined as “All that is needed is there, and all that is there is needed.” In other words, all data elements required by database transactions must be defined in the model, and all data elements defined in the model must be used by at least one database transaction.
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9-4a Data Analysis and Requirements
The first step in conceptual design is to discover the characteristics of the data ele- ments. An effective database is an information factory that produces key ingredients for successful decision making. Appropriate data element characteristics are those that can be transformed into appropriate information. Therefore, the designer’s efforts are focused on:
• Information needs. What kind of information is needed? That is, what output (reports and queries) must be generated by the system, what information does the current system generate, and to what extent is that information adequate?
• Information users. Who will use the information? How is the information to be used? What are the various end-user data views?
• Information sources. Where is the information to be found? How is the information to be extracted once it is found?
• Information constitution. What data elements are needed to produce the information? What are the data attributes? What relationships exist in the data? What is the data volume? How frequently is the data used? What data transformations will be used to generate the required information?
The designer obtains the answers to those questions from a variety of sources to compile the necessary information:
• Developing and gathering end-user data views. The database designer and the end user(s) jointly develop a precise description of end-user data views, which in turn are used to help identify the database’s main data elements.
• Directly observing the current system: existing and desired output. The end user usually has an existing system in place, whether it is manual or computer-based. The designer reviews the existing system to identify the data and its characteristics. The designer examines the input forms and files (tables) to discover the data type and volume. If the end user already has an automated system in place, the designer carefully examines the current and desired reports to describe the data required to support the reports.
• Interfacing with the systems design group. As noted earlier in this chapter, the database design process is part of the SDLC. In some cases, the systems analyst in charge of designing the new system will also develop the conceptual database model. (This is usually true in a decentralized environment.) In other cases, the database design is considered part of the DBA’s job. The presence of a DBA usually implies the existence of a formal data-processing department. The DBA designs the database according to the specifications created by the systems analyst.
To develop an accurate data model, the designer must have a thorough understanding of the company’s data types and their extent and uses. But data does not, by itself, yield the required understanding of the total business. From a database point of view, the collection of data becomes meaningful only when business rules are defined. Remember from Chapter 2, Data Models, that a business rule is a brief and precise description of a policy, procedure, or principle within a specific organization’s environment. Business rules, derived from a detailed description of an organization’s operations, help to create and enforce actions within that organization’s environment. When business rules are written properly, they define entities, attributes, relationships, connectivities, cardinali- ties, and constraints.
To be effective, business rules must be easy to understand, and they must be widely disseminated to ensure that every person in the organization shares a common
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interpretation of the rules. Using simple language, business rules describe the main and distinguishing characteristics of the data as viewed by the company. Examples of business rules are as follows:
• A customer may make many payments on an account.
• Each payment on an account is credited to only one customer.
• A customer may generate many invoices.
• Each invoice is generated by only one customer.
Given their critical role in database design, business rules must not be established casually. Poorly defined or inaccurate business rules lead to database designs and imple- mentations that fail to meet the needs of the organization’s end users.
Ideally, business rules are derived from a formal description of operations, which is a document that provides a precise, up-to-date, and thoroughly reviewed description of the activities that define an organization’s operating environment. (To the database designer, the operating environment is both the data sources and the data users.) Naturally, an organization’s operating environment is dependent on the organization’s mission. For example, the operating environment of a university would be quite different from that of a steel manufacturer, an airline, or a nursing home. Yet, no matter how different the organizations may be, the data analysis and requirements component of the database design is enhanced when the data envi- ronment and data use are described accurately and precisely within a description of operations.
In a business environment, the main sources of information for the description of operations—and therefore of business rules—are company managers, policymakers, department managers, and written documentation such as company procedures, stan- dards, and operations manuals. A faster and more direct source of business rules is direct interviews with end users. Unfortunately, because perceptions differ, the end user can be a less reliable source when it comes to specifying business rules. For example, a mainte- nance department mechanic might believe that any mechanic can initiate a maintenance procedure, when actually only mechanics with inspection authorization should perform such a task. This distinction might seem trivial, but it has major legal consequences. Although end users are crucial contributors to the development of business rules, it pays to verify end-user perceptions. Often, interviews with several people who perform the same job yield very different perceptions of their job components. While such a discov- ery might point to “management problems,” that general diagnosis does not help the database designer. Given the discovery of such problems, the database designer’s job is to reconcile the differences and verify the results of the reconciliation to ensure that the business rules are appropriate and accurate.
Knowing the business rules enables the designer to fully understand how the busi- ness works and what role the data plays within company operations. Consequently, the designer must identify the company’s business rules and analyze their impact on the nature, role, and scope of data.
Business rules yield several important benefits in the design of new systems:
• They help standardize the company’s view of data.
• They constitute a communications tool between users and designers.
• They allow the designer to understand the nature, role, and scope of the data.
• They allow the designer to understand business processes.
• They allow the designer to develop appropriate relationship participation rules and foreign key constraints. See Chapter 4, Entity Relationship (ER) Modeling.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
description of operations
A document that provides a precise, detailed, up-to-date, and thoroughly reviewed description of the activities that define an organization’s operating environment.
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The last point is especially noteworthy: whether a given relationship is mandatory or optional is usually a function of the applicable business rule.
9-4b Entity Relationship Modeling and Normalization
Before creating the ER model, the designer must communicate and enforce appropriate standards to be used in the documentation of the design. The standards include the use of diagrams and symbols, documentation writing style, layout, and any other conven- tions to be followed during documentation. Designers often overlook this very import- ant requirement, especially when they are working as members of a design team. Failure to standardize documentation often means a failure to communicate later, and commu- nications failures often lead to poor design work. In contrast, well-defined and enforced standards make design work easier and promise (but do not guarantee) a smooth inte- gration of all system components.
Because the business rules usually define the nature of the relationship(s) among the entities, the designer must incorporate them into the conceptual model. The process of defining business rules and developing the conceptual model using ER diagrams can be described using the steps shown in Table 9.3.6
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TABLE 9.3
DEVELOPING THE CONCEPTUAL MODEL USING ER DIAGRAMS
STEP
ACTIVITY
1
Identify, analyze, and refine the business rules.
2
Identify the main entities, using the results of Step 1.
3
Define the relationships among the entities, using the results of Steps 1 and 2.
4
Define the attributes, primary keys, and foreign keys for each of the entities.
5
Normalize the entities. (Remember that entities are implemented as tables in an RDBMS.)
6
Complete the initial ER diagram.
7
Validate the ER model against the end users’ information and processing requirements.
8
Modify the ER model, using the results of Step 7.
Some of the steps listed in Table 9.3 take place concurrently, and some, such as the normalization process, can generate a demand for additional entities and/or attributes, thereby causing the designer to revise the ER model. For example, while identifying two main entities, the designer might also identify the composite bridge entity that represents the many-to-many relationship between the two main entities.
To review, suppose that you are creating a conceptual model for the JollyGood Movie Rental Corporation, whose end users want to track customers’ DVD movie kiosk rentals. The simple ER diagram presented in Figure 9.9 shows a composite entity that helps track customers and their video rentals. Business rules define the optional nature of the relationships between the entities VIDEO and CUSTOMER. For exam- ple, customers are not required to check out a video. A video need not be checked out in order to exist in the kiosk. A customer may rent many videos, and a video may be rented by many customers. In particular, note the composite RENTAL entity that con- nects the two main entities.
6 See “Linking Rules to Models,” Alice Sandifer and Barbara von Halle, Database Programming and Design, 4(3), March 1991, pp. 13−16. Although the source seems dated, it remains the current standard. The tech- nology has changed substantially, but the process has not.
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FIGURE9.9 JOLLYGOODMOVIERENTALERD
As you will likely discover, the initial ER model may be subjected to several revi- sions before it meets the system’s requirements. Such a revision process is quite natural. Remember that the ER model is a communications tool as well as a design blueprint. Therefore, when you meet with the proposed system users, the initial ER model should give rise to questions such as “Is this really what you meant?” For example, the ERD shown in Figure 9.9 is far from complete. Clearly, many more attributes must be defined and the dependencies must be checked before the design can be implemented. In addi- tion, the design cannot yet support typical video rental transactions. For example, each video is likely to have many copies available for rental purposes. However, if the VIDEO entity shown in Figure 9.9 is used to store the titles as well as the copies, the design trig- gers the data redundancies shown in Table 9.4.
TABLE 9.4
DATA REDUNDANCIES IN THE VIDEO TABLE
VIDEO_ID
VIDEO_TITLE
VIDEO_COPY
VIDEO_CHG
VIDEO_DAYS
SF-12345FT-1
Adventures on Planet III
1
$1.09
1
SF-12345FT-2
Adventures on Planet III
2
$1.09
1
SF-12345FT-3
Adventures on Planet III
3
$1.09
1
WE-5432GR-1
TipToe Canoe and Tyler 2: A Journey
1
$1.09
2
WE-5432GR-2
TipToe Canoe and Tyler 2: A Journey
2
$1.09
2
The initial ERD shown in Figure 9.9 must be modified to reflect the answer to the question “Is more than one copy available for each title?” Also, payment transactions must be supported. (You will have an opportunity to modify this initial design in Prob- lem 5 at the end of the chapter.)
From the preceding discussion, you might get the impression that ER modeling activ- ities such as entity and attribute definition, normalization, and verification take place in a precise sequence. In fact, once you have completed the initial ER model, chances are that you will move back and forth among the activities until you are satisfied that the ER model accurately represents a database design that can meet the required system demands. The activities often take place in parallel, and the process is iterative. Figure 9.10 summarizes the ER modeling interactions. Figure 9.11 summarizes the array of design tools and infor- mation sources that the designer can use to produce the conceptual model.
All objects (entities, attributes, relations, views, and so on) are defined in a data dictio- nary, which is used in tandem with the normalization process to help eliminate data anom- alies and redundancy problems. During this ER modeling process, the designer must:
• Define entities, attributes, primary keys, and foreign keys. (The foreign keys serve as the basis for the relationships among the entities.)
• Make decisions about adding new primary key attributes to satisfy end-user and processing requirements.
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Chapter 9 Database Design 463
FIGURE9.10 ERMODELINGISANITERATIVEPROCESSBASEDONMANYACTIVITIES
Database initial study
DBLC processes and database transactions
Data analysis User views and business rules
Initial ER model
Normalization
Final ER model
Verification
Attributes
FIGURE9.11 CONCEPTUALDESIGNTOOLSANDINFORMATIONSOURCES
Information sources
Business rules and data constraints
Data flow diagrams (DFD)*
Process functional descriptions (FD)* (user views)
Design tools
ER diagram
Normalization
Data dictionary
Conceptual model
ERD
Definition and validation
* Output generated by the systems analysis and design activities
• • •
Make decisions about the treatment of composite and multivalued attributes. Make decisions about adding derived attributes to satisfy processing requirements. Make decisions about the placement of foreign keys in 1:1 relationships.
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• Avoid unnecessary ternary relationships.
• Draw the corresponding ER diagram.
• Normalize the entities.
• Include all data element definitions in the data dictionary.
• Make decisions about standard naming conventions.
The naming conventions requirement is important, yet it is frequently ignored at the designer’s risk. Real database design is generally accomplished by teams. Therefore, it is important to ensure that team members work in an environment in which naming standards are defined and enforced. Proper documentation is crucial to the successful completion of the design, and adherence to the naming conventions serves database designers well. In fact, a common refrain from users seems to be: “I didn’t know why you made such a fuss over naming conventions, but now that I’m doing this stuff for real, I’ve become a true believer.”
9-4c Data Model Verification
Data model verification is one of the last steps in the conceptual design stage, and it is one of the most critical. In this step, the ER model must be verified against the proposed system processes to corroborate that they can be supported by the database model. Veri- fication requires that the model be run through a series of tests against:
• End-user data views
• All required transactions: SELECT, INSERT, UPDATE, and DELETE operations
• Access rights and security
• Business-imposed data requirements and constraints
Because real-world database design is generally done by teams, the database design is probably divided into major components known as modules. A module is an infor- mation system component that handles a specific business function, such as inventory, orders, or payroll. Under these conditions, each module is supported by an ER segment that is a subset or fragment of an enterprise ER model. Working with modules accom- plishes several important ends:
• The modules (and even the segments within them) can be delegated to design groups within teams, greatly speeding up the development work.
• The modules simplify the design work. The large number of entities within a complex design can be daunting. Each module contains a more manageable number of entities.
• The modules can be prototyped quickly. Implementation and application program- ming trouble spots can be identified more readily. Quick prototyping is also a great confidence builder.
• Even if the entire system cannot be brought online quickly, the implementation of one or more modules will demonstrate that progress is being made and that at least part of the system is ready to begin serving the end users.
As useful as modules are, they represent a loose collection of ER model fragments that could wreak havoc in the database if left unchecked. For example, the ER model fragments:
• Might present overlapping, duplicated, or conflicting views of the same data
• Might not be able to support all processes in the system’s modules
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module
(1) A design
segment that can be implemented as an autonomous unit, and is sometimes linked to produce a system. (2) An information system component that handles a specific function, such as inventory, orders, or payroll.
Copyright 2017 Cengage Lea Editorial review has deemed that

To avoid these problems, it is better if the modules’ ER fragments are merged into a single enterprise ER model. This process starts by selecting a central ER model segment and iteratively adding more ER model segments one at a time. At each stage, for each new entity added to the model, you need to validate that the new entity does not overlap or conflict with a previously identified entity in the enterprise ER model.
Merging the ER model segments into an enterprise ER model triggers a careful reeval- uation of the entities, followed by a detailed examination of the attributes that describe those entities. This process serves several important purposes:
• The emergence of the attribute details might lead to a revision of the entities them- selves. Perhaps some of the components first believed to be entities will instead turn out to be attributes within other entities. Or, a component that was originally con- sidered an attribute might turn out to contain a sufficient number of subcomponents to warrant the introduction of one or more new entities.
• The focus on attribute details can provide clues about the nature of relationships as they are defined by the primary and foreign keys. Improperly defined relationships lead to implementation problems first and to application development problems later.
• To satisfy processing and end-user requirements, it might be useful to create a new primary key to replace an existing primary key. For example, in the example illus- trated in Figure 9.9, a surrogate primary key (RENTAL_ID) could be introduced to replace the original primary key composed of VIDEO_ID and CUST_NUM.
• Unless the entity details (the attributes and their characteristics) are precisely defined, it is difficult to evaluate the extent of the design’s normalization. Knowledge of the normalization levels helps guard against undesirable redundancies.
• A careful review of the rough database design blueprint is likely to lead to revi- sions. Those revisions will help ensure that the design is capable of meeting end-user requirements.
After finishing the merging process, the resulting enterprise ER model is verified against each of the module’s processes. The ER model verification process is detailed in Table 9.5.
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TABLE 9.5
THE ER MODEL VERIFICATION PROCESS
STEP
ACTIVITY
1
Identify the ER model’s central entity.
2
Identify each module and its components.
3
Identify each module’s transaction requirements: Internal: updates/inserts/deletes/queries/reports External: module interfaces
4
Verify all processes against system requirements.
5
Make all necessary changes suggested in Step 4.
6
Repeat Steps 2–5 for all modules.
Keep in mind that this process requires the continuous verification of business trans- actions as well as system and user requirements. The verification sequence must be repeated for each of the system’s modules. Figure 9.12 illustrates the iterative nature of the process.
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FIGURE9.12 ITERATIVEERMODELVERIFICATIONPROCESS
Identify central entity, modules, and components
Define processes and transaction steps
Verify ER model
Does ER Yes require changes?
No
ER model verified
Make changes to ER model
cohesivity
The strength of the relationships between a module’s components. Module cohesivity must be high.
module coupling
The extent to which modules are independent of one another.
The verification process starts with selecting the central (most important) entity, which is the focus for most of the system’s operations.
To identify the central entity, the designer selects the entity involved in the greatest number of the model’s relationships. In the ER diagram, it is the entity with more lines connected to it than any other.
The next step is to identify the module or subsystem to which the central entity belongs and to define that module’s boundaries and scope. The entity belongs to the module that uses it most frequently. Once each module is identified, the central entity is placed within the module’s framework to let you focus on the module’s details.
Within the central entity/module framework, you must
• Ensure the module’s cohesivity. The term cohesivity describes the strength of the relation- ships found among the module’s entities. A module must display high cohesivity—that is, the entities must be strongly related, and the module must be complete and self-sufficient.
• Analyze each module’s relationships with other modules to address module coupling. Module coupling describes the extent to which modules are independent of one another. Modules must display low coupling, indicating that they are independent of other modules. Low coupling decreases unnecessary intermodule dependencies, thereby allowing the creation of a truly modular system and eliminating unnecessary relationships among entities.
Processes may be classified according to their:
• Frequency (daily, weekly, monthly, yearly, or exceptions)
• Operational type (INSERT or ADD, UPDATE or CHANGE, DELETE, queries and reports, batches, maintenance, and backups)
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All identified processes must be verified against the ER model. If necessary, appropri- ate changes are implemented. The process verification is repeated for all of the model’s modules. You can expect that additional entities and attributes will be incorporated into the conceptual model during its validation.
At this point, a conceptual model has been defined as hardware- and software- independent. Such independence ensures the system’s portability across platforms. Por- tability can extend the database’s life by making it possible to migrate to another DBMS and hardware platform.
9-4d Distributed Database Design
Although not a requirement for most databases, some may need to be distributed among multiple geographical locations. Processes that access the database may also vary from one location to another. For example, a retail process and a warehouse storage process are likely to be found in different physical locations. If the database data and processes will be distributed across the system, portions of a database, known as database frag- ments, may reside in several physical locations. A database fragment is a subset of a database that is stored at a given location. The database fragment may be a subset of rows or columns from one or multiple tables.
Distributed database design defines the optimum allocation strategy for database fragments to ensure database integrity, security, and performance. The allocation strat- egy determines how to partition the database and where to store each fragment. The design implications introduced by distributed processes are examined in detail in Chap- ter 12, Distributed Database Management Systems.
9-5 DBMS Software Selection
The selection of DBMS software is critical to the information system’s smooth operation. Consequently, the advantages and disadvantages of the proposed DBMS software should be carefully studied. To avoid false expectations, the end user must be made aware of the limitations of both the DBMS and the database.
Although the factors that affect the purchasing decision vary from company to com- pany, some of the most common are:
• Cost. This includes the original purchase price, along with maintenance, operational, license, installation, training, and conversion costs.
• DBMS features and tools. Some database software includes a variety of tools that facilitate application development. For example, the availability of query by example (QBE), screen painters, report generators, application generators, and data dictionaries helps to create a more pleasant work environment for both the end user and the application programmer. Database administrator facili- ties, query facilities, ease of use, performance, security, concurrency control, transaction processing, and third-party support also influence DBMS software selection.
• Underlying model. This can be hierarchical, network, relational, object/relational, or object-oriented.
• Portability. A DBMS can be portable across platforms, systems, and languages.
• DBMS hardware requirements. Items to consider include processor(s), RAM, disk
space, and so on.
database fragment
A subset of a distributed database. Although the fragments may be stored at different sites within
a computer network, the set of all fragments is treated as a single database.
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9-6 Logical Design
Logical design is the second stage in the database design process. The logical design goal is to design an enterprise-wide database that is based on a specific data model but independent of physical-level details. Logical design requires that all objects in the con- ceptual model be mapped to the specific constructs used by the selected database model. For example, the logical design for a relational DBMS includes the specifications for the relations (tables), relationships, and constraints (in other words, domain definitions, data validations, and security views).
The logical design is generally performed in four steps, which are listed in Table 9.6.
TABLE 9.6
LOGICAL DESIGN STEPS
STEP
ACTIVITY
1
Map the conceptual model to logical model components.
2
Validate the logical model using normalization.
3
Validate the logical model integrity constraints.
4
Validate the logical model against user requirements.
Such steps, like most of the data-modeling process, are not necessarily performed sequentially, but in an iterative fashion. The following sections cover these steps in more detail.
9-6a Map the Conceptual Model to the Logical Model
The first step in creating the logical design is to map the conceptual model to the cho- sen database constructs. Because this book deals primarily with relational databases, and because most current database design projects are based on the relational database model, this section focuses on logical design using relational constructs. In the real world, logical design generally involves translating the ER model into a set of relations (tables), columns, and constraint definitions. The process of translating the conceptual model into a set of relations is depicted in Table 9.7.
TABLE 9.7
MAPPING THE CONCEPTUAL MODEL TO THE RELATIONAL MODEL
STEP
ACTIVITY
1
Map strong entities.
2
Map supertype/subtype relationships.
3
Map weak entities.
4
Map binary relationships.
5
Map higher-degree relationships.
logical design
A stage in the design phase that matches the conceptual design to
the specific constructs of the selected DBMS and
is therefore software- dependent. Logical design is used to translate the conceptual design into the internal model for a selected database management system, such as DB2, SQL Server, Oracle, IMS, Informix, Access, or Ingress.
Remember, the steps indicated in Table 9.7 are not sequential but iterative. The exam- ple of the Simple College ER model shown in Figure 9.13 illustrates this process.
As indicated in Table 9.7, the first step in the logical design stage is to map strong entities to tables. Recall from Chapter 4 that a strong entity is one that resides in the “1”
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FIGURE 9.13 THE SIMPLE COLLEGE CONCEPTUAL MODEL
side of all its relationships—that is, an entity that does not have a mandatory attribute that is a foreign key to another table. Therefore, the first entities to be translated into tables would be the EMPLOYEE and COURSE entities. In this case, you define the table name, its columns, and their characteristics. For example, the relation definitions for the strong entities of Simple College would be:
COURSE (CRS_CODE, CRS_TITLE, CRS_DESCRIPT, CRS_CREDIT) PRIMARY KEY: CRS_CODE
EMPLOYEE (EMP_NUM, EMP_LNAME, EMP_FNAME, EMP_INITIAL, EMP_E_MAIL) PRIMARY KEY: EMP_NUM
Once all strong entities are mapped, you are ready to map any entities involved in a supertype/subtype relationship or any weak entities. In the case of Simple College, the PROFESSOR entity is a subtype of the EMPLOYEE entity. PROFESSOR is also a weak entity because it inherits its primary key from EMPLOYEE and is existence-dependent on EMPLOYEE. At this point, you could also start defining the relationships between supertype and subtype entities. For example:
PROFESSOR (EMP_NUM, PROF_SPECIALTY, PROF_RANK) PRIMARY KEY: EMP_NUM
FOREIGN KEY: EMP_NUM REFERENCES EMPLOYEE
Next, you start mapping all binary relationships. In the previous example, you defined the supertype/subtype relationship between EMPLOYEE and PROFESSOR. This is an instance that demonstrates the iterative nature of the process. Continuing with the Sim- ple College ER model, you would define the CLASS relation and define its 1:M relation- ships with PROFESSOR and COURSE:
CLASS (CLASS_CODE, EMP_NUM, CLASS_TIME, CLASS_DAYS, CRS_CODE)
PRIMARY KEY: CLASS_CODE
FOREIGN KEYS: EMP_NUM REFERENCES PROFESSOR
CRS_CODE REFERENCES COURSE
Next, you will proceed with all relationships between three or more entities until all relationships in the model are clearly defined. The logical design’s tables must cor- respond to the entities (EMPLOYEE, PROFESSOR, COURSE, and CLASS) shown in
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the conceptual design of Figure 9.13, and the table columns must correspond to the attributes specified in the conceptual design. The final outcome of this process is a list of relations, attributes, and relationships that will be the basis for the next step.
9-6b Validate the Logical Model Using Normalization
The logical design should contain only properly normalized tables. The process of map- ping the conceptual model to the logical model may unveil some new attributes or the discovery of new multivalued or composite attributes. Therefore, it’s very likely that new attributes may be added to tables, or that entire new tables may be added to the logical model. For each identified table (old and new), you must ensure that all attributes are fully dependent on the identified primary key and that the tables are in at least third normal form (3NF).
As indicated throughout this section, database design is an iterative process. Activities such as normalization take place at different stages in the design process. Each time you reiterate a step, the model is further refined and better documented. New attributes may be created and assigned to the proper entities. Functional dependencies among deter- minant and dependent attributes are evaluated and data anomalies are prevented via normalization.
9-6c Validate Logical Model Integrity Constraints
The translation of the conceptual model into a logical model also requires definition of the attribute domains and appropriate constraints. For example, the domain definitions for the CLASS_CODE, CLASS_DAYS, and CLASS_TIME attributes displayed in the CLASS entity in Figure 9.13 are written this way:
CLASS_CODE
CLASS_DAYS
CLASS_TIME
is a valid class code.
Type: numeric
Range: low value=1000 high value=9999 Display format: 9999
Length: 4
is a valid day code.
Type: character
Display format: XXX
Valid entries: MWF, TR, M, T, W, R, F, S Length: 3
is a valid time.
Type: character
Display format: 99:99 (24-hour clock) Display range: 06:00 to 22:00
Length: 5
All these defined constraints must be supported by the logical data model. In this stage, you must map these constraints to the proper relational model constraints. For example, the CLASS_DAYS attribute is character data that should be restricted to a list of valid character combinations. Here, you define this attribute to have a CHECK IN constraint to enforce that the only allowed values are “MWF”, “TR”, “M”, “T”, “W”, “R”, “F”, and “S”.
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During this step, you also define which attributes are mandatory and which are optional, and ensure that all entities maintain entity and referential integrity.
The right to use the database is also specified during the logical design phase. Who will be allowed to use the tables, and what portions of the tables will be available to which users? Within a relational framework, the answers to those questions require the defini- tion of appropriate views. For example, a given process may require the creation of the following view to get data about the class schedules:
CREATE VIEW vSCHEDULE AS
SELECT FROM WHERE
EMP_LNAME, EMP_FNAME, CLASS_CODE, CRS_TITLE, CLASS_TIME, CLASS_DAYS PROFESSOR, CLASS, COURSE
PROFESSOR.EMP_NUM = CLASS.EMP_NUM AND
CLASS.CRS_CODE = COURSE.CRS_CODE
Special attention is needed at this stage to ensure that all views can be resolved and that security is enforced to ensure the privacy of the data. Additionally, if you are working with a distributed database design, data could be stored at multiple locations, and each location may have different security restrictions. After validating the logical model integrity constraints, you are ready to validate the model against the end-user requirements.
9-6d ValidatetheLogicalModelAgainstUserRequirements
The logical design translates the software-independent conceptual model into a soft- ware-dependent model. The final step in the logical design process is to validate all logical model definitions against all end-user data, transaction, and security require- ments. A process similar to the one depicted in Table 9.5 takes place again to ensure the correctness of the logical model. The stage is now set to define the physical requirements that allow the system to function within the selected DBMS/hardware environment.
9-7 Physical Design
Physical design is the process of determining the data storage organization and data access characteristics of the database to ensure its integrity, security, and performance. This is the last stage in the database design process. The storage characteristics are a func- tion of the types of devices supported by the hardware, the type of data access methods supported by the system, and the DBMS. Physical design can become a very technical job that affects not only the accessibility of the data in the storage device(s) but the per- formance of the system.
The physical design stage consists of the steps in Table 9.8.
physical design
A stage of database design that maps
the data storage and access characteristics
of a database. Because these characteristics are a function of the types of devices supported
by the hardware, the data access methods supported by the system physical design are both hardware- and software- dependent. See also physical model.
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TABLE 9.8
PHYSICAL DESIGN STEPS
STEP
ACTIVITY
1
Define data storage organization.
2
Define integrity and security measures.
3
Determine performance measurements.
The following sections cover these steps in more detail.
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9-7a Define Data Storage Organization
Before you can define data storage organization, you must determine the volume of data to be managed and the data usage patterns.
• Knowing the data volume will help you determine how much storage space to reserve for the database. To do this, the designer follows a process similar to the one used during ER model verification. For each table, identify all possible transactions, their frequency, and volume. For each transaction, you determine the amount of data to be added or deleted from the database. This information will help you determine the amount of data to be stored in the related table.
• Conversely, knowing how frequently new data is inserted, updated, and retrieved will help the designer determine the data usage patterns. Usage patterns are critical, particularly in distributed database design. For example, are there any weekly batch uploads or monthly aggregation reports to be generated? How frequently is new data added to the system?
Equipped with the two previous pieces of information, the designer must:
• Determine the location and physical storage organization for each table. As you saw in Section 9-3c, tables are stored in table spaces, and a table space can hold data from multiple tables. In this step, the designer assigns which tables will use which table spaces, and assigns the location of the table spaces. For example, a useful technique available in most relational databases is the use of clustered tables. The clustered tables storage technique stores related rows from two related tables in adjacent data blocks on disk. This ensures that the data is stored in sequentially adjacent locations, thereby reducing data access time and increasing system performance.
• Identify indexes and the type of indexes to be used for each table. As you saw in previous chapters, indexes are useful for ensuring the uniqueness of data values in a column and to facilitate data lookups. You also know that the DBMS automatically creates a unique index for the primary key of each table. You will learn in Chapter 11 about the various types of index organization. In this step, you identify all required indexes and determine the best type of organization to use based on the data usage patterns and performance requirements.
• Identify the views and the type of views to be used on each table. As you learned in Chapter 8, a view is useful to limit access to data based on user or transaction needs. Views can also be used to simplify processing and end-user data access. In this step the designer must ensure that all views can be implemented and that they provide only the required data. The designer must also become familiar with the types of views supported by the DBMS and how they could help meet system goals.
9-7b Define Integrity and Security Measures
Once the physical organization of the tables, indexes, and views are defined, the database is ready for the end users. However, before users can access the data in the database, they must be properly authenticated. In this step of physical design, two tasks must be addressed:
• Define user and security groups and roles. User management is more a function of database administration than database design. However, as a designer you must know the different types of users and groups of users to properly enforce database security. Most DBMS implementations support the use of database roles. A database role is a set of database privileges that could be assigned as a unit to a user or group. For exam- ple, you could define an Advisor role that has Read access to the vSCHEDULE view.
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clustered table
A storage technique that stores related rows from two related tables in adjacent data blocks on disk.
database role
A set of database privileges that could be assigned as a unit to a user or group.
Copyright 2017 Cengage Lea Editorial review has deemed that

• Assign security controls. The DBMS also allows administrators to assign specific access rights for database objects to a user or group of users. For example, you could assign the SELECT and UPDATE access rights to the user leilers on the CLASS table. An access right could also be revoked from a specific user or groups of users. This fea- ture could come in handy during database backups, scheduled maintenance events, or even during data breach incidents.
9-7c Determine Performance Measures
Physical design becomes more complex when data is distributed at different locations because the performance is affected by the communication media’s throughput. Given such complexities, it is not surprising that designers favor database software that hides as many of the physical-level activities as possible. Despite the fact that relational models tend to hide the complexities of the computer’s physical characteristics, the performance of relational databases is affected by physical storage properties. For example, perfor- mance can be affected by characteristics of the storage media, such as seek time, sector and block (page) size, buffer pool size, and the number of disk platters and read/write heads. In addition, factors such as the creation of an index can have a considerable effect on the relational database’s performance—that is, data access speed and efficiency.
In summary, physical design performance measurement deals with fine-tuning the DBMS and queries to ensure that they will meet end-user performance requirements.
Note
The preceding sections have separated the discussions of logical and physical design activities. In fact, logical and physical design can be carried out in parallel, on a table-by-table basis. Such parallel activities require the designer to have a thor- ough understanding of the software and hardware to take full advantage of their characteristics.
9-8 Database Design Strategies There are two classical approaches to database design:
• Top-down design starts by identifying the data sets and then defines the data ele- ments for each of those sets. This process involves the identification of different entity types and the definition of each entity’s attributes.
• Bottom-up design first identifies the data elements (items) and then groups them together in data sets. In other words, it first defines attributes, and then groups them to form entities.
The two approaches are illustrated in Figure 9.14. Selecting a primary emphasis on top- down or bottom-up procedures often depends on the scope of the problem or on personal preferences. Although the two methodologies are complementary rather than mutually exclusive, a primary emphasis on a bottom-up approach may be more productive for small databases with few entities, attributes, relations, and transactions. For situations in which the number, variety, and complexity of entities, relations, and transactions is overwhelming,
Online Content
Physical design is par- ticularly important in the older hierarchical and network models described in Appen- dixes K and L, The Hier- archical Database Model and The Network Data- base Model, respec- tively. Both appendixes are available at www. cengagebrain.com.
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top-down design
A design philosophy that begins by defining the main structures of a system and then moves to define the smaller units within those structures. In database design, this process first identifies entities and then defines the attributes within the entities.
bottom-up design
A design philosophy that begins by identifying individual design components and then aggregates them into larger units. In database design, the process begins by defining attributes and then groups them into entities.
For a detailed discussion of database performance and query optimization techniques that could be used, see Chapter 11, Database Performance Tuning and Query Optimization.
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a primarily top-down approach may be easier. Most companies have standards for systems development and database design already in place.
FIGURE 9.14 TOP-DOWN VS. BOTTOM-UP DESIGN SEQUENCING
Conceptual model
TB oo pt
Entity
Attribute Attribute Attribute Attribute
t
o m
p
D
o
w nU
Entity
Note
9-9 Centralized Versus Decentralized Design
The two general approaches to database design (bottom-up and top-down) can be influ- enced by factors such as the scope and size of the system, the company’s management style, and the company’s structure (centralized or decentralized). Depending on these factors, the database design may be based on two very different design philosophies: centralized and decentralized.
Centralized design is productive when the data component has a relatively small number of objects and procedures. The design can be carried out and represented in a fairly simple database. Centralized design is typical of relatively simple, small databases and can be successfully done by a single database administrator or by a small, infor- mal design team. The company operations and the scope of the problem are sufficiently limited to allow even a single designer to define the problem(s), create the conceptual design, verify the conceptual design with the user views, define system processes and data constraints to ensure the efficacy of the design, and ensure that the design will com- ply with all the requirements. (Although centralized design is typical for small compa- nies, do not make the mistake of assuming that it is limited to small companies. Even large companies can operate within a relatively simple database environment.) Figure 9.15 summarizes the centralized design option. Note that a single conceptual design is completed and then validated in the centralized design approach.
Decentralized design might be used when the system’s data component has a con- siderable number of entities and complex relations on which very complex operations are
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Even when a primarily top-down approach is selected, the normalization process that revises existing table structures is inevitably a bottom-up technique. ER models con- stitute a top-down process even when the selection of attributes and entities can be described as bottom-up. Because both the ER model and normalization techniques form the basis for most designs, the top-down versus bottom-up debate may be based on a theoretical distinction rather than an actual difference.
centralized design
A process by which
all database design decisions are carried out centrally by a
small group of people. Suitable in a top-down design approach
when the problem domain is relatively small, as in a single unit or department in an organization.
decentralized design
A process in which conceptual design models subsets of an organization’s database requirements, which are then aggregated into a complete design. Such modular designs are typical of complex systems with a relatively large number of objects and procedures.
Copyright 2017 Cengage Lea Editorial review has deemed that

performed. Decentralized design is also often used when the problem itself is spread across several operational sites and each element is a subset of the entire data set. (See Figure 9.16.)
In large and complex projects, the database typically cannot be designed by only one person. Instead, a carefully selected team of database designers tackles a complex data- base project. Within the decentralized design framework, the database design task is divided into several modules. Once the design criteria have been established, the lead designer assigns design subsets or modules to design groups within the team.
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FIGURE9.15 CENTRALIZEDDESIGN
Conceptual model
Conceptual model verification
User views System processes Data constraints
Data dictionary
FIGURE9.16 DECENTRALIZEDDESIGN
Conceptual models
Verification
Engineering
Views Processes Constraints
Data component
Purchasing
Views Processes Constraints
Aggregation
Conceptual model
Data dictionary
Submodule criteria
Manufacturing
Views Processes Constraints

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Because each design group focuses on modeling a subset of the system, the definition of boundaries and the interrelation among data subsets must be very precise. Each design group creates a conceptual data model corresponding to the subset being modeled. Each conceptual model is then verified individually against the user views, processes, and con- straints for each of the modules. After the verification process has been completed, all modules are integrated into one conceptual model. Because the data dictionary describes the characteristics of all objects within the conceptual data model, it plays a vital role in the integration process. After the subsets have been aggregated into a larger conceptual model, the lead designer must verify that it still can support all of the required transactions.
Keep in mind that the aggregation process requires the designer to create a single model in which various aggregation problems must be addressed. (See Figure 9.17.)
• Synonyms and homonyms. Various departments might know the same object by different names (synonyms), or they might use the same name to address different objects (homonyms). The object can be an entity, an attribute, or a relationship.
• Entity and entity subtypes. An entity subtype might be viewed as a separate entity by one or more departments. The designer must integrate such subtypes into a higher-level entity.
• Conflicting object definitions. Attributes can be recorded as different types (character, numeric), or different domains can be defined for the same attribute. Constraint defi- nitions can vary as well. The designer must remove such conflicts from the model.
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FIGURE9.17 SUMMARYOFAGGREGATIONPROBLEMS
Entity X
Entity X Entity Y
Synonyms: Two departments use different names for the same entity. Label used:
Department A X Department B Y
Homonyms: Two different entities are addressed by the same label. (Department B uses the label X to describe both entity X and entity Y.)
Entity and entity subclass: The entities X1 and X2 are subsets of entity X. Example:
Label used: X
X
Entity X1 Department A
EMPLOYEE
SECRETARY
Typing speed Classification
Entity X
Entity X2 Department B
Name Address Phone
PILOT
Hours flown License
Common attributes
Distinguishing attributes
Conflicting object definitions: Attributes for the entity PROFESSOR
Payroll Dept. Conflicting Primary key: PROF_SSN
definitions Phone attribute: 898-2853
Systems Dept. PROF_NUM 2853

Summary
• An information system is designed to help transform data into information and to manage both data and information. Thus, the database is a very important part of the information system. Systems analysis is the process that establishes the need for an information system and its extent. Systems development is the process of creating an information system.
• The Systems Development Life Cycle (SDLC) traces the history of an application within the information system. The SDLC can be divided into five phases: planning, analysis, detailed systems design, implementation, and maintenance. The SDLC is an iterative process rather than a sequential process.
• The Database Life Cycle (DBLC) describes the history of the database within the infor- mation system. The DBLC is composed of six phases: database initial study, database design, implementation and loading, testing and evaluation, operation, and main- tenance and evolution. Like the SDLC, the DBLC is iterative rather than sequential.
• The conceptual portion of the design may be subject to several variations based on two basic design philosophies: bottom-up versus top-down and centralized versus decentralized.
Key Terms
bottom-up design
boundaries
centralized design
clustered tables
cohesivity
computer-aided software engineering (CASE)
conceptual design database development database fragment
Database Life Cycle (DBLC) database role decentralized design description of operations differential backup
full backup information system logical design minimal data rule module
module coupling
physical design
scope
systems analysis
systems development
Systems Development Life Cycle (SDLC)
top-down design transaction log backup virtualization
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
Review Questions
1. What is an information system? What is its purpose?
2. How do systems analysis and systems development fit into a discussion about infor- mation systems?
3. What does the acronym SDLC mean, and what does an SDLC portray?
4. What does the acronym DBLC mean, and what does a DBLC portray?
5. Discuss the distinction between centralized and decentralized conceptual database design.
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6. What is the minimal data rule in conceptual design? Why is it important?
7. Discuss the distinction between top-down and bottom-up approaches in database design.
8. What are business rules? Why are they important to a database designer?
9. What is the data dictionary’s function in database design?
10. WhatstepsarerequiredinthedevelopmentofanERdiagram?(Hint:SeeTable9.3.)
11. List and briefly explain the activities involved in the verification of an ER model.
12. What factors are important in a DBMS software selection?
13. List and briefly explain the four steps performed during the logical design stage.
14. List and briefly explain the three steps performed during the physical design stage.
15. Whatthreelevelsofbackupmaybeusedindatabaserecoverymanagement?Briefly describe what each backup level does.
Problems
1. The ABC Car Service & Repair Centers are owned by the Silent Car Dealership; ABC services and repairs only silent cars. Three ABC centers provide service and repair for the entire state.
Each of the three centers is independently managed and operated by a shop manager, a receptionist, and at least eight mechanics. Each center maintains a fully stocked parts inventory.
Each center also maintains a manual file system in which each car’s maintenance history is kept: repairs made, parts used, costs, service dates, owner, and so on. Files are also kept to track inventory, purchasing, billing, employees’ hours, and payroll.
You have been contacted by one of the center’s managers to design and implement a computerized database system. Given the preceding information, do the following:
a. Indicate the most appropriate sequence of activities by labeling each of the following steps in the correct order. (For example, if you think that “Load the database” is the appropriate first step, label it “1.”)
Normalize the conceptual model.
Obtain a general description of company operations. Load the database.
Create a description of each system process.
Test the system.
Draw a data flow diagram and system flowcharts. Create a conceptual model using ER diagrams.
Create the application programs. Interview the mechanics. Create the file (table) structures. Interview the shop manager.
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b. Describe the various modules that you believe the system should include.
c. How will a data dictionary help you develop the system? Give examples.
d. What general (system) recommendations might you make to the shop manager? For example, if the system will be integrated, what modules will be integrated? What benefits would be derived from such an integrated system? Include several general recommendations.
e. What is the best approach to conceptual database design? Why?
f. Name and describe at least four reports the system should have. Explain their use. Who will use those reports?
2. Suppose that you have been asked to create an information system for a manufac- turing plant that produces nuts and bolts of many shapes, sizes, and functions. What questions would you ask, and how would the answers affect the database design?
a. What do you envision the SDLC to be?
b. What do you envision the DBLC to be?
3. Suppose that you perform the same functions noted in Problem 2 for a larger ware- housing operation. How are the two sets of procedures similar? How and why are they different?
4. Using the same procedures and concepts employed in Problem 1, how would you create an information system for the Tiny College example in Chapter 4?
5. Write the proper sequence of activities for the design of a video rental database. (The initial ERD was shown in Figure 9.9.) The design must support all rental activities, customer payment tracking, and employee work schedules, as well as track which employees checked out the videos to the customers. After you finish writing the design activity sequence, complete the ERD to ensure that the database design can be successfully implemented. (Make sure that the design is normalized properly and that it can support the required transactions.)
6. In a construction company, a new system has been in place for a few months and now there is a list of possible changes/updates that need to be done. For each of the changes/updates, specify what type of maintenance needs to be done: (a) corrective, (b) adaptive, and (c) perfective.
a. An error in the size of one of the fields has been identified and it needs to be updated status field needs to be changed.
b. The company is expanding into a new type of service and this will require to enhancing the system with a new set of tables to support this new service and integrate it with the existing data.
c. The company has to comply with some government regulations. To do this, it will require adding a couple of fields to the existing system tables.
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7. You have been assigned to design the database for a new soccer club. Indicate the most appropriate sequence of activities by labeling each of the following steps in the correct order. (For example, if you think that “Load the database” is the appropriate first step, label it “1.”)
Create the application programs.
Create a description of each system process.
Test the system.
Load the database.
Normalize the conceptual model.
Interview the soccer club president.
Create a conceptual model using ER diagrams.
Interview the soccer club director of coaching.
Create the file (table) structures.
Obtain a general description of the soccer club operations. Draw a data flow diagram and system flowcharts.
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PART 4
Advanced Database Concepts
10 Transaction Management and Concurrency Control 11 Database Performance Tuning and Query Optimization 12 Distributed Database Management Systems
13 Business Intelligence and Data Warehouses
14 Big Data Analytics and NoSQL
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Chapter 10
Transaction Management and Concurrency Control
In this chapter, you will learn:
• About database transactions and their properties
• What concurrency control is and what role it plays in maintaining the database’s integrity • What locking methods are and how they work
• How stamping methods are used for concurrency control
• How optimistic methods are used for concurrency control
• How database recovery management is used to maintain database integrity
Preview
Database transactions reflect real-world transactions that are triggered by events such as buying a product, registering for a course, or making a deposit into a checking account. Transactions are likely to contain many parts, such as updating a customer’s account, adjusting product inventory, and updating the seller’s accounts receivable. All parts of a transaction must be successfully completed to prevent data integrity problems. Therefore, executing and managing transactions are important database system activities.
In this chapter you will learn about the main properties of database transactions (ato- micity, consistency, isolation, and durability, plus serializability for concurrent trans- actions). After defining the transaction properties, the chapter shows how SQL can be used to represent transactions, and how transaction logs can ensure the DBMS’s ability to recover transactions.
When many transactions take place at the same time, they are called concurrent trans- actions. Managing the execution of such transactions is called concurrency control. This chapter discusses some of the problems that can occur with concurrent transactions (lost updates, uncommitted data, and inconsistent retrievals) and the most common algo- rithms for concurrency control: locks, time stamping, and optimistic methods. Finally, you will see how database recovery management can ensure that a database’s contents are restored to a valid consistent state in case of a hardware or software failure.
Data Files and Available Formats
CH10_SaleCo P P P P CH10_ABC_Markets P P P P Data Files Available on cengagebrain.com
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
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Chapter 10 Transaction Management and Concurrency Control 483 10-1 What Is a Transaction?
To illustrate what transactions are and how they work, use the Ch10_SaleCo database. The relational diagram for the database is shown in Figure 10.1.
Note
FIGURE 10.1 THE CH10_SALECO DATABASE RELATIONAL DIAGRAM
Although SQL commands illustrate several transaction and concurrency control issues, you should be able to follow the discussions even if you have not studied Chapter 7, Intro- duction to Structured Query Language (SQL), and Chapter 8, Advanced SQL. If you don’t know SQL, ignore the SQL commands and focus on the discussions. If you have a working knowledge of SQL, you can use the Ch10_SaleCo database to generate your own SELECT and UPDATE examples and to augment the material in Chapters 7 and 8 by writing your own triggers and stored procedures.
As you examine the relational diagram in Figure 10.1, note the following features:
• The design stores the customer balance (CUST_BALANCE) value in the CUSTOMER table to indicate the total amount owed by the customer. The CUST_BALANCE attri- bute is increased when the customer makes a purchase on credit, and it is decreased when the customer makes a payment. Including the current customer account bal- ance in the CUSTOMER table makes it easy to write a query to determine the current balance for any customer and to generate important summaries such as total, average, minimum, and maximum balances.
• The ACCT_TRANSACTION table records all customer purchases and payments to track the details of customer account activity.
You could change the design of the Ch10_SaleCo database to reflect accounting practice more precisely, but the implementation provided here will enable you to track the transactions well enough to understand the chapter’s discussions.
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484 Part 4
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To understand the concept of a transaction, suppose that you sell a product to a customer. Furthermore, suppose that the customer may charge the purchase to his or her account. Given that scenario, your sales transaction consists of at least the following parts:
• You must write a new customer invoice.
• You must reduce the quantity on hand in the product’s inventory.
• You must update the account transactions.
• You must update the customer balance.
The preceding sales transaction must be reflected in the database. In database terms, a transaction is any action that reads from or writes to a database. A transaction may consist of the following:
• A simple SELECT statement to generate a list of table contents.
• A series of related UPDATE statements to change the values of attributes in various
tables.
• A series of INSERT statements to add rows to one or more tables.
• A combination of SELECT, UPDATE, and INSERT statements.
The sales transaction example includes a combination of INSERT and UPDATE statements.
Given the preceding discussion, you can augment the definition of a transaction. A transaction is a logical unit of work that must be entirely completed or entirely aborted; no intermediate states are acceptable. In other words, a multicomponent transaction, such as the previously mentioned sale, must not be partially completed. Updating only the inven- tory or only the accounts receivable is not acceptable. All of the SQL statements in the transaction must be completed successfully. If any of the SQL statements fail, the entire transaction is rolled back to the original database state that existed before the transaction started. A successful transaction changes the database from one consistent state to another. A consistent database state is one in which all data integrity constraints are satisfied.
To ensure consistency of the database, every transaction must begin with the database in a known consistent state. If the database is not in a consistent state, the transaction will yield an inconsistent database that violates its integrity and business rules. For that reason, subject to limitations discussed later, all transactions are controlled and executed by the DBMS to guarantee database integrity.
Most real-world database transactions are formed by two or more database requests. A database request is the equivalent of a single SQL statement in an application pro- gram or transaction. For example, if a transaction is composed of two UPDATE state- ments and one INSERT statement, the transaction uses three database requests. In turn, each database request generates several input/output (I/O) operations that read from or write to physical storage media.
10-1a Evaluating Transaction Results
Not all transactions update the database. Suppose that you want to examine the CUSTOMER table to determine the current balance for customer number 10016. Such a transaction can be completed by using the following SQL code:
SELECT FROM WHERE
CUST_NUMBER, CUST_BALANCE CUSTOMER
CUST_NUMBER = 10016;
Although the query does not make any changes in the CUSTOMER table, the SQL code represents a transaction because it accesses the database. If the database existed in
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
transaction
A sequence of database requests that accesses the database. A transaction is a logical unit of work; that is,
it must be entirely completed or aborted— no intermediate ending states are accepted.
All transactions must have the properties of atomicity, consistency, isolation, and durability.
consistent database state
A database state in which all data integrity constraints are satisfied.
database request
The equivalent of a single SQL statement in an application program or a transaction.
Copyright 2017 Cengage Lea Editorial review has deemed that

a consistent state before the access, the database remains in a consistent state after the access because the transaction did not alter the database.
Remember that a transaction may consist of a single SQL statement or a collection of related SQL statements. Revisit the previous sales example to illustrate a more complex transaction, using the Ch10_SaleCo database. Suppose that on January 18, 2016, you register the credit sale of one unit of product 89-WRE-Q to customer 10016 for $277.55. The required transaction affects the INVOICE, LINE, PRODUCT, CUSTOMER, and ACCT_TRANSACTION tables. The SQL statements that represent this transaction are as follows:
INSERT INTO INVOICE
VALUES (1009, 10016,’18-Jan-2016′, 256.99, 20.56, 277.55, ‘cred’, 0.00, 277.55);
INSERT INTO LINE
VALUES (1009, 1, ’89-WRE-Q’, 1, 256.99, 256.99);
UPDATE SET WHERE
UPDATE SET WHERE
PRODUCT
PROD_QOH = PROD_QOH – 1 PROD_CODE = ’89-WRE-Q’;
CUSTOMER
CUST_BALANCE = CUST_BALANCE + 277.55 CUST_NUMBER = 10016;
Chapter 10 Transaction Management and Concurrency Control 485
INSERT INTO ACCT_TRANSACTION
VALUES (10007, ’18-Jan-16′, 10016, ‘charge’, 277.55); COMMIT;
The results of the successfully completed transaction are shown in Figure 10.2. (All records involved in the transaction are outlined in red.)
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FIGURE10.2 TRACINGTHETRANSACTIONINTHECH10_SALECODATABASE
Database name: Ch10_SaleCo Table name: INVOICE Table name: LINE
Table name: PRODUCT
Table name: CUSTOMER Table name: ACCT_TRANSACTION

486 Part 4
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Note
To better understand the transaction results, note the following:
• A new row 1009 was added to the INVOICE table. In this row, derived attribute values were stored for the invoice subtotal, the tax, the invoice total, and the invoice balance.
• The LINE row for invoice 1009 was added to reflect the purchase of one unit of prod- uct 89-WRE-Q with a price of $256.99. In this row, the derived attribute values for the line amount were stored.
• Product 89-WRE-Q’s quantity on hand (PROD_QOH) in the PRODUCT table was reduced by one, from 12 to 11.
• The customer balance (CUST_BALANCE) for customer 10016 was updated by adding $277.55 to the existing balance (the initial value was $0.00).
• A new row was added to the ACCT_TRANSACTION table to reflect the new account transaction number 10007.
• The COMMIT statement was used to end a successful transaction. (See Section 10-1c.)
Now suppose that the DBMS completes the first three SQL statements. Further- more, suppose that during the execution of the fourth statement (the UPDATE of the CUSTOMER table’s CUST_BALANCE value for customer 10016), the computer system loses electrical power. If the computer does not have a backup power supply, the transaction cannot be completed. Therefore, the INVOICE and LINE rows were added, and the PROD- UCT table was updated to represent the sale of product 89-WRE-Q, but customer 10016 was not charged, nor was the required record written in the ACCT_TRANSACTION table. The database is now in an inconsistent state, and it is not usable for subsequent transac- tions. Assuming that the DBMS supports transaction management, the DBMS will roll back the database to a previous consistent state.
Although the DBMS is designed to recover a database to a previous consistent
By default, MS Access does not support transaction management as discussed here. More sophisticated DBMSs, such as Oracle, SQL Server, and DB2, support the transaction man- agement components discussed in this chapter. MS Access supports transaction manage- ment though specialized application programing interfaces (API) such as the Workspace or the DBEngine objects of the Data Access Objects (DAO) database middleware (see Chapter 15, Database Connectivity and Web Technologies for more information.)
state when an interruption prevents the completion of a transaction, the transaction itself is defined by the end user or programmer and must be semantically correct. The DBMS cannot guarantee that the semantic meaning of the transaction truly represents the real-world event. For example, suppose that following the sale of 10 units of product 89-WRE-Q, the inventory UPDATE commands were written this way:
UPDATE SET WHERE
PRODUCT
PROD_QOH = PROD_QOH + 10 PROD_CODE = ’89-WRE-Q’;
The sale should have decreased the PROD_QOH value for product 89-WRE-Q by 10. Instead, the UPDATE added 10 to product 89-WRE-Q’s PROD_QOH value.
Although the UPDATE command’s syntax is correct, its use yields incorrect results,
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that is, a database inconsistent with the real-world event. Yet, the DBMS will execute the transaction anyway. The DBMS cannot evaluate whether the transaction represents the real-world event correctly; that is the end user’s responsibility. End users and program- mers are capable of introducing many errors in this fashion. Imagine the consequences of reducing the quantity on hand for product 1546-QQ2 instead of product 89-WRE-Q or of crediting the CUST_BALANCE value for customer 10012 rather than customer 10016.
Clearly, improper or incomplete transactions can have a devastating effect on database integrity. Some DBMSs—especially the relational variety—provide means by which the user can define enforceable constraints based on business rules. Other integrity rules, such as those governing referential and entity integrity, are enforced automatically by the DBMS when the table structures are properly defined, thereby letting the DBMS validate some transactions. For example, if a transaction inserts a new customer number into a customer table and the number already exists, the DBMS will end the transaction with an error code to indicate a violation of the primary key integrity rule.
10-1b Transaction Properties
Each individual transaction must display atomicity, consistency, isolation, and durability. These four properties are sometimes referred to as the ACID test. Let’s look briefly at each of the properties.
• Atomicity requires that all operations (SQL requests) of a transaction be completed; if not, the transaction is aborted. If a transaction T1 has four SQL requests, all four requests must be successfully completed; otherwise, the entire transaction is aborted. In other words, a transaction is treated as a single, indivisible, logical unit of work.
• Consistency indicates the permanence of the database’s consistent state. A trans- action takes a database from one consistent state to another. When a transaction is completed, the database must be in a consistent state. If any of the transaction parts violates an integrity constraint, the entire transaction is aborted.
• Isolation means that the data used during the execution of a transaction cannot be used by a second transaction until the first one is completed. In other words, if trans- action T1 is being executed and is using the data item X, that data item cannot be accessed by any other transaction (T2 … Tn) until T1 ends. This property is particu- larly useful in multiuser database environments because several users can access and update the database at the same time.
• Durability ensures that once transaction changes are done and committed, they can- not be undone or lost, even in the event of a system failure.
In addition to the individual transaction properties indicated above, there is another important property that applies when executing multiple transactions concurrently. For example, let’s assume that the DBMS has three transactions (T1, T2 and T3) executing at the same time. To properly carry out transactions, the DBMS must schedule the concur- rent execution of the transaction’s operations. In this case, each individual transaction must comply with the ACID properties and, at the same time, the schedule of such mul- tiple transaction operations must exhibit the property of serializability. Serializability ensures that the schedule for the concurrent execution of the transactions yields consis- tent results. This property is important in multiuser and distributed databases in which multiple transactions are likely to be executed concurrently. Naturally, if only a single transaction is executed, serializability is not an issue.
atomicity
The transaction property that requires all parts of a transaction to be treated as a single, indivisible, logical unit of work. All parts of a transaction must be completed or the entire transaction is aborted.
consistency
A database condition in which all data integrity constraints are satisfied. To ensure consistency of a database, every transaction must begin with the database in a known consistent state. If not, the transaction will yield an inconsistent database that violates its integrity and business rules.
isolation
A database transaction property in which a data item used by
one transaction is
not available to other transactions until the first one ends.
durability
The transaction property that ensures that once transaction changes are done and committed, they cannot be undone or lost, even in the event of a system failure.
serializability
A property in which the selected order of concurrent transaction operations creates the same final database state that would have been produced if the transactions had been executed in a serial fashion.
Chapter 10 Transaction Management and Concurrency Control 487
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488 Part 4
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A single-user database system automatically ensures serializability and isolation of the database because only one transaction is executed at a time. The atomicity, consis- tency, and durability of transactions must be guaranteed by single-user DBMSs. (Even a single-user DBMS must manage recovery from errors created by OS-induced interrup- tions, power interruptions, and abnormal application terminations or crashes.)
Multiuser databases are typically subject to multiple concurrent transactions. There- fore, the multiuser DBMS must implement controls to ensure serializability and isolation of transactions—in addition to atomicity and durability—to guard the database’s consis- tency and integrity. For example, if several concurrent transactions are executed over the same data set and the second transaction updates the database before the first transac- tion is finished, the isolation property is violated and the database is no longer consistent. The DBMS must manage the transactions by using concurrency control techniques to avoid undesirable situations.
10-1c Transaction Management with SQL
The American National Standards Institute (ANSI) has defined standards that govern SQL database transactions. Transaction support is provided by two SQL statements: COMMIT and ROLLBACK. The ANSI standards require that when a transaction sequence is initiated by a user or an application program, the sequence must continue through all succeeding SQL statements until one of the following four events occurs:
• A COMMIT statement is reached, in which case all changes are permanently recorded within the database. The COMMIT statement automatically ends the SQL transaction.
• A ROLLBACK statement is reached, in which case all changes are aborted and the database is rolled back to its previous consistent state.
• The end of a program is successfully reached, in which case all changes are perma- nently recorded within the database. This action is equivalent to COMMIT.
• The program is abnormally terminated, in which case the database changes are aborted and the database is rolled back to its previous consistent state. This action is equivalent to ROLLBACK.
The use of COMMIT is illustrated in the following simplified sales example, which updates a product’s quantity on hand (PROD_QOH) and the customer’s balance when the customer buys two units of product 1558-QW1 priced at $43.99 per unit (for a total of $87.98) and charges the purchase to the customer’s account:
UPDATE SET WHERE UPDATE SET WHERE COMMIT;
PRODUCT
PROD_QOH = PROD_QOH – 2 PROD_CODE = ‘1558-QW1’;
CUSTOMER
CUST_BALANCE = CUST_BALANCE + 87.98 CUST_NUMBER = ‘10011’;
(Note that the example is simplified to make it easy to trace the transaction. In the Ch10_SaleCo database, the transaction would involve several additional table updates.)
The COMMIT statement used in the preceding example is not necessary if the UPDATE statement is the application’s last action and the application terminates nor- mally. However, good programming practice dictates that you include the COMMIT statement at the end of a transaction declaration.
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A transaction begins implicitly when the first SQL statement is encountered. Not all SQL implementations follow the ANSI standard; some (such as SQL Server) use trans- action management statements such as the following to indicate the beginning of a new transaction:
BEGIN TRANSACTION;
Other SQL implementations allow you to assign characteristics for the transactions as parameters to the BEGIN statement. For example, the Oracle RDBMS uses the SET TRANSACTION statement to declare the start of a new transaction and its properties.
10-1d The Transaction Log
A DBMS uses a transaction log to keep track of all transactions that update the data- base. The DBMS uses the information stored in this log for a recovery requirement triggered by a ROLLBACK statement, a program’s abnormal termination, or a system failure such as a network discrepancy or a disk crash. Some RDBMSs use the trans- action log to recover a database forward to a currently consistent state. After a server failure, for example, Oracle automatically rolls back uncommitted transactions and rolls forward transactions that were committed but not yet written to the physical database. This behavior is required for transactional correctness and is typical of any transactional DBMS.
While the DBMS executes transactions that modify the database, it also automatically updates the transaction log. The transaction log stores the following:
• A record for the beginning of the transaction.
• For each transaction component (SQL statement):
– The type of operation being performed (INSERT, UPDATE, DELETE).
– The names of the objects affected by the transaction (the name of the table).
– The “before” and “after” values for the fields being updated.
– Pointers to the previous and next transaction log entries for the same transaction.
• The ending (COMMIT) of the transaction.
Although using a transaction log increases the processing overhead of a DBMS, the ability to restore a corrupted database is worth the price.
Table 10.1 illustrates a simplified transaction log that reflects a basic transaction composed of two SQL UPDATE statements. If a system failure occurs, the DBMS will examine the transaction log for all uncommitted or incomplete transactions and restore (ROLLBACK) the database to its previous state on the basis of that information. When the recovery process is completed, the DBMS will write in the log all committed transac- tions that were not physically written to the database before the failure occurred.
If a ROLLBACK is issued before the termination of a transaction, the DBMS will restore the database only for that particular transaction, rather than for all of them, to maintain the durability of the previous transactions. In other words, committed transac- tions are not rolled back.
The transaction log is a critical part of the database, and it is usually implemented as one or more files that are managed separately from the actual database files. The trans- action log is subject to common dangers such as disk-full conditions and disk crashes. Because the transaction log contains some of the most critical data in a DBMS, some implementations support logs on several different disks to reduce the consequences of a system failure.
transaction log
A feature used by
the DBMS to keep
track of all transaction operations that update the database. The information stored in this log is used by the DBMS for recovery purposes.
Chapter 10 Transaction Management and Concurrency Control 489
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490 Part 4 Advanced Database Concepts
TABLE 10.1
A TRANSACTION LOG
TRL_ TRX_ ID NUM
PREV NEXT OPERATION TABLE ROW ID PTR PTR
ATTRIBUTE
BEFORE VALUE
AFTER VALUE
341
101
Null
352
START
****Start Transaction
352
101
341
363
UPDATE
PRODUCT
1558-QW1
PROD_QOH
25
23
363
101
352
365
UPDATE
CUSTOMER
10011
CUST_ BALANCE
525.75
615.73
365
101
363
Null
COMMIT
**** End of Transaction
TRL_ID = Transaction log record ID TRX_NUM = Transaction number
PTR = Pointer to a transaction log record ID
(Note: The transaction number is automatically assigned by the DBMS.)
10-2 Concurrency Control
concurrency control
A DBMS feature that coordinates the simultaneous execution of transactions in
a multiprocessing database system while preserving data integrity.
lost update
A concurrency control problem in which a data update is lost during the concurrent execution of transactions.
TABLE 10.2
Coordinating the simultaneous execution of transactions in a multiuser database system is known as concurrency control. The objective of concurrency control is to ensure the serializability of transactions in a multiuser database environment. To achieve this goal, most concurrency control techniques are oriented toward preserving the isolation prop- erty of concurrently executing transactions. Concurrency control is important because the simultaneous execution of transactions over a shared database can create several data integrity and consistency problems. The three main problems are lost updates, uncom- mitted data, and inconsistent retrievals.
10-2a Lost Updates
The lost update problem occurs when two concurrent transactions, T1 and T2, are updating the same data element and one of the updates is lost (overwritten by the other transaction). To see an illustration of lost updates, examine a simple PROD- UCT table. One of the table’s attributes is a product’s quantity on hand (PROD_ QOH). Assume that you have a product whose current PROD_QOH value is 35. Also assume that two concurrent transactions, T1 and T2, occur and update the PROD_QOH value for some item in the PRODUCT table. The transactions are shown in Table 10.2.
TWO CONCURRENT TRANSACTIONS TO UPDATE QOH
TRANSACTION COMPUTATION
Table 10.3 shows the serial execution of the transactions under normal circumstances, yielding the correct answer PROD_QOH = 105.
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T1: Purchase 100 units
PROD_QOH = PROD_QOH + 100
T2: Sell 30 units
PROD_QOH = PROD_QOH − 30

Chapter 10 Transaction Management and Concurrency Control 491
TABLE 10.3
SERIAL EXECUTION OF TWO TRANSACTIONS
TIME TRANSACTION STEP STORED VALUE
However, suppose that a transaction can read a product’s PROD_QOH value from the table before a previous transaction has been committed, using the same product. The sequence depicted in Table 10.4 shows how the lost update problem can arise. Note that the first transaction (T1) has not yet been committed when the second transaction (T2) is executed. Therefore, T2 still operates on the value 35, and its subtraction yields 5 in memory. In the meantime, T1 writes the value 135 to disk, which is promptly overwrit- ten by T2. In short, the addition of 100 units is “lost” during the process.
TABLE 10.4
LOST UPDATES
TIME TRANSACTION STEP STORED VALUE
10-2b Uncommitted Data
The phenomenon of uncommitted data occurs when two transactions, T1 and T2, are executed concurrently and the first transaction (T1) is rolled back after the second transaction (T2) has already accessed the uncommitted data—thus violating the iso- lation property of transactions. To illustrate that possibility, use the same transactions described during the lost updates discussion. T1 has two atomic parts, one of which is the update of the inventory; the other possible part is the update of the invoice total (not shown). T1 is forced to roll back due to an error during the updating of the invoice’s total; it rolls back all the way, undoing the inventory update as well. This time the T1 transac- tion is rolled back to eliminate the addition of the 100 units. (See Table 10.5.) Because T2 subtracts 30 from the original 35 units, the correct answer should be 5.
TABLE 10.5
TRANSACTIONS CREATING AN UNCOMMITTED DATA PROBLEM
TRANSACTION COMPUTATION
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1
T1
Read PROD_QOH
35
2
T1
PROD_QOH = 35 + 100
3
T1
Write PROD_QOH
135
4
T2
Read PROD_QOH
135
5
T2
PROD_QOH = 135 − 30
6
T2
Write PROD_QOH
105
1
T1
Read PROD_QOH
35
2
T2
Read PROD_QOH
35
3
T1
PROD_QOH = 35 + 100
4
T2
PROD_QOH = 35 − 30
5
T1
Write PROD_QOH (lost update)
135
6
T2
Write PROD_QOH
5
uncommitted data
A concurrency control problem in which a transaction accesses uncommitted data from another transaction.
T1: Purchase 100 units
PROD_QOH = PROD_QOH + 100 (Rolled back)
T2: Sell 30 units
PROD_QOH = PROD_QOH − 30

492 Part 4 Advanced Database Concepts
Table 10.6 shows how the serial execution of these transactions yields the correct
answer under normal circumstances.
TABLE 10.6
CORRECT EXECUTION OF TWO TRANSACTIONS
TIME TRANSACTION STEP STORED VALUE
1
T1
Read PROD_QOH
35
2
T1
PROD_QOH = 35 + 100
3
T1
Write PROD_QOH
135
4
T1
*****ROLLBACK *****
35
5
T2
Read PROD_QOH
35
6
T2
PROD_QOH = 35 − 30
7
T2
Write PROD_QOH
5
Table 10.7 shows how the uncommitted data problem can arise when the ROLLBACK is completed after T2 has begun its execution.
TABLE 10.7
AN UNCOMMITTED DATA PROBLEM
TIME TRANSACTION STEP STORED VALUE
1
T1
Read PROD_QOH
35
2
T1
PROD_QOH = 35 + 100
3
T1
Write PROD_QOH
135
4
T2
Read PROD_QOH (Read uncommitted data)
135
5
T2
PROD_QOH = 135 − 30
6
T1
***** ROLLBACK *****
35
7
T2
Write PROD_QOH
105
10-2c Inconsistent Retrievals
Inconsistent retrievals occur when a transaction accesses data before and after one or more other transactions finish working with such data. For example, an inconsistent retrieval would occur if transaction T1 calculated some summary (aggregate) function over a set of data while another transaction (T2) was updating the same data. The prob- lem is that the transaction might read some data before it is changed and other data after it is changed, thereby yielding inconsistent results.
To illustrate the problem, assume the following conditions:
1. T1 calculates the total quantity on hand of the products stored in the PRODUCT table.
2. At the same time, T2 updates the quantity on hand (PROD_QOH) for two of the PRODUCT table’s products.
The two transactions are shown in Table 10.8.
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inconsistent retrievals
A concurrency control problem that arises when a transaction- calculating summary (aggregate) functions over a set of data while other transactions are updating the data, yielding erroneous results.
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TABLE 10.8
RETRIEVAL DURING UPDATE
TRANSACTION T1 TRANSACTION T2
While T1 calculates the total quantity on hand (PROD_QOH) for all items, T2 rep- resents the correction of a typing error: the user added 10 units to product 1558-QW1’s PROD_QOH but meant to add the 10 units to product 1546-QQ2’s PROD_QOH. To correct the problem, the user adds 10 to product 1546-QQ2’s PROD_QOH and sub- tracts 10 from product 1558-QW1’s PROD_QOH. (See the two UPDATE statements in Table 10.8.) The initial and final PROD_QOH values are reflected in Table 10.9. (Only a few PROD_CODE values are shown for the PRODUCT table. To illustrate the point, the sum for the PROD_QOH values is shown for these few products.)
TABLE 10.9
TRANSACTION RESULTS: DATA ENTRY CORRECTION
BEFORE AFTER
Chapter 10 Transaction Management and Concurrency Control 493
SELECT SUM(PROD_QOH) FROM PRODUCT
UPDATE PRODUCT
SET PROD_QOH = PROD_QOH + 10 WHERE PROD_CODE = 1546-QQ2
UPDATE PRODUCT
SET PROD_QOH = PROD_QOH − 10 WHERE PROD_CODE = 1558-QW1
COMMIT;
PROD_CODE
PROD_QOH
PROD_QOH
11QER/31
8
8
13-Q2/P2
32
32
1546-QQ2
15
(15 + 10) 25
1558-QW1
23
(23 − 10) 13
2232-QTY
8
8
2232-QWE
6
6
Total
92
92
Although the final results shown in Table 10.9 are correct after the adjustment, Table 10.10 demonstrates that inconsistent retrievals are possible during the transaction execu- tion, making the result of T1’s execution incorrect. The “After” summation shown in Table 10.10 reflects that the value of 25 for product 1546-QQ2 was read after the WRITE state- ment was completed. Therefore, the “After” total is 40 + 25 = 65. The “Before” total reflects that the value of 23 for product 1558-QW1 was read before the next WRITE statement was completed to reflect the corrected update of 13. Therefore, the “Before” total is 65 + 23 = 88.
The computed answer of 102 is obviously wrong because you know from Table 10.9 that the correct answer is 92. Unless the DBMS exercises concurrency control, a multiuser database environment can create havoc within the information system.
10-2d The Scheduler
You now know that severe problems can arise when two or more concurrent transactions are executed. You also know that a database transaction involves a series of database I/O operations that take the database from one consistent state to another. Finally, you know
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494 Part 4 Advanced Database Concepts
TABLE 10.10
INCONSISTENT RETRIEVALS
TIME TRANSACTION ACTION VALUE TOTAL
1
T1
Read PROD_QOH for PROD_CODE = ’11QER/31′
8
8
2
T1
Read PROD_QOH for PROD_CODE = ’13-Q2/P2′
32
40
3
T2
Read PROD_QOH for PROD_CODE = ‘1546-QQ2’
15
4
T2
PROD_QOH = 15 + 10
5
T2
Write PROD_QOH for PROD_CODE = ‘1546-QQ2’
25
6
T1
Read PROD_QOH for PROD_CODE = ‘1546-QQ2’
25
(After) 65
7
T1
Read PROD_QOH for PROD_CODE = ‘1558-QW1’
23
(Before) 88
8
T2
Read PROD_QOH for PROD_CODE = ‘1558-QW1’
23
9
T2
PROD_QOH = 23 − 10
10
T2
Write PROD_QOH for PROD_CODE = ‘1558-QW1’
13
11
T2
***** COMMIT *****
12
T1
Read PROD_QOH for PROD_CODE = ‘2232-QTY’
8
96
13
T1
Read PROD_QOH for PROD_CODE = ‘2232-QWE’
6
102
that database consistency can be ensured only before and after the execution of transac- tions. A database always moves through an unavoidable temporary state of inconsistency during a transaction’s execution if such a transaction updates multiple tables and rows. (If the transaction contains only one update, then there is no temporary inconsistency.) The temporary inconsistency exists because a computer executes the operations serially, one after another. During this serial process, the isolation property of transactions pre- vents them from accessing the data not yet released by other transactions. This consider- ation is even more important today, with the use of multicore processors that can execute several instructions at the same time. What would happen if two transactions executed concurrently and they were accessing the same data?
In previous examples, the operations within a transaction were executed in an arbi- trary order. As long as two transactions, T1 and T2, access unrelated data, there is no conflict and the order of execution is irrelevant to the final outcome. However, if the transactions operate on related data or the same data, conflict is possible among the transaction components and the selection of one execution order over another might have some undesirable consequences. So, how is the correct order determined, and who determines that order? Fortunately, the DBMS handles that tricky assignment by using a built-in scheduler.
The scheduler is a special DBMS process that establishes the order in which the oper- ations are executed within concurrent transactions. The scheduler interleaves the exe- cution of database operations to ensure serializability and isolation of transactions. To determine the appropriate order, the scheduler bases its actions on concurrency control algorithms, such as locking or time stamping methods, which are explained in the next sections. However, it is important to understand that not all transactions are serializable. The DBMS determines what transactions are serializable and proceeds to interleave the execution of the transaction’s operations. Generally, transactions that are not serializable are executed on a first-come, first-served basis by the DBMS. The scheduler’s main job is to create a serializable schedule of a transaction’s operations, in which the interleaved execution of the transactions (T1, T2, T3, etc.) yields the same results as if the transac- tions were executed in serial order (one after another).
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scheduler
The DBMS component that establishes
the order in which concurrent transaction operations are executed. The scheduler interleaves the execution of database operations in
a specific sequence to ensure serializability.
serializable schedule
In transaction management, a schedule of operations in which the interleaved execution of the transactions yields the same result as if they were executed in serial order.
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Chapter 10 Transaction Management and Concurrency Control 495
The scheduler also makes sure that the computer’s central processing unit (CPU) and storage systems are used efficiently. If there were no way to schedule the execution of transactions, all of them would be executed on a first-come, first-served basis. The prob- lem with that approach is that processing time is wasted when the CPU waits for a READ or WRITE operation to finish, thereby losing several CPU cycles. In short, first-come, first-served scheduling tends to yield unacceptable response times within the multiuser DBMS environment. Therefore, some other scheduling method is needed to improve the efficiency of the overall system.
Additionally, the scheduler facilitates data isolation to ensure that two transactions do not update the same data element at the same time. Database operations might require READ and/or WRITE actions that produce conflicts. For example, Table 10.11 shows the possible conflict scenarios when two transactions, T1 and T2, are executed concurrently over the same data. Note that in Table 10.11, two operations are in conflict when they access the same data and at least one of them is a WRITE operation.
TABLE 10.11
READ/WRITE CONFLICT SCENARIOS: CONFLICTING DATABASE OPERATIONS MATRIX
TRANSACTIONS
Several methods have been proposed to schedule the execution of conflicting opera- tions in concurrent transactions. These methods are classified as locking, time stamping, and optimistic. Locking methods, discussed next, are used most frequently.
T1
T2
RESULT
Operations
Read
Read
No conflict
Read
Write
Conflict
Write
Read
Conflict
Write
Write
Conflict
10-3 Concurrency Control with Locking Methods
Locking methods are one of the most common techniques used in concurrency control because they facilitate the isolation of data items used in concurrently executing trans- actions. A lock guarantees exclusive use of a data item to a current transaction. In other words, transaction T2 does not have access to a data item that is currently being used by transaction T1. A transaction acquires a lock prior to data access; the lock is released (unlocked) when the transaction is completed so that another transaction can lock the data item for its exclusive use. This series of locking actions assumes that concurrent transactions might attempt to manipulate the same data item at the same time. The use of locks based on the assumption that conflict between transactions is likely is usually referred to as pessimistic locking.
Recall from Sections 10-1a and 10-1b that data consistency cannot be guaranteed during a transaction; the database might be in a temporary inconsistent state when sev- eral updates are executed. Therefore, locks are required to prevent another transaction from reading inconsistent data.
Most multiuser DBMSs automatically initiate and enforce locking procedures. All lock information is handled by a lock manager, which is responsible for assigning and policing the locks used by the transactions.
lock
A device that guarantees unique use of a data item in a particular transaction operation.
A transaction requires a lock prior to data access; the lock is released
after the operation’s execution to enable other transactions to lock the data item for their own use.
pessimistic locking
The use of locks based on the assumption that conflict between transactions is likely.
lock manager
A DBMS component that is responsible for assigning and releasing locks.
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10-3a Lock Granularity
Lock granularity indicates the level of lock use. Locking can take place at the following levels: database, table, page, row, or even field (attribute).
Database Level In a database-level lock, the entire database is locked, thus prevent- ing the use of any tables in the database by transaction T2 while transaction T1 is being executed. This level of locking is good for batch processes, but it is unsuitable for multiuser DBMSs. You can imagine how s-l-o-w data access would be if thousands of transactions had to wait for the previous transaction to be completed before the next one could reserve the entire database. Figure 10.3 illustrates the database-level lock; because of it, transactions T1 and T2 cannot access the same database concurrently even when they use different tables.
Table Level In a table-level lock, the entire table is locked, preventing access to any row by transaction T2 while transaction T1 is using the table. If a transaction requires access to several tables, each table may be locked. However, two transactions can access the same database as long as they access different tables.
Table-level locks, while less restrictive than database-level locks, cause traffic jams when many transactions are waiting to access the same table. Such a condition is espe- cially irksome if the lock forces a delay when different transactions require access to different parts of the same table—that is, when the transactions would not interfere with each other. Consequently, table-level locks are not suitable for multiuser DBMSs. Figure 10.4 illustrates the effect of a table-level lock. Note that transactions T1 and T2 cannot access the same table even when they try to use different rows; T2 must wait until T1 unlocks the table.
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lock granularity
The level of lock use. Locking can take place at the following levels: database, table, page, row, and field (attribute).
database-level lock
A type of lock that restricts database access to the owner of the lock and allows only one user at a time to access the database. This lock works for batch processes but is unsuitable for online multiuser DBMSs.
FIGURE10.3 DATABASE-LEVELLOCKINGSEQUENCE
Time
1 2 3 4 5 6 7 8 9
Transaction 1 (T1) (Update Table A)
Lock database request
Payroll Database
Table A
Table B
Transaction 2 (T2) (Update Table B)
Lock database request WAIT
Locked
Unlocked
OK
OK
Locked
Unlocked
table-level lock
A locking scheme
that allows only one transaction at a time
to access a table. A table-level lock locks an entire table, preventing access to any row by transaction T2 while transaction T1 is using the table.
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Chapter 10 Transaction Management and Concurrency Control 497
FIGURE10.4 ANEXAMPLEOFATABLE-LEVELLOCK
Time
1 2 3 4 5 6 7 8 9
Transaction 1 (T1) (Update row 5)
Payroll Database Table A
Transaction 2 (T2) (Update row 30)
Lock Table A request WAIT
OK Locked
Unlocked
(end of transaction 2)
Lock Table A request Locked
OK
Unlocked (end of transaction 1)
Page Level In a page-level lock, the DBMS locks an entire diskpage. A diskpage, or page, is the equivalent of a diskblock, which can be described as a directly addressable section of a disk. A page has a fixed size, such as 4K, 8K, or 16K. For example, if you want to write only 73 bytes to a 4K page, the entire 4K page must be read from disk, updated in memory, and written back to disk. A table can span several pages, and a page can contain several rows of one or more tables. Page-level locks are currently the most frequently used locking method for multiuser DBMSs. An example of a page-level lock is shown in Figure 10.5. Note that T1 and T2 access the same table while locking different diskpages. If T2 requires the use of a row located on a page that is locked by T1, T2 must wait until T1 unlocks the page.
page-level lock
In this type of lock, the database management system locks an entire diskpage, or section of
a disk. A diskpage can contain data for one or more rows and from one or more tables.
diskpage (page)
In permanent storage, the equivalent of a disk block, which can be described as a directly addressable section of
a disk. A diskpage has a fixed size, such as 4K, 8K, or 16K.
FIGURE10.5 ANEXAMPLEOFAPAGE-LEVELLOCK
Time
1 2 3 4 5 6 7
Transaction 1 (T1) (Update row 1)
Payroll Database
Table A
1
2 Page 1 3
4
5 Page 2 6
Transaction 2 (T2) (Update rows 5 and 2)
Lock page 2 request Locked
Lock page 1 request
WAIT
Locked
Lock page 1 request Locked
Unlock page 1 (end of transaction)
OK
OK
OK
Row number
Unlock pages 1 and 2
(end of transaction)
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Row Level A row-level lock is much less restrictive than the locks discussed earlier. The DBMS allows concurrent transactions to access different rows of the same table even when the rows are located on the same page. Although the row-level locking approach improves the availability of data, its management requires high overhead because a lock exists for each row in a table of the database involved in a conflicting transaction. Modern DBMSs automatically escalate a lock from a row level to a page level when the application session requests multiple locks on the same page. Figure 10.6 illustrates the use of a row-level lock.
Note in Figure 10.6 that both transactions can execute concurrently, even when the requested rows are on the same page. T2 must wait only if it requests the same row as T1.
Field Level The field-level lock allows concurrent transactions to access the same row as long as they require the use of different fields (attributes) within that row. Although field-level locking clearly yields the most flexible multiuser data access, it is rarely imple- mented in a DBMS because it requires an extremely high level of computer overhead and because the row-level lock is much more useful in practice.
10-3b LockTypes
Regardless of the level of granularity of the lock, the DBMS may use different lock types
or modes: binary or shared/exclusive.
Binary A binary lock has only two states: locked (1) or unlocked (0). If an object such as a database, table, page, or row is locked by a transaction, no other transaction can use that object. If an object is unlocked, any transaction can lock the object for its use. Every database operation requires that the affected object be locked. As a rule, a transaction must unlock the object after its termination. Therefore, every transaction requires a lock and unlock operation for each accessed data item. Such operations are automatically managed and scheduled by the DBMS; the user does not lock or unlock data items. (Every DBMS has a default-locking mechanism. If the end user wants to override the default settings, the LOCK TABLE command and other SQL commands are available for that purpose.)
The binary locking technique is illustrated in Table 10.12, using the lost update prob- lem you encountered in Table 10.4. Note that the lock and unlock features eliminate
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FIGURE10.6 ANEXAMPLEOFAROW-LEVELLOCK
Time Transaction 1 (T1) (Update row 1)
Payroll Database Table A
Transaction 2 (T2) (Update row 2)
Lock row 1 request 2
1
2 OK 3 4 5 6
Row number
1
3 4 5 6
Locked
Unlock row 1 (end of transaction)
Page 1 Page 2
OK
Lock row 2 request Locked
Unlock row 2 (end of transaction)
row-level lock
A less restrictive database lock in which the DBMS allows concurrent transactions to access different rows of the same table, even when the rows are on the same page.
field-level lock
A lock that allows concurrent transactions to access the same row as long as they require the use of different fields (attributes) within that row. This type of lock yields the most flexible multiuser data access but requires a high level of computer overhead.
binary lock
A lock that has only two states: locked (1) and unlocked (0). If a data item is locked by
a transaction, no other transaction can use that data item.
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Chapter 10 Transaction Management and Concurrency Control 499
TABLE 10.12
AN EXAMPLE OF A BINARY LOCK
TIME TRANSACTION STEP STORED VALUE
1
T1
Lock PRODUCT
2
T1
Read PROD_QOH
15
3
T1
PROD_QOH = 15 + 10
4
T1
Write PROD_QOH
25
5
T1
Unlock PRODUCT
6
T2
Lock PRODUCT
7
T2
Read PROD_QOH
23
8
T2
PROD_QOH = 23 − 10
9
T2
Write PROD_QOH
13
10
T2
Unlock PRODUCT
the lost update problem because the lock is not released until the WRITE statement is completed. Therefore, a PROD_QOH value cannot be used until it has been prop- erly updated. However, binary locks are now considered too restrictive to yield opti- mal concurrency conditions. For example, the DBMS will not allow two transactions to read the same database object even though neither transaction updates the database and therefore no concurrency problems can occur. Remember from Table 10.11 that concur- rency conflicts occur only when two transactions execute concurrently and one of them updates the database.
Shared/Exclusive An exclusive lock exists when access is reserved specifi- cally for the transaction that locked the object. The exclusive lock must be used when the potential for conflict exists (see Table 10.11). A shared lock exists when concurrent transactions are granted read access on the basis of a common lock. A shared lock produces no conflict as long as all the concurrent transactions are read-only.
A shared lock is issued when a transaction wants to read data from the database and no exclusive lock is held on that data item. An exclusive lock is issued when a transaction wants to update (write) a data item and no locks are currently held on that data item by any other transaction. Using the shared/exclusive locking concept, a lock can have three states: unlocked, shared (read), and exclusive (write).
As shown in Table 10.11, two transactions conflict only when at least one is a write transaction. Because the two read transactions can be safely executed at once, shared locks allow several read transactions to read the same data item concurrently. For exam- ple, if transaction T1 has a shared lock on data item X and transaction T2 wants to read data item X, T2 may also obtain a shared lock on data item X.
If transaction T2 updates data item X, an exclusive lock is required by T2 over data item X. The exclusive lock is granted if and only if no other locks are held on the data item (this condition is known as the mutual exclusive rule: only one transaction at a time can own an exclusive lock on an object.) Therefore, if a shared (or exclusive) lock is already held on data item X by transaction T1, an exclusive lock cannot be granted to transaction T2, and T2 must wait to begin until T1 commits. In other words, a shared lock will always block an exclusive (write) lock; hence, decreasing transaction concurrency.
exclusive lock
An exclusive lock
is issued when a transaction requests permission to update
a data item and no
locks are held on that data item by any other transaction. An exclusive lock does not allow other transactions to access the database.
shared lock
A lock that is issued when a transaction requests permission to read data from
a database and no exclusive locks are held on the data by another transaction. A shared lock allows other read- only transactions to access the database.
mutual exclusive rule
A condition in which only one transaction at a time can own an exclusive lock on the same object.
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Although the use of shared locks renders data access more efficient, a shared/exclu- sive lock schema increases the lock manager’s overhead for several reasons:
• The type of lock held must be known before a lock can be granted.
• Three lock operations exist: READ_LOCK to check the type of lock, WRITE_LOCK
to issue the lock, and UNLOCK to release the lock.
• The schema has been enhanced to allow a lock upgrade from shared to exclusive and a lock downgrade from exclusive to shared.
Although locks prevent serious data inconsistencies, they can lead to two major problems:
• The resulting transaction schedule might not be serializable.
• The schedule might create deadlocks. A deadlock occurs when two transactions wait indefinitely for each other to unlock data. A database deadlock, which is similar to traffic gridlock in a big city, is caused when two or more transactions wait for each other to unlock data.
Fortunately, both problems can be managed: serializability is attained through a locking protocol known as two-phase locking, and deadlocks can be managed by using deadlock detection and prevention techniques. Those techniques are examined in the next two sections.
10-3c Two-Phase Locking to Ensure Serializability
Two-phase locking (2PL) defines how transactions acquire and relinquish locks. Two-phase
locking guarantees serializability, but it does not prevent deadlocks. The two phases are:
1. A growing phase, in which a transaction acquires all required locks without unlock- ing any data. Once all locks have been acquired, the transaction is in its locked point.
2. Ashrinkingphase,inwhichatransactionreleasesalllocksandcannotobtainanewlock.
The two-phase locking protocol is governed by the following rules:
• Two transactions cannot have conflicting locks.
• No unlock operation can precede a lock operation in the same transaction.
• No data is affected until all locks are obtained—that is, until the transaction is in its locked point.
Figure 10.7 depicts the two-phase locking protocol.
In this example, the transaction first acquires the two locks it needs. When it has the two locks, it reaches its locked point. Next, the data is modified to conform to the trans- action’s requirements. Finally, the transaction is completed as it releases all of the locks it acquired in the first phase. Two-phase locking increases the transaction processing cost and might cause additional undesirable effects, such as deadlocks.
10-3d Deadlocks
A deadlock occurs when two transactions wait indefinitely for each other to unlock data. For
example, a deadlock occurs when two transactions, T1 and T2, exist in the following mode: T1 = access data items X and Y
T2 = access data items Y and X
If T1 has not unlocked data item Y, T2 cannot begin; if T2 has not unlocked data item X, T1 cannot continue. Consequently, T1 and T2 each wait for the other to unlock the
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deadlock
A condition in which two or more transactions wait indefinitely for the other to release the lock on a previously locked data item. Also called deadly embrace.
two-phase locking (2PL)
A set of rules that governs how transactions acquire
and relinquish locks. Two-phase locking guarantees serializability, but it does not prevent deadlocks. The two- phase locking protocol is divided into two phases: (1) A growing phase occurs when the transaction acquires the locks it needs without unlocking any existing data locks. Once all locks have been acquired, the transaction is in its locked point. (2) A shrinking phase occurs when the transaction releases all locks and cannot obtain a new lock.
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required data item. Such a deadlock is also known as a deadly embrace. Table 10.13 demonstrates how a deadlock condition is created.
The preceding example used only two concurrent transactions to demonstrate a dead- lock condition. In a real-world DBMS, many more transactions can be executed simulta- neously, thereby increasing the probability of generating deadlocks. Note that deadlocks are possible only when one of the transactions wants to obtain an exclusive lock on a data item; no deadlock condition can exist among shared locks.
Chapter 10 Transaction Management and Concurrency Control 501
FIGURE 10.7 TWO-PHASE LOCKING PROTOCOL
Acquire
Acquire lock lock
Locked
point
Release lock
Release lock
Time1 2 3 4 5 6 7 8
Start Operations End
Locked
Growing phase phase Shrinking phase
TABLE 10.13
HOW A DEADLOCK CONDITION IS CREATED
TIME TRANSACTION REPLY LOCK STATUS
DATA X
DATA Y
0
Unlocked
Unlocked
1
T1:LOCK(X)
OK
Locked
Unlocked
2
T2:LOCK(Y)
OK
Locked
Locked
3
T1:LOCK(Y)
WAIT
Locked
Locked
4
T2:LOCK(X)
WAIT
Locked
Locked
5
T1:LOCK(Y)
WAIT
Locked Locked
D e a
Locked
6
T2:LOCK(X)
WAIT
Locked
7
T1:LOCK(Y)
WAIT
Locked d
Locked
8
T2:LOCK(X)
WAIT
Locked o
Locked
9
T1:LOCK(Y)
WAIT
Locked ck
Locked
…
…………..
……..
………
……….
…
…………..
……..
………
……….
…
…………..
……..
………
……….
…
…………..
……..
………
………
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l
deadly embrace
See deadlock.

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The three basic techniques to control deadlocks are:
• Deadlock prevention. A transaction requesting a new lock is aborted when there is the possibility that a deadlock can occur. If the transaction is aborted, all changes made by this transaction are rolled back and all locks obtained by the transaction are released. The transaction is then rescheduled for execution. Deadlock prevention works because it avoids the conditions that lead to deadlocking.
• Deadlock detection. The DBMS periodically tests the database for deadlocks. If a dead- lock is found, the “victim” transaction is aborted (rolled back and restarted) and the other transaction continues.
• Deadlock avoidance. The transaction must obtain all of the locks it needs before it can be executed. This technique avoids the rolling back of conflicting transactions by requiring that locks be obtained in succession. However, the serial lock assignment required in deadlock avoidance increases action response times.
The choice of which deadlock control method to use depends on the database envi- ronment. For example, if the probability of deadlocks is low, deadlock detection is rec- ommended. However, if the probability of deadlocks is high, deadlock prevention is recommended. If response time is not high on the system’s priority list, deadlock avoid- ance might be employed. All current DBMSs support deadlock detection in transac- tional databases, while some DBMSs use a blend of prevention and avoidance techniques for other types of data, such as data warehouses or XML data.
10-4 Concurrency Control with Time Stamping Methods
The time stamping approach to scheduling concurrent transactions assigns a global, unique time stamp to each transaction. The time stamp value produces an explicit order in which transactions are submitted to the DBMS. Time stamps must have two proper- ties: uniqueness and monotonicity. Uniqueness ensures that no equal time stamp values can exist, and monotonicity1 ensures that time stamp values always increase.
All database operations (read and write) within the same transaction must have the same time stamp. The DBMS executes conflicting operations in time stamp order, thereby ensuring serializability of the transactions. If two transactions conflict, one is stopped, rolled back, rescheduled, and assigned a new time stamp value.
The disadvantage of the time stamping approach is that each value stored in the data- base requires two additional time stamp fields: one for the last time the field was read and one for the last update. Time stamping thus increases memory needs and the database’s processing overhead. Time stamping demands a lot of system resources because many transactions might have to be stopped, rescheduled, and restamped.
10-4a Wait/Die and Wound/Wait Schemes
Time stamping methods are used to manage concurrent transaction execution. In this section, you will learn about two schemes used to decide which transaction is rolled back and which continues executing: the wait/die scheme and the wound/wait scheme.2 An
1 The term monotonicity is part of the standard concurrency control vocabulary. The authors’ first introduction to this term and its proper use was in an article written by W. H. Kohler, “A survey of techniques for synchro- nization and recovery in decentralized computer systems,” Computer Surveys 3(2), June 1981, pp. 149–283.
2 The procedure was first described by R. E. Stearnes and P. M. Lewis II in “System-level concurrency control for distributed database systems,” ACM Transactions on Database Systems, No. 2, June 1978, pp. 178–198.
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time stamping
In transaction management, a technique used in scheduling concurrent transactions that assigns a global unique time stamp to each transaction.
uniqueness
In concurrency control, a property of time stamping that ensures no equal time stamp values can exist.
monotonicity
A quality that ensures that time stamp
values always increase. (The time stamping approach to scheduling concurrent transactions assigns a global, unique time stamp to each transaction. The time stamp value produces an explicit order in which transactions are submitted to the DBMS.)
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Chapter 10 Transaction Management and Concurrency Control 503
example illustrates the difference. Assume that you have two conflicting transactions: T1 and T2, each with a unique time stamp. Suppose that T1 has a time stamp of 11548789 and T2 has a time stamp of 19562545. You can deduce from the time stamps that T1 is the older transaction (the lower time stamp value), and T2 is the newer transaction. Given that scenario, the four possible outcomes are shown in Table 10.14.
TABLE 10.14
WAIT/DIE AND WOUND/WAIT CONCURRENCY CONTROL SCHEMES
TRANSACTION TRANSACTION WAIT/DIE SCHEME WOUND/WAIT SCHEME REQUESTING LOCK OWNING LOCK
T1 (11548789)
T2 (19562545)
• T1 waits until T2 is completed and T2 releases its locks.
• T1 preempts (rolls back) T2.
• T2 is rescheduled using the same time stamp.
T2 (19562545)
T1 (11548789)
• T2 dies (rolls back).
• T2 is rescheduled using the same time stamp.
• T2 waits until T1 is completed and T1 releases its locks.
Using the wait/die scheme:
• If the transaction requesting the lock is the older of the two transactions, it will wait until the other transaction is completed and the locks are released.
• If the transaction requesting the lock is the younger of the two transactions, it will die (roll back) and is rescheduled using the same time stamp.
In short, in the wait/die scheme, the older transaction waits for the younger one to complete and release its locks.
In the wound/wait scheme:
• If the transaction requesting the lock is the older of the two transactions, it will preempt (wound) the younger transaction by rolling it back. T1 preempts T2 when T1 rolls back T2. The younger, preempted transaction is rescheduled using the same time stamp.
• If the transaction requesting the lock is the younger of the two transactions, it will wait until the other transaction is completed and the locks are released.
In short, in the wound/wait scheme, the older transaction rolls back the younger transaction and reschedules it.
In both schemes, one of the transactions waits for the other transaction to finish and release the locks. However, in many cases, a transaction requests multiple locks. How long does a transaction have to wait for each lock request? Obviously, that scenario can cause some transactions to wait indefinitely, causing a deadlock. To prevent a deadlock, each lock request has an associated time-out value. If the lock is not granted before the time-out expires, the transaction is rolled back.
10-5 Concurrency Control with Optimistic Methods
The optimistic approach is based on the assumption that the majority of database operations do not conflict. The optimistic approach requires neither locking nor time stamping techniques. Instead, a transaction is executed without restrictions until it is
wait/die
A concurrency control scheme in which an older transaction must wait for the younger transaction to complete and release the locks before requesting the locks itself. Otherwise, the newer transaction dies and is rescheduled.
wound/wait
A concurrency control scheme in which
an older transaction
can request the lock, preempt the younger transaction, and reschedule it. Otherwise, the newer transaction waits until the older transaction finishes.
optimistic approach
In transaction management, a concurrency control technique based on the assumption that most database operations do not conflict.
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committed. Using an optimistic approach, each transaction moves through two or three phases, referred to as read, validation, and write.3
• During the read phase, the transaction reads the database, executes the needed com- putations, and makes the updates to a private copy of the database values. All update operations of the transaction are recorded in a temporary update file, which is not accessed by the remaining transactions.
• During the validation phase, the transaction is validated to ensure that the changes made will not affect the integrity and consistency of the database. If the validation test is positive, the transaction goes to the write phase. If the validation test is negative, the transaction is restarted and the changes are discarded.
• During the write phase, the changes are permanently applied to the database.
The optimistic approach is acceptable for most read or query database systems that require few update transactions. In a heavily used DBMS environment, the manage- ment of deadlocks—their prevention and detection—constitutes an important DBMS function. The DBMS will use one or more of the techniques discussed here, as well as variations on those techniques. To further understand how transaction management is implemented in a database, it is important that you learn about the transaction isolation levels as defined in ANSI SQL 1992 standard.
10-6 ANSI Levels of Transaction Isolation
The ANSI SQL standard (1992) defines transaction management based on transaction isolation levels. Transaction isolation levels refer to the degree to which transaction data is “protected or isolated” from other concurrent transactions. The isolation levels are described based on what data other transactions can see (read) during execution. More precisely, the transaction isolation levels are described by the type of “reads” that a trans- action allows or not. The types of read operations are:
• Dirty read: a transaction can read data that is not yet committed.
• Nonrepeatable read: a transaction reads a given row at time t1, and then it reads the same row at time t2, yielding different results. The original row may have been updated or deleted.
• Phantom read: a transaction executes a query at time t1, and then it runs the same query at time t2, yielding additional rows that satisfy the query.
Based on the above operations, ANSI defined four levels of transaction isolation: Read Uncommitted, Read Committed, Repeatable Read, and Serializable. Table 10.15 shows the four ANSI transaction isolation levels. The table also shows an additional level of isolation provided by Oracle and MS SQL Server databases.
Read Uncommitted will read uncommitted data from other transactions. At this iso- lation level, the database does not place any locks on the data, which increases transaction performance but at the cost of data consistency. Read Committed forces transactions to read only committed data. This is the default mode of operation for most databases (including Oracle and SQL Server). At this level, the database will use exclusive locks on data, causing other transactions to wait until the original transaction commits. The Repeatable Read isolation level ensures that queries return consistent results. This type of isolation level uses shared locks to ensure other transactions do not update a row after
3 The optimistic approach to concurrency control is described in an article by H. T. King and J. T. Robinson, “Optimistic methods for concurrency control,” ACM Transactions on Database Systems 6(2), June 1981, pp. 213–226. Even the most current software is built on conceptual standards that were developed more than two decades ago.
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dirty read
In transaction management, when a transaction reads data that is not yet committed.
nonrepeatable read
In transaction management, when
a transaction reads a given row at time t1, then reads the same row at time t2, yielding different results because the original row may have been updated or deleted.
phantom read
In transaction management, when a transaction executes a query at time t1, then runs the same query at time t2, yielding additional rows that satisfy the query.
Read Uncommitted
An ANSI SQL transaction isolation level that allows transactions to read uncommitted data from other transactions, and which allows nonrepeatable reads and phantom reads. The least restrictive level defined by ANSI SQL.
Read Committed
An ANSI SQL transaction isolation level that allows transactions to read
only committed data. This is the default mode of operations for most databases.
Repeatable Read
An ANSI SQL transaction isolation level that uses shared locks to ensure that other transactions do not update a row after the original query updates it. However, phantom reads are allowed.
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TABLE 10.15
TRANSACTION ISOLATION LEVELS
ISOLATION ALLOWED COMMENT LEVEL
Chapter 10 Transaction Management and Concurrency Control 505
DIRTY READ
NONREPEATABLE READ
PHANTOM READ
Less restrictive
Read Uncommitted
The transaction reads uncommitted data, allows nonrepeatable reads, and phantom reads.
More restrictive
Read Committed
Repeatable Read
Y
Y
Y
N
Y
N
Y
Y
Does not allow uncommitted data reads but allows nonrepeatable reads and phantom reads.
N
Only allows phantom reads.
Serializable
N
N
N
Does not allow dirty reads, nonrepeatable reads, or phantom reads.
Oracle / SQL Server Only
Read Only / Snapshot
N
N
N
Supported by Oracle and SQL Server. The transaction can only see the changes that were committed at the time the transaction started.
the original query reads it. However, new rows are read (phantom read) as these rows did not exist when the first query ran. The Serializable isolation level is the most restric- tive level defined by the ANSI SQL standard. However, it is important to note that even with a Serializable isolation level, deadlocks are always possible. Most databases use a deadlock detection approach to transaction management, and, therefore, they will detect “deadlocks” during the transaction validation phase and reschedule the transaction.
The reason for the different levels of isolation is to increase transaction concurrency. The isolation levels go from the least restrictive (Read Uncommitted) to the more restric- tive (Serializable). The higher the isolation level the more locks (shared and exclusive) are required to improve data consistency, at the expense of transaction concurrency per- formance. The isolation level of a transaction is defined in the transaction statement, for example using general ANSI SQL syntax:
BEGIN TRANSACTION ISOLATION LEVEL READ COMMITTED … SQL STATEMENTS….
COMMIT TRANSACTION;
Oracle and MS SQL Server use the SET TRANSACTION ISOLATION LEVEL state- ment to define the level of isolation. SQL Server supports all four ANSI isolation levels. Oracle by default provides consistent statement-level reads to ensure Read Committed and Repeatable Read transactions. MySQL uses START TRANSACTION WITH CON- SISTENT SNAPSHOT to provide transactions with consistent reads; that is, the transac- tion can only see the committed data at the time the transaction started.
As you can see from the previous discussion, transaction management is a complex subject and databases make use of various techniques to manage the concurrent exe- cution of transactions. However, it may be necessary sometimes to employ database recovery techniques to restore the database to a consistent state.
Serializable
An ANSI SQL transaction isolation level that does not allow dirty reads, nonrepeatable reads,
or phantom reads; the most restrictive level defined by the ANSI SQL standard.
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10-7 Database Recovery Management
Database recovery restores a database from a given state (usually inconsistent) to a previ- ously consistent state. Recovery techniques are based on the atomic transaction property: all portions of the transaction must be treated as a single, logical unit of work in which all operations are applied and completed to produce a consistent database. If a transaction oper- ation cannot be completed for some reason, the transaction must be aborted and any changes to the database must be rolled back (undone). In short, transaction recovery reverses all of the changes that the transaction made to the database before the transaction was aborted.
Although this chapter has emphasized the recovery of transactions, recovery tech- niques also apply to the database and to the system after some type of critical error has occurred. Critical events can cause a database to stop working and compromise the integrity of the data. Examples of critical events are:
• Hardware/software failures. A failure of this type could be a hard disk media failure, a bad capacitor on a motherboard, or a failing memory bank. Other causes of errors under this category include application program or operating system errors that cause data to be overwritten, deleted, or lost. Some database administrators argue that this is one of the most common sources of database problems.
• Human-caused incidents. This type of event can be categorized as unintentional or intentional.
– An unintentional failure is caused by a careless end user. Such errors include deleting the wrong rows from a table, pressing the wrong key on the keyboard, or shutting down the main database server by accident.
– Intentional events are of a more severe nature and normally indicate that the company data is at serious risk. Under this category are security threats caused by hackers trying to gain unauthorized access to data resources and virus attacks caused by disgruntled employees trying to compromise the database operation and damage the company.
• Natural disasters. This category includes fires, earthquakes, floods, and power failures.
Whatever the cause, a critical error can render the database into an inconsistent state. The following section introduces the various techniques used to recover the database from an inconsistent state to a consistent state.
10-7a Transaction Recovery
In Section 10-1d, you learned about the transaction log and how it contains data for database recovery purposes. Database transaction recovery uses data in the transaction log to recover a database from an inconsistent state to a consistent state.
Before continuing, examine four important concepts that affect the recovery process:
• The write-ahead-log protocol ensures that transaction logs are always written before any database data is actually updated. This protocol ensures that, in case of a failure, the database can later be recovered to a consistent state using the data in the transaction log.
• Redundant transaction logs (several copies of the transaction log) ensure that a physical disk failure will not impair the DBMS’s ability to recover data.
• Database buffers are temporary storage areas in primary memory used to speed up disk operations. To improve processing time, the DBMS software reads the data from the physical disk and stores a copy of it on a “buffer” in primary memory. When a transaction updates data, it actually updates the copy of the data in the buffer because that process is much faster than accessing the physical disk every time. Later, all buf- fers that contain updated data are written to a physical disk during a single operation, thereby saving significant processing time.
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database recovery
The process of restoring a database to a previous consistent state.
atomic transaction property
A property that requires all parts of a transaction to be treated as a single, logical unit of work in which all operations must be completed (committed) to produce a consistent database.
write-ahead-log protocol
In concurrency control, a process that ensures transaction logs are written to permanent storage before any database data is actually updated. Also called a write-ahead protocol.
redundant transaction logs Multiple copies of the transaction log kept by database management systems to ensure that the physical failure of a disk will not impair the DBMS’s ability to recover data.
buffer
Temporary storage area in primary memory used to speed up disk operations.
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• Database checkpoints are operations in which the DBMS writes all of its updated buffers in memory (also known as dirty buffers) to disk. While this is happening, the DBMS does not execute any other requests. A checkpoint operation is also reg- istered in the transaction log. As a result of this operation, the physical database and the transaction log will be in sync. This synchronization is required because update operations update the copy of the data in the buffers and not in the physical database. Checkpoints are automatically and periodically executed by the DBMS according to certain operational parameters (such a high watermark for the transaction log size or volume of outstanding transactions) but can also be executed explicitly (as part of a database transaction statement) or implicitly (as part of a database backup operation). Of course, checkpoints that are too frequent would affect transaction performance; checkpoints that are too infrequent would affect database recovery performance. In any case, checkpoints serve a very practical function. As you will see next, check- points also play an important role in transaction recovery.
The database recovery process involves bringing the database to a consistent state after a failure. Transaction recovery procedures generally make use of deferred-write and write-through techniques.
When the recovery procedure uses a deferred-write technique (also called a deferred update), the transaction operations do not immediately update the physical database. Instead, only the transaction log is updated. The database is physically updated only with data from committed transactions, using information from the transaction log. If the transaction aborts before it reaches its commit point, no changes (no ROLLBACK or undo) need to be made to the database because it was never updated. The recovery process for all started and committed transactions (before the failure) follows these steps:
1. Identify the last checkpoint in the transaction log. This is the last time transaction data was physically saved to disk.
2. For a transaction that started and was committed before the last checkpoint, nothing needs to be done because the data is already saved.
3. For a transaction that performed a commit operation after the last checkpoint, the DBMS uses the transaction log records to redo the transaction and update the database, using the “after” values in the transaction log. The changes are made in ascending order, from oldest to newest.
4. For any transaction that had a ROLLBACK operation after the last checkpoint or that was left active (with neither a COMMIT nor a ROLLBACK) before the failure occurred, nothing needs to be done because the database was never updated.
When the recovery procedure uses a write-through technique (also called an
immediate update), the database is immediately updated by transaction operations during the transaction’s execution, even before the transaction reaches its commit point. If the trans- action aborts before it reaches its commit point, a ROLLBACK or undo operation needs to be done to restore the database to a consistent state. In that case, the ROLLBACK operation will use the transaction log “before” values. The recovery process follows these steps:
1. Identify the last checkpoint in the transaction log. This is the last time transaction data was physically saved to disk.
2. For a transaction that started and was committed before the last checkpoint, nothing needs to be done because the data is already saved.
3. For a transaction that was committed after the last checkpoint, the DBMS re-does the transaction, using the “after” values of the transaction log. Changes are applied in ascending order, from oldest to newest.
checkpoint
In transaction management, an operation in which the database management system writes all of its updated buffers to disk.
deferred write technique
See deferred update.
deferred update
In transaction management, a condition in which transaction operations do not immediately update a physical database. Also called deferred write technique.
write-through technique
In concurrency control, a process that ensures a database is immediately updated by operations during the transaction’s execution, even before the transaction reaches its commit point. Also called immediate update.
immediate update
See write-through technique.
Chapter 10 Transaction Management and Concurrency Control 507
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4. For any transaction that had a ROLLBACK operation after the last checkpoint or that was left active (with neither a COMMIT nor a ROLLBACK) before the failure occurred, the DBMS uses the transaction log records to ROLLBACK or undo the operations, using the “before” values in the transaction log. Changes are applied in reverse order, from newest to oldest.
Use the transaction log in Table 10.16 to trace a simple database recovery process. To make sure you understand the recovery process, the simple transaction log includes three transactions and one checkpoint. This transaction log includes the transaction components used earlier in the chapter, so you should already be familiar with the basic process. Given the transaction, the transaction log has the following characteristics:
• Transaction 101 consists of two UPDATE statements that reduce the quantity on hand for product 54778-2T and increase the customer balance for customer 10011 for a credit sale of two units of product 54778-2T.
• Transaction 106 is the same credit sales event you saw in Section 10-1a. This transac- tion represents the credit sale of one unit of product 89-WRE-Q to customer 10016 for $277.55. This transaction consists of five SQL DML statements: three INSERT statements and two UPDATE statements.
• Transaction 155 represents a simple inventory update. This transaction consists of one UPDATE statement that increases the quantity on hand of product 2232/QWE from 6 units to 26 units.
• A database checkpoint writes all updated database buffers to disk. The checkpoint event writes only the changes for all previously committed transactions. In this case, the checkpoint applies all changes made by transaction 101 to the database data files.
Using Table 10.16, you can now trace the database recovery process for a DBMS using the deferred update method as follows:
1. Identifythelastcheckpoint—inthiscase,TRLID423.Thiswasthelasttimedatabase buffers were physically written to disk.
2. Note that transaction 101 started and finished before the last checkpoint. Therefore, all changes were already written to disk, and no additional action needs to be taken.
3. For each transaction committed after the last checkpoint (TRL ID 423), the DBMS will use the transaction log data to write the changes to disk, using the “after” values. For example, for transaction 106:
a. Find COMMIT (TRL ID 457).
b. Use the previous pointer values to locate the start of the transaction (TRL ID 397).
c. Use the next pointer values to locate each DML statement, and apply the changes to disk using the “after” values. (Start with TRL ID 405, then 415, 419, 427, and 431.) Remember that TRL ID 457 was the COMMIT state- ment for this transaction.
d. Repeat the process for transaction 155.
4. Any other transactions will be ignored. Therefore, for transactions that ended with ROLLBACK or that were left active (those that do not end with a COMMIT or ROLLBACK), nothing is done because no changes were written to disk.
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TABLE 10.16
A TRANSACTION LOG FOR TRANSACTION RECOVERY EXAMPLES
TRL ID TRX PREV NEXT NUM PTR PTR
OPERATION
TABLE
ROW ID
ATTRIBUTE
BEFORE AFTER VALUE VALUE
341 101 Null 352
352 101 341 363
363 101 352 365
365 101 363 Null
397 106 Null 405
405 106 397 415
415 106 405 419
419 106 415 427
423 CHECKPOINT
54778-2T 10011
PROD_QOH CUST_BALANCE
45 43 615.73 675.62
427 106 419 431 431 106 427 457 457 106 431 Null 521 155 Null 525 525 155 521 528 528 155 525 Null
UPDATE INSERT COMMIT START UPDATE COMMIT
CUSTOMER ACCT_TRANSACTION **** End of Transaction ****Start Transaction PRODUCT
**** End of Transaction
10016 10007
0.00 277.55 1007,18-JAN-2016, …
START UPDATE UPDATE COMMIT START INSERT INSERT UPDATE
****Start Transaction PRODUCT
CUSTOMER
**** End of Transaction ****Start Transaction INVOICE
LINE PRODUCT
PROD_QOH CUST_BALANCE
1009,1, 89-WRE-Q,1, … 12 11
2232/QWE * * * * * C *R*A* S* H * * * *
PROD_QOH
6 26
1009 1009,1 89-WRE-Q
1009,10016, …

510 Part 4 Advanced Database Concepts
Summary
• A transaction is a sequence of database operations that access the database. A trans- action is a logical unit of work; that is, all parts are executed or the transaction is aborted. A transaction takes a database from one consistent state to another. A consis- tent database state is one in which all data integrity constraints are satisfied.
• Transactions have four main properties: atomicity, consistency, isolation, and dura- bility. Atomicity means that all parts of the transaction must be executed; otherwise, the transaction is aborted. Consistency means that the database’s consistent state is maintained. Isolation means that data used by one transaction cannot be accessed by another transaction until the first one is completed. Durability means that changes made by a transaction cannot be rolled back once the transaction is committed. In addition, transaction schedules have the property of serializability—the result of the concurrent execution of transactions is the same as that of the transactions being executed in serial order.
• SQL provides support for transactions through the use of two statements: COMMIT, which saves changes to disk, and ROLLBACK, which restores the previous database state. SQL transactions are formed by several SQL statements or database requests. Each database request originates several I/O database operations. The transaction log keeps track of all transactions that modify the database. The information stored in the transaction log is used for recovery (ROLLBACK) purposes.
• Concurrency control coordinates the simultaneous execution of transactions. The concurrent execution of transactions can result in three main problems: lost updates, uncommitted data, and inconsistent retrievals. The scheduler is responsible for estab- lishing the order in which the concurrent transaction operations are executed. The transaction execution order is critical and ensures database integrity in multiuser database systems. The scheduler uses locking, time stamping, and optimistic methods to ensure the serializability of transactions.
• A lock guarantees unique access to a data item by a transaction. The lock prevents one transaction from using the data item while another transaction is using it. There are several levels of locks: database, table, page, row, and field. Two types of locks can be used in database systems: binary locks and shared/exclusive locks. A binary lock can have only two states: locked (1) or unlocked (0). A shared lock is used when a transac- tion wants to read data from a database and no other transaction is updating the same data. Several shared or “read” locks can exist for a particular item. An exclusive lock is issued when a transaction wants to update (write to) the database and no other locks (shared or exclusive) are held on the data.
• Serializability of schedules is guaranteed through the use of two-phase locking. The two-phase locking schema has a growing phase, in which the transaction acquires all of the locks that it needs without unlocking any data, and a shrinking phase, in which the transaction releases all of the locks without acquiring new locks. When two or more transactions wait indefinitely for each other to release a lock, they are in a deadlock, also called a deadly embrace. There are three deadlock control techniques: prevention, detection, and avoidance.
• Concurrency control with time stamping methods assigns a unique time stamp to each transaction and schedules the execution of conflicting transactions in time stamp order. Two schemes are used to decide which transaction is rolled back and which continues executing: the wait/die scheme and the wound/wait scheme.
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• Concurrency control with optimistic methods assumes that the majority of database transactions do not conflict and that transactions are executed concurrently, using private, temporary copies of the data. At commit time, the private copies are updated to the database. The ANSI standard defines four transaction isolation levels: Read Uncommitted, Read Committed, Repeatable Read, and Serializable.
• Database recovery restores the database from a given state to a previous consistent state. Database recovery is triggered when a critical event occurs, such as a hardware error or application error.
Chapter 10 Transaction Management and Concurrency Control 511
1. Explain the following statement: A transaction is a logical unit of work.
2. What is a consistent database state, and how is it achieved?
3. The DBMS does not guarantee that the semantic meaning of the transaction truly represents the real-world event. What are the possible consequences of that limitation? Give an example.
4. List and discuss the four individual transaction properties.
5. What does serializability of transactions mean?
6. What is a transaction log, and what is its function?
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Key Terms
atomicity
atomic transaction property binary lock
buffer
checkpoint
concurrency control consistency
consistent database state database-level lock database recovery
database request
deadlock
deadly embrace
deferred update deferred-write technique dirty read
diskpage
durability
exclusive lock
field-level lock immediate update inconsistent retrieval isolation
lock
lock granularity
lock manager
lost update
monotonicity
mutual exclusive rule nonrepeatable read optimistic approach
page
page-level lock pessimistic locking phantom read
Read Committed
Read Uncommitted redundant transaction log
Repeatable Read row-level lock scheduler serializability Serializable serializable schedule shared lock table-level lock
time stamping transaction
transaction log two-phase locking (2PL) uncommitted data uniqueness
wait/die
wound/wait write-ahead-log protocol write-through technique
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
Review Questions

512 Part 4
Advanced Database Concepts
7. What is a scheduler, what does it do, and why is its activity important to concurrency control?
8. What is a lock, and how does it work in general?
9. What are the different levels of lock granularity?
10. Why might a page-level lock be preferred over a field-level lock?
11. What is concurrency control, and what is its objective?
12. What is an exclusive lock, and under what circumstances is it granted?
13. Whatisadeadlock,andhowcanitbeavoided?Discussseveralstrategiesfordealing with deadlocks.
14. What are some disadvantages of time stamping methods for concurrency control?
15. Why might it take a long time to complete transactions when using an optimistic approach to concurrency control?
16. What are the three types of database-critical events that can trigger the database recovery process? Give some examples for each one.
17. What are the four ANSI transaction isolation levels? What type of reads does each level allow?
Problems
1. Suppose that you are a manufacturer of product ABC, which is composed of parts A, B, and C. Each time a new product ABC is created, it must be added to the product inventory, using the PROD_QOH in a table named PRODUCT. Also, each time the product is created, the parts inventory, using PART_QOH in a table named PART, must be reduced by one each of parts A, B, and C. The sample database contents are shown in Table P10.1.
TABLE P10.1
TABLE NAME: PRODUCT
TABLE NAME: PART
PROD_CODE
PROD_QOH
PART_CODE
PART_QOH
ABC
1,205
Given the preceding information, answer Questions a through e.
a. How many database requests can you identify for an inventory update for both
PRODUCT and PART?
b. Using SQL, write each database request you identified in Step a. c. Write the complete transaction(s).
d. Write the transaction log, using Table 10.1 as your template.
e. Using the transaction log you created in Step d, trace its use in database recovery.
2. Describe the three most common problems with concurrent transaction execution. Explain how concurrency control can be used to avoid those problems.
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A
567
B
98
C
549

Chapter 10 Transaction Management and Concurrency Control 513
3. What DBMS component is responsible for concurrency control? How is this feature used to resolve conflicts?
4. Using a simple example, explain the use of binary and shared/exclusive locks in a DBMS.
5. Suppose that your database system has failed. Describe the database recovery process and the use of deferred-write and write-through techniques.
6. ABC Markets sell products to customers. The relational diagram shown in Figure P10.6 represents the main entities for ABC’s database. Note the following important characteristics:
Online Content
The Ch10_ABC_Markets data- base is available at www. cengagebrain.com. Use this database to provide solutions for Problems 6–11.
• A customer may make many purchases, each one represented by an invoice.
• The CUS_BALANCE is updated with each credit purchase or payment and
represents the amount the customer owes.
• The CUS_BALANCE is increased (+) with every credit purchase and decreased (–) with every customer payment.
• The date of last purchase is updated with each new purchase made by the customer.
• The date of last payment is updated with each new payment made by the customer.
• An invoice represents a product purchase by a customer.
• An INVOICE can have many invoice LINEs, one for each product purchased.
• The INV_TOTAL represents the total cost of the invoice, including taxes.
• The INV_TERMS can be “30,” “60,” or “90” (representing the number of days of credit) or “CASH,” “CHECK,” or “CC.”
• The invoice status can be “OPEN,” “PAID,” or “CANCEL.”
• A product’s quantity on hand (P_QTYOH) is updated (decreased) with each
product sale.
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FIGURE P10.6 THE ABC MARKETS RELATIONAL DIAGRAM

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• A customer may make many payments. The payment type (PMT_TYPE) can be one of the following:
• “CASH” for cash payments.
• “CHECK” for check payments.
• “CC” for credit card payments.
• The payment details (PMT_DETAILS) are used to record data about check or credit card payments:
• The bank, account number, and check number for check payments.
• The issuer, credit card number, and expiration date for credit card payments.
Note: Not all entities and attributes are represented in this example. Use only the attri- butes indicated.
Using this database, write the SQL code to represent each of the following transac- tions. Use BEGIN TRANSACTION and COMMIT to group the SQL statements in logical transactions.
a. On May 11, 2016, customer 10010 makes a credit purchase (30 days) of one unit of product 11QER/31 with a unit price of $110.00; the tax rate is 8 percent. The invoice number is 10983, and this invoice has only one product line.
b. On June 3, 2016, customer 10010 makes a payment of $100 in cash. The payment ID is 3428.
7. Create a simple transaction log (using the format shown in Table 10.14) to represent the actions of the transactions in Problems 6a and 6b.
8. Assuming that pessimistic locking is being used but the two-phase locking proto- col is not, create a chronological list of the locking, unlocking, and data manipula- tion activities that would occur during the complete processing of the transaction described in Problem 6a.
9. Assuming that pessimistic locking is being used with the two-phase locking protocol, create a chronological list of the locking, unlocking, and data manipulation activities that would occur during the complete processing of the transaction described in Problem 6a.
10. Assuming that pessimistic locking is being used but the two-phase locking proto- col is not, create a chronological list of the locking, unlocking, and data manipula- tion activities that would occur during the complete processing of the transaction described in Problem 6b.
11. Assumingthatpessimisticlockingwiththetwo-phaselockingprotocolisbeingused with row-level lock granularity, create a chronological list of the locking, unlocking, and data manipulation activities that would occur during the complete processing of the transaction described in Problem 6b.
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Chapter 11
Database Performance Tuning and Query Optimization
In this chapter, you will learn:
• Basic database performance-tuning concepts
• How a DBMS processes SQL queries
• About the importance of indexes in query processing
• About the types of decisions the query optimizer has to make
• Some common practices used to write efficient SQL code
• How to formulate queries and tune the DBMS for optimal performance
Preview
Database performance tuning is a critical topic, yet it usually receives minimal coverage in the database curriculum. Most databases used in classrooms have only a few records per table. As a result, the focus is often on making SQL queries perform an intended task, without considering the efficiency of the query process. In fact, even the most efficient query environment yields no visible performance improvements over the least efficient query environment when only 20 or 30 table rows (records) are queried. Unfortunately, that lack of attention to query efficiency can yield unacceptably slow results in the real world when queries are executed over tens of millions of records. In this chapter, you will learn what it takes to create a more efficient query environment.
Data Files and Available Formats
CH11_SaleCo P P P P
Data Files Available on cengagebrain.com
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
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516 Part 4 Note
Advanced Database Concepts
Because this book focuses on databases, this chapter covers only the factors that directly affect database performance. Also, because performance-tuning techniques can be DBMS-specific, the material in this chapter might not be applicable under all circum- stances, nor will it necessarily pertain to all DBMS types. This chapter is designed to build a foundation for the general understanding of database performance-tuning issues and to help you choose appropriate performance-tuning strategies. (For the most current infor- mation about tuning your database, consult the database vendor’s documentation.)
11-1 Database Performance-Tuning Concepts
One of the main functions of a database system is to provide timely answers to end users. End users interact with the DBMS through the use of queries to generate information, using the following sequence:
1. The end-user (client-end) application generates a query.
2. The query is sent to the DBMS (server end).
3. The DBMS (server end) executes the query.
4. The DBMS sends the resulting data set to the end-user (client-end) application.
End users expect their queries to return results as quickly as possible. How do you know that the performance of a database is good? Good database performance is hard to evaluate. How do you know if a 1.06-second query response time is good enough? It is easier to identify bad database performance than good database performance—all it takes is end-user complaints about slow query results. Unfortunately, the same query might perform well one day and not so well two months later. Regardless of end-user perceptions, the goal of database performance is to execute queries as fast as possible. Therefore, database performance must be closely monitored and regularly tuned. Database performance tuning refers to a set of activities and procedures designed to reduce the response time of the database system—that is, to ensure that an end-user query is processed by the DBMS in the minimum amount of time.
The time required by a query to return a result set depends on many factors, which tend to be wide-ranging and to vary among environments and among vendors. In gen- eral, the performance of a typical DBMS is constrained by three main factors: CPU processing power, available primary memory (RAM), and input/output (hard disk and network) throughput. Table 11.1 lists some system components and summarizes general guidelines for achieving better query performance.
Naturally, the system will perform best when its hardware and software resources are optimized. However, in the real world, unlimited resources are not the norm; internal and external constraints always exist. Therefore, the system components should be opti- mized to obtain the best throughput possible with existing (and often limited) resources, which is why database performance tuning is important.
Fine-tuning the performance of a system requires a holistic approach. That is, all factors must be checked to ensure that each one operates at its optimum level and has sufficient resources to minimize the occurrence of bottlenecks. Because database design is such an important factor in determining the database system’s performance efficiency, it is worth repeating this book’s mantra:
Good database performance starts with good database design. No amount of fine-tuning will make a poorly designed database perform as well as a well-designed database.
arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
database performance tuning A set of activities and procedures designed
to reduce the response time of a database system—that is, to ensure that an end- user query is processed by the DBMS in the minimum amount of time.
Copyright 2017 Cengage Le Editorial review has deemed that

TABLE 11.1
GENERAL GUIDELINES FOR BETTER SYSTEM PERFORMANCE
SYSTEM RESOURCES CLIENT SERVER
Chapter 11 Database Performance Tuning and Query Optimization 517
Hardware
CPU
The fastest possible Dual-core CPU or higher
The fastest possible
Multiple processors (quad-core technology)
Cluster of networked computers
RAM
The maximum possible to avoid OS memory to disk swapping
The maximum possible to avoid OS memory to disk swapping
Hard disk
Fast SATA/EIDE hard disk with sufficient free hard disk space Solid State Drives (SSD) for faster speed
Multiple high-speed, high-capacity disks
Fast disk interface (SAS / SCSI / Firewire / Fibre Channel
RAID configuration optimized for throughput
Solid State Drives (SSD) for faster speed Separate disks for OS, DBMS, and
data spaces
Network
High-speed connection
High-speed connection
Software
Operating System (OS)
64-bit OS for larger address spaces Fine-tuned for best client application performance
64-bit OS for larger address spaces Fine-tuned for best server application performance
Network
Fine-tuned for best throughput
Fine-tuned for best throughput
Application
Optimize SQL in client application
Optimize DBMS server for best performance
This is particularly true when redesigning existing databases, where the end user expects unrealistic performance gains from older databases.
What constitutes a good, efficient database design? From the performance-tuning point of view, the database designer must ensure that the design makes use of features in the DBMS that guarantee the integrity and optimal performance of the database. This chapter provides fundamental knowledge that will help you optimize database perfor- mance by selecting the appropriate database server configuration, using indexes, under- standing table storage organization and data locations, and implementing the most efficient SQL query syntax.
11-1a Performance Tuning: Client and Server
In general, database performance-tuning activities can be divided into those on the
client side and those on the server side.
• On the client side, the objective is to generate a SQL query that returns the correct answer in the least amount of time, using the minimum amount of resources at the server end. The activities required to achieve that goal are commonly referred to as SQL performance tuning.
• On the server side, the DBMS environment must be properly configured to respond to clients’ requests in the fastest way possible, while making optimum use of existing resources. The activities required to achieve that goal are commonly referred to as DBMS performance tuning.
SQL performance tuning
Activities to help generate a SQL query that returns the correct answer in the least amount of time, using the minimum amount of resources at the server end.
DBMS performance tuning
Activities to ensure that clients’ requests are addressed as quickly as possible while making optimum use of existing resources.
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518 Part 4
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Online
Content
If you want to learn more about clients and servers, check Appendix F, Client/Server Systems, at www.cengagebrain. com.
Keep in mind that DBMS implementations are typically more complex than just a two-tier client/server configuration. The network component plays a critical role in deliv- ering messages between clients and servers; this is especially important in distributed databases. In this chapter however, we assume a fully optimized network, and, therefore, our focus is on the database components. Even in multi-tier client/server environments that consist of a client front end, application middleware, and database server back end, performance-tuning activities are frequently divided into subtasks to ensure the fastest possible response time between any two component points. The database administrator must work closely with the network group to ensure that database traffic flows efficiently in the network infrastructure. This is even more important when you consider that most database systems service geographically dispersed users.
This chapter covers SQL performance-tuning practices on the client side and DBMS performance-tuning practices on the server side. However, before you start learning about the tuning processes, you must first learn more about the DBMS architectural components and processes, and how those processes interact to respond to end-users’ requests.
11-1b DBMS Architecture
The architecture of a DBMS is represented by the processes and structures (in memory and permanent storage) used to manage a database. Such processes collaborate with one another to perform specific functions. Figure 11.1 illustrates the basic DBMS architecture.
Note the following components and functions in Figure 11.1:
• All data in a database is stored in data files. A typical enterprise database is nor- mally composed of several data files. A data file can contain rows from a single table, or it can contain rows from many different tables. A database administrator (DBA)
data file
A named physical storage space that stores a database’s
data. It can reside in a different directory on a hard disk or on one or more hard disks. All data in a database is stored in data files. A typical enterprise database is normally composed of several data files. A data file can contain rows from one or more tables.
FIGURE11.1 BASICDBMSARCHITECTURE
Client computer
SQL query
Result set is sent back to client
DBMS server computer
Client process
Listener
Scheduler SQL cache
User process
Lock Optimizer manager
Data cache
Database Table spaces
Data files
Database data files stored in permanent secondary memory (hard disk)
I/O operations
DBMS processes running in primary memory (RAM)
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determines the initial size of the data files that make up the database; however, the data files can automatically expand as required in predefined increments known as extents. For example, if more space is required, the DBA can define that each new extent will be in 10 KB or 10 MB increments.
• Data files are generally grouped in file groups or table spaces. A table space or file group is a logical grouping of several data files that store data with similar charac- teristics. For example, you might have a system table space where the data dictionary table data is stored, a user data table space to store the user-created tables, an index table space to hold all indexes, and a temporary table space to do temporary sorts, grouping, and so on. Each time you create a new database, the DBMS automatically creates a minimum set of table spaces.
• The data cache, or buffer cache, is a shared, reserved memory area that stores the most recently accessed data blocks in RAM. The data read from the data files is stored in the data cache after the data has been read or before the data is written to the data files. The data cache also caches system catalog data and the contents of the indexes.
• The SQL cache, or procedure cache, is a shared, reserved memory area that stores the most recently executed SQL statements or PL/SQL procedures, including triggers and functions. (To learn more about PL/SQL procedures, triggers, and SQL functions, study Chapter 8, Advanced SQL.) The SQL cache does not store the SQL written by the end user. Rather, the SQL cache stores a “processed” version of the SQL that is ready for execution by the DBMS.
• To work with the data, the DBMS must retrieve the data from permanent storage and place it in RAM. In other words, the data is retrieved from the data files and placed in the data cache.
• To move data from permanent storage (data files) to RAM (data cache), the DBMS issues I/O requests and waits for the replies. An input/output (I/O) request is a low- level data access operation that reads or writes data to and from computer devices, such as memory, hard disks, video, and printers. Note that an I/O disk read operation retrieves an entire physical disk block, generally containing multiple rows, from per- manent storage to the data cache, even if you will use only one attribute from only one row. The physical disk block size depends on the operating system and could be 4K, 8K, 16K, 32K, 64K, or even larger. Furthermore, depending on the circumstances, a DBMS might issue a single-block read request or a multiblock read request.
• Working with data in the data cache is many times faster than working with data in the data files because the DBMS does not have to wait for the hard disk to retrieve the data; no hard disk I/O operations are needed to work within the data cache.
• Most performance-tuning activities focus on minimizing the number of I/O operations because using I/O operations is many times slower than reading data from the data cache. For example, as of this writing, RAM access times range from 5 to 70 nanoseconds, while hard disk access times range from 5 to 15 milliseconds. This means that hard disks are about six orders of magnitude (a million times) slower than RAM.
Figure 11.1 also illustrates some typical DBMS processes. Although the number of processes and their names vary from vendor to vendor, the functionality is similar. The following processes are represented in Figure 11.1:
extents
In a DBMS environment, refers to the ability of data files to expand in size automatically using predefined increments.
table space
In a DBMS, a logical storage space used to group related data. Also known as a file group.
file group
See table space.
data cache
A shared, reserved memory area that stores the most recently accessed data blocks in RAM. Also called buffer cache.
buffer cache
See data cache.
SQL cache
A shared, reserved memory area that stores the most recently executed SQL statements or PL/SQL procedures, including triggers and functions. Also called procedure cache.
procedure cache
See SQL cache.
input/output (I/O) request
A low-level data access operation that reads or writes data to and from computer devices.
Chapter 11 Database Performance Tuning and Query Optimization 519
•
Listener. The listener process listens for clients’ requests and handles the processing of the SQL requests to other DBMS processes. Once a request is received, the listener passes the request to the appropriate user process.
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520 Part 4
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• User. The DBMS creates a user process to manage each client session. Therefore, when you log on to the DBMS, you are assigned a user process. This process handles all requests you submit to the server. There are many user processes—at least one per logged-in client.
• Scheduler. The scheduler process organizes the concurrent execution of SQL requests. (See Chapter 10, Transaction Management and Concurrency Control.)
• Lock manager. This process manages all locks placed on database objects, including disk pages. (See Chapter 10.)
• Optimizer. The optimizer process analyzes SQL queries and finds the most efficient way to access the data. You will learn more about this process later in the chapter.
11-1c Database Query Optimization Modes
Most of the algorithms proposed for query optimization are based on two principles:
• The selection of the optimum execution order to achieve the fastest execution time
• The selection of sites to be accessed to minimize communication costs
Within those two principles, a query optimization algorithm can be evaluated on the basis of its operation mode or the timing of its optimization.
Operation modes can be classified as manual or automatic. Automatic query optimization means that the DBMS finds the most cost-effective access path with- out user intervention. Manual query optimization requires that the optimization be selected and scheduled by the end user or programmer. Automatic query optimization is clearly more desirable from the end user’s point of view, but the cost of such convenience is the increased overhead that it imposes on the DBMS.
Query optimization algorithms can also be classified according to when the optimi- zation is done. Within this timing classification, query optimization algorithms can be static or dynamic.
• Static query optimization takes place at compilation time. In other words, the best optimization strategy is selected when the query is compiled by the DBMS. This approach is common when SQL statements are embedded in procedural program- ming languages such as C# or Visual Basic .NET. When the program is submitted to the DBMS for compilation, it creates the plan necessary to access the database. When the program is executed, the DBMS uses that plan to access the database.
• Dynamic query optimization takes place at execution time. Database access strategy is defined when the program is executed. Therefore, access strategy is dynamically determined by the DBMS at run time, using the most up-to-date information about the database. Although dynamic query optimization is efficient, its cost is measured by run-time processing overhead. The best strategy is determined every time the query is executed; this could happen several times in the same program.
Finally, query optimization techniques can be classified according to the type of information that is used to optimize the query. For example, queries may be based on statistically based or rule-based algorithms.
• A statistically based query optimization algorithm uses statistical information about the database. The statistics provide information about database characteristics such as size, number of records, average access time, number of requests serviced, and number of users with access rights. These statistics are then used by the DBMS to determine the best access strategy. Within statistically based optimizers, some DBMSs allow setting a goal to specify that the optimizer should attempt to minimize
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
automatic query optimization
A method by which a DBMS finds the most efficient access path for the execution of a query.
manual query optimization
An operation mode that requires the end user or programmer to define the access path for the execution of a query.
static query optimization
A query optimization mode in which
the access path
to a database is predetermined at compilation time.
dynamic query optimization
The process of determining the SQL access strategy at run time, using the most up-to-date information about the database.
statistically based query optimization algorithm
A query optimization technique that uses statistical information about a database. The DBMS then uses these statistics to determine the best access strategy.
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the time to retrieve the first row or the last row. Minimizing the time to retrieve the first row is often used in transaction systems and interactive client environments. In these cases, the goal is to present the first several rows to the user as quickly as possi- ble. Then, while the DBMS waits for the user to scroll through the data, it can fetch the other rows for the query. Setting the optimizer goal to minimize retrieval of the last row is typically done in embedded SQL and inside stored procedures. In these cases, the control will not pass back to the calling application until all of the data has been retrieved; therefore, it is important to retrieve all of the data to the last row as quickly as possible so control can be returned.
• The statistical information is managed by the DBMS and is generated in one of two different modes: dynamic or manual. In the dynamic statistical generation mode, the DBMS automatically evaluates and updates the statistics after each data access operation. In the manual statistical generation mode, the statistics must be updated periodically through a user-selected utility such as IBM’s RUNSTAT com- mand, which is used by DB2 DBMSs.
• A rule-based query optimization algorithm is based on a set of user-defined rules to determine the best query access strategy. The rules are entered by the end user or database administrator, and they are typically general in nature.
Because database statistics play a crucial role in query optimization, this topic is explored in more detail in the next section.
11-1d Database Statistics
Another DBMS process that plays an important role in query optimization is gathering database statistics. The term database statistics refers to a number of measurements about database objects, such as number of processors used, processor speed, and tempo- rary space available. Such statistics provide a snapshot of database characteristics.
As you will learn later in this chapter, the DBMS uses these statistics to make critical decisions about improving query processing efficiency. Database statistics can be gath- ered manually by the DBA or automatically by the DBMS. For example, many DBMS vendors support the ANALYZE command in SQL to gather statistics. In addition, many vendors have their own routines to gather statistics. For example, IBM’s DB2 uses the RUNSTATS procedure, while Microsoft’s SQL Server uses the UPDATE STATISTICS procedure and provides the Auto-Update and Auto-Create Statistics options in its initial- ization parameters. A sample of measurements that the DBMS may gather about various database objects is shown in Table 11.2.
TABLE 11.2
SAMPLE DATABASE STATISTICS MEASUREMENTS
DATABASE OBJECT SAMPLE MEASUREMENTS
Chapter 11 Database Performance Tuning and Query Optimization 521
dynamic statistical generation mode
In a DBMS, the capability to automatically evaluate and update the database access statistics after each data access operation.
manual statistical generation mode
A mode of generating statistical data access information for query optimization. In this mode, the DBA must periodically run a routine to generate the data access statistics—for example, running the RUNSTAT command in an IBM DB2 database.
rule-based query optimization algorithm
A query optimization technique that uses preset rules and points to determine the best approach to executing a query.
database statistics
In query optimization, measurements about database objects, such as the number of rows in a table, number of disk blocks used, maximum and average row length, number of columns in each row, and number of distinct values in each column. Such statistics provide a snapshot of database characteristics.
Tables
Number of rows, number of disk blocks used, row length, number of columns in each row, number of distinct values in each column, maximum value in each column, minimum value in each column, and columns that have indexes
Indexes
Number and name of columns in the index key, number of key values in the index, number of distinct key values in the index key, histogram of key values in an index, and number of disk pages used by the index
Environment Resources
Logical and physical disk block size, location and size of data files, and number of extends per data file
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If the object statistics exist, the DBMS will use them in query processing. Most newer DBMSs (such as Oracle, MySQL, SQL Server, and DB2) automatically gather statistics; others require the DBA to gather statistics manually. To generate the database object statistics manually, each DBMS has its own commands.
In Oracle, use ANALYZE

object_name COMPUTE STATISTICS;
In MySQL, use ANALYZE TABLE ;
In SQL Server, use UPDATE STATISTICS , where object name refers to a table or a view.
For example, to generate statistics for the VENDOR table, you would use:
In Oracle: ANALYZE TABLE VENDOR COMPUTE STATISTICS; In MySQL: ANALYZE TABLE VENDOR;
In SQL Server: UPDATE STATISTICS VENDOR;
When you generate statistics for a table, all related indexes are also analyzed. How- ever, you could generate statistics for a single index by using the following command, where VEND_NDX is the name of the index:
ANALYZE INDEX VEND_NDX COMPUTE STATISTICS;
In SQL Server, use UPDATE STATISTICS . An exam- ple command would be UPDATE STATISTICS VENDOR VEND_NDX;.
Database statistics are stored in the system catalog in specially designated tables. It is common to periodically regenerate the statistics for database objects, especially database objects that are subject to frequent change. For example, if you have a video rental DBMS, your system will likely use a RENTAL table to store the daily video rent- als. That RENTAL table and its associated indexes would be subject to constant inserts and updates as you record daily rentals and returns. Therefore, the RENTAL table sta- tistics you generated last week do not accurately depict the table as it exists today. The more current the statistics are, the better the chances that the DBMS will properly select the fastest way to execute a given query.
Now that you know the basic architecture of DBMS processes and memory struc- tures, and the importance and timing of the database statistics gathered by the DBMS, you are ready to learn how the DBMS processes a SQL query request.
11-2 Query Processing
What happens at the DBMS server end when the client’s SQL statement is received?
In simple terms, the DBMS processes a query in three phases:
1. Parsing. The DBMS parses the SQL query and chooses the most efficient access/ execution plan.
2. Execution. The DBMS executes the SQL query using the chosen execution plan. 3. Fetching. The DBMS fetches the data and sends the result set back to the client.
The processing of SQL DDL statements (such as CREATE TABLE) is different from the processing required by DML statements. The difference is that a DDL statement actually updates the data dictionary tables or system catalog, while a DML statement (SELECT, INSERT, UPDATE, or DELETE) mostly manipulates end-user data. Figure 11.2 shows the general steps required for query processing. Each step will be discussed in the following sections.
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11-2a SQL Parsing Phase
The optimization process includes breaking down—parsing—the query into smaller units and transforming the original SQL query into a slightly different version of the orig- inal SQL code, but one that is fully equivalent and more efficient. Fully equivalent means that the optimized query results are always the same as the original query. More efficient means that the optimized query will almost always execute faster than the original query. (Note that it almost always executes faster because many factors affect the performance of a database, as explained earlier. Those factors include the network, the client computer’s resources, and other queries running concurrently in the same database.) To determine the most efficient way to execute the query, the DBMS may use the database statistics you learned about earlier.
The SQL parsing activities are performed by the query optimizer, which analyzes the SQL query and finds the most efficient way to access the data. This process is the most time-consuming phase in query processing. Parsing a SQL query requires several steps, in which the SQL query is:
• Validated for syntax compliance
• Validated against the data dictionary to ensure that table names and column names
are correct
• Validated against the data dictionary to ensure that the user has proper access rights
• Analyzed and decomposed into more atomic components
query optimizer
A DBMS process that analyzes SQL queries and finds the most efficient way to access the data. The query optimizer generates the access or execution plan for the query.
Chapter 11 Database Performance Tuning and Query Optimization 523
FIGURE11.2 QUERYPROCESSING
Select …. From … Where …
Parsing phase
SQL cache
Access plan
Execution phase
Fetching phase
• Generate result set
Data files
• Syntax check
• Naming check
• Access rights check
• Decompose and analyze
• Generate access plan
• Store access plan in SQL cache
• Execute I/O operations
• Add locks for transaction mgmt
• Retrieve data blocks from data files • Place data blocks in data cache
Data cache
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• Optimized through transformation into a fully equivalent but more efficient SQL query
• Prepared for execution by determining the most efficient execution or access plan
Once the SQL statement is transformed, the DBMS creates what is commonly known as an access plan or execution plan. An access plan is the result of parsing a SQL statement; it contains the series of steps a DBMS will use to execute the query and return the result set in the most efficient way. First, the DBMS checks to see if an access plan already exists for the query in the SQL cache. If it does, the DBMS reuses the access plan to save time. If it does not, the optimizer evaluates various plans and then decides which indexes to use and how to best perform join operations. The chosen access plan for the query is then placed in the SQL cache and made available for use and future reuse.
Access plans are DBMS-specific and translate the client’s SQL query into the series of complex I/O operations required to read the data from the physical data files and generate the result set. Table 11.3 illustrates some I/O operations for an Oracle RDBMS. Most DBMSs perform similar types of operations when accessing and manipulating data sets.
TABLE 11.3
SAMPLE DBMS ACCESS PLAN I/O OPERATIONS
OPERATION
DESCRIPTION
Table scan (full)
Reads the entire table sequentially, from the first row to the last, one row at a time (slowest)
Table access (row ID)
Reads a table row directly, using the row ID value (fastest)
Index scan (range)
Reads the index first to obtain the row IDs and then accesses the table rows directly (faster than a full table scan)
Index access (unique)
Used when a table has a unique index in a column
Nested loop
Reads and compares a set of values to another set of values, using a nested loop style (slow)
Merge
Merges two data sets (slow)
Sort
Sorts a data set (slow)
In Table 11.3, note that a table access using a row ID is the fastest method. A row ID is a unique identification for every row saved in permanent storage; it can be used to access the row directly. Conceptually, a row ID is similar to a slip you get when you park your car in an airport parking lot. The parking slip contains the section number and lot number. Using that information, you can go directly to your car without searching every section and lot.
11-2b SQL Execution Phase
In this phase, all I/O operations indicated in the access plan are executed. When the exe- cution plan is run, the proper locks—if needed—are acquired for the data to be accessed, and the data is retrieved from the data files and placed in the DBMS’s data cache. All transaction management commands are processed during the parsing and execution phases of query processing.
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access plan
A set of instructions generated at application compilation time that is created and managed by a DBMS. The access plan predetermines how an application’s query will access the database at run time.
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11-2c SQL Fetching Phase
After the parsing and execution phases are completed, all rows that match the specified condition(s) are retrieved, sorted, grouped, and aggregated (if required). During the fetch- ing phase, the rows of the resulting query result set are returned to the client. The DBMS might use temporary table space to store temporary data. In this stage, the database server coordinates the movement of the result set rows from the server cache to the client cache. For example, a given query result set might contain 9,000 rows; the server would send the first 100 rows to the client and then wait for the client to request the next set of rows, until the entire result set is sent to the client.
11-2d Query Processing Bottlenecks
The main objective of query processing is to execute a given query in the fastest way possible with the least amount of resources. As you have seen, the execution of a query requires the DBMS to break down the query into a series of interdependent I/O opera- tions to be executed in a collaborative manner. The more complex a query is, the more complex the operations are, which means that bottlenecks are more likely. A query processing bottleneck is a delay introduced in the processing of an I/O operation that causes the overall system to slow down. In the same way, the more components a system has, the more interfacing is required among the components, increasing the likelihood of bottlenecks. Within a DBMS, five components typically cause bottlenecks:
• CPU. The CPU processing power of the DBMS should match the system’s expected work load. A high CPU utilization might indicate that the processor speed is too slow for the amount of work performed. However, heavy CPU utilization can be caused by other factors, such as a defective component, not enough RAM (the CPU spends too much time swapping memory blocks), a badly written device driver, or a rogue process. A CPU bottleneck will affect not only the DBMS but all processes running in the system.
• RAM. The DBMS allocates memory for specific usage, such as data cache and SQL cache. RAM must be shared among all running processes, including the operat- ing system and DBMS. If there is not enough RAM available, moving data among components that are competing for scarce RAM can create a bottleneck.
• Hard disk. Other common causes of bottlenecks are hard disk speed and data transfer rates. Current hard disk storage technology allows for greater storage capacity than in the past; however, hard disk space is used for more than just storing end-user data. Current operating systems also use the hard disk for virtual memory, which refers to copying areas of RAM to the hard disk as needed to make room in RAM for more urgent tasks. Therefore, more hard disk storage space and faster data transfer rates reduce the likelihood of bottlenecks.
• Network. In a database environment, the database server and the clients are con- nected via a network. All networks have a limited amount of bandwidth that is shared among all clients. When many network nodes access the network at the same time, bottlenecks are likely.
• Application code. Not all bottlenecks are caused by limited hardware resources. Two of the most common sources of bottlenecks are inferior application code and poorly designed databases. Inferior code can be improved with code optimization tech- niques, as long as the underlying database design is sound. However, no amount of coding will make a poorly designed database perform better.
query processing bottleneck
In query optimization,
a delay introduced in the processing of an I/O operation that causes the overall system to slow down.
Chapter 11 Database Performance Tuning and Query Optimization 525
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Bottlenecks are the result of multiple database transactions competing for the use of database resources (CPU, RAM, hard disk, indexes, locks, buffers, etc.). As you learned earlier in this chapter, a DBMS uses many components and structures to perform its operations, such as processes, buffers, locks, table spaces, indexes, and log files. These resources are used by all transactions executing on the database, and, therefore, the transactions often compete for such resources. Because most (if not all) transactions work with data rows in tables, one of the most typical bottlenecks is caused by transac- tions competing for the same data rows. Another common source of contention is for shared memory resources, particularly shared buffers and locks. To speed up data update operations, the DMBS uses buffers to cache the data. At the same time, to manage access to data, the DBMS uses locks. Learning how to avoid these bottlenecks and optimize database performance is the main focus of this chapter.
11-3 Indexes and Query Optimization
Indexes are crucial in speeding up data access because they facilitate searching, sorting, and using aggregate functions and even join operations. The improvement in data access speed occurs because an index is an ordered set of values that contains the index key and pointers. The pointers are the row IDs for the actual table rows. Conceptually, a data index is similar to a book index. When you use a book index, you look up a word, which is similar to the index key. The word is accompanied by one or more page numbers where the word is used; these numbers are similar to pointers.
An index scan is more efficient than a full table scan because the index data is pre- ordered and the amount of data is usually much smaller. Therefore, when performing searches, it is almost always better for the DBMS to use the index to access a table than to scan all rows in a table sequentially. For example, Figure 11.3 shows the index repre- sentation of a CUSTOMER table with 14,786 rows and the index STATE_NDX on the CUS_STATE attribute.
Suppose you submit the following query:
FIGURE11.3 INDEXREPRESENTATIONFORTHECUSTOMERTABLE
STATE_NDX INDEX
CUSTOMER TABLE (14,786 rows)
SELECT FROM WHERE
CUS_NAME, CUS_STATE CUSTOMER
CUS_STATE = ‘FL’;
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If there is no index, the DBMS will perform a full-table scan and read all 14,786 cus- tomer rows. Assuming that the index STATE_NDX is created (and analyzed), the DBMS will automatically use the index to locate the first customer with a state equal to ‘FL’ and then proceed to read all subsequent CUSTOMER rows, using the row IDs in the index as a guide. Assuming that only five rows meet the condition CUS_STATE = ‘FL’ there are five accesses to the index and five accesses to the data, for a total of 10 I/O accesses. The DBMS would be saved from reading approximately 14,776 I/O requests for customer rows that do not meet the criteria. That is a lot of CPU cycles!
If indexes are so important, why not index every column in every table? The simple answer is that it is not practical to do so. Indexing every column in every table overtaxes the DBMS in terms of index-maintenance processing, especially if the table has many attributes and rows, or requires many inserts, updates, and deletes.
One measure that determines the need for an index is the data sparsity of the col- umn you want to index. Data sparsity refers to the number of different values a column could have. For example, a STU_SEX column in a STUDENT table can have only two possible values, M or F; therefore, that column is said to have low sparsity. In contrast, the STU_DOB column that stores the student date of birth can have many different date values; therefore, that column is said to have high sparsity. Knowing the sparsity helps you decide whether the use of an index is appropriate. For exam- ple, when you perform a search in a column with low sparsity, you are likely to read a high percentage of the table rows anyway; therefore, index processing might be unnecessary work. In Section 11-5, you learn how to determine when an index is recommended.
Most DBMSs implement indexes using one of the following data structures:
• Hash index. A hash index is based on an ordered list of hash values. A hash algorithm is used to create a hash value from a key column. This value points to an entry in a hash table, which in turn points to the actual location of the data row. This type of index is good for simple and fast lookup operations based on equality conditions—for example, LNAME=”Scott” and FNAME=”Shannon”.
• B-tree index. The B-tree index is an ordered data structure organized as an upside- down tree. (See Figure 11.4.) The index tree is stored separately from the data. The lower-level leaves of the B-tree index contain the pointers to the actual data rows. B-tree indexes are “self-balanced,” which means that it takes approximately the same amount of time to access any given row in the index. This is the default and most common type of index used in databases. The B-tree index is used mainly in tables in which column values repeat a relatively small number of times.
• Bitmap index. A bitmap index uses a bit array (0s and 1s) to represent the exis- tence of a value or condition. These indexes are used mostly in data warehouse appli- cations in tables with a large number of rows in which a small number of column values repeat many times. (See Figure 11.4.) Bitmap indexes tend to use less space than B-tree indexes because they use bits instead of bytes to store their data.
Using the preceding index characteristics, a database designer can determine the best type of index to use. For example, assume that a CUSTOMER table has several thousand rows. The CUSTOMER table has two columns that are used extensively for query pur- poses: CUS_LNAME, which represents a customer’s last name, and REGION_CODE, which can have one of four values (NE, NW, SW, and SE). Based on this information, you could conclude that:
• Because the CUS_LNAME column contains many different values that repeat a relatively small number of times compared to the total number of rows in the table, a B-tree index will be used.
data sparsity
A column distribution of values or the number of different values a column can have.
hash index
An index based on an ordered list of hash values.
B-tree index
An ordered data structure organized as an upside-down tree.
bitmap index
An index that uses a bit array (0s and 1s) to represent the existence of a value or condition.
Chapter 11 Database Performance Tuning and Query Optimization 527
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FIGURE11.4 B-TREEANDBITMAPINDEXREPRESENTATION
• Because the REGION_CODE column contains only a few different values that repeat a relatively large number of times compared to the total number of rows in the table, a bitmap index will be used. Figure 11.4 shows the B-tree and bitmap representations for the CUSTOMER table used in the previous discussion.
Current-generation DBMSs are intelligent enough to determine the best type of index to use under certain circumstances, provided that the DBMS has updated data- base statistics. Regardless of which index is chosen, the DBMS determines the best plan to execute a given query. The next section guides you through a simplified exam- ple of the type of choices the query optimizer must make.
11-4 Optimizer Choices
Query optimization is the central activity during the parsing phase in query processing. In this phase, the DBMS must choose what indexes to use, how to perform join opera- tions, which table to use first, and so on. Each DBMS has its own algorithms for deter- mining the most efficient way to access the data. The query optimizer can operate in one of two modes:
• A rule-based optimizer uses preset rules and points to determine the best approach to execute a query. The rules assign a “fixed cost” to each SQL operation; the costs are then added to yield the cost of the execution plan. For example, a full table scan has a set cost of 10, while a table access by row ID has a set cost of 3.
• A cost-based optimizer uses sophisticated algorithms based on statistics about the objects being accessed to determine the best approach to execute a query. In this
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rule-based optimizer
A query optimization mode based on the rule-based query optimization algorithm.
cost-based optimizer
A query optimization mode that uses an algorithm based on statistics about the objects being accessed, including number of rows, indexes available, index sparsity, and so on.
Copyright 2017 Cengage Lea Editorial review has deemed that

case, the optimizer process adds up the processing cost, the I/O costs, and the resource costs (RAM and temporary space) to determine the total cost of a given execution plan.
The optimizer’s objective is to find alternative ways to execute a query—to evaluate the “cost” of each alternative and then to choose the one with the lowest cost. To understand the function of the query optimizer, consider a simple example. Assume that you want to list all products provided by a vendor based in Florida. To acquire that information, you could write the following query:
SELECT FROM WHERE
P_CODE, P_DESCRIPT, P_PRICE, V_NAME, V_STATE PRODUCT, VENDOR
PRODUCT.V_CODE = VENDOR.V_CODE
AND VENDOR.V_STATE = ‘FL’;
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Furthermore, assume that the database statistics indicate the following:
• The PRODUCT table has 7,000 rows.
• The VENDOR table has 300 rows.
• Ten vendors are located in Florida.
• One thousand products come from vendors in Florida.
It is important to point out that only the first two items are available to the optimizer. The second two items are assumed to illustrate the choices that the optimizer must make. Armed with the information in the first two items, the optimizer would try to find the most efficient way to access the data. The primary factor in determining the most effi- cient access plan is the I/O cost. (Remember, the DBMS always tries to minimize I/O operations.) Table 11.4 shows two sample access plans for the previous query and their respective I/O costs.
To make the example easier to understand, the I/O Operations and I/O Cost columns in Table 11.4 estimate only the number of I/O disk reads the DBMS must perform.
TABLE 11.4
COMPARING ACCESS PLANS AND I/O COSTS
PLAN STEP OPERATION I/O I/O COST RESULTING OPERATIONS SET ROWS
TOTAL I/O COST
A
A1
Cartesian product (PRODUCT, VENDOR)
7,000 + 300
7,300
2,100,000
7,300
A2
Select rows in A1 with matching vendor codes
2,100,000
2,100,000
7,000
2,107,300
A3
Select rows in A2 with V_STATE = ‘FL’
7,000
7,000
1,000
2,114,300
B
B1
Select rows in VENDOR with V_STATE = ‘FL’
300
300
10
300
B2
Cartesian Product (PRODUCT, B1)
7,000 + 10
7,010
70,000
7,310
B3
Select rows in B2 with matching vendor codes
70,000
70,000
1,000
77,310
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Note
For simplicity’s sake, it is assumed that there are no indexes and that each row read has an I/O cost of 1. For example, in Step A1, the DBMS must calculate the Cartesian product of PRODUCT and VENDOR. To do that, the DBMS must read all rows from PRODUCT (7,000) and all rows from VENDOR (300), yielding a total of 7,300 I/O operations. The same computation is done in all steps. In Table 11.4, you can see how Plan A has a total I/O cost that is almost 30 times higher than Plan B. In this case, the optimizer will choose Plan B to execute the SQL.
Not all DBMSs optimize SQL queries the same way. As a matter of fact, Oracle parses queries differently from the methods described in several sections in this chapter. Always read the documentation to examine the optimization requirements for your DBMS implementation.
optimizer hints
Special instructions for the query optimizer that are embedded inside the SQL command text.
Given the right conditions, some queries could be answered entirely by using only an index. For example, assume that you are using the PRODUCT table and the index P_QOH_NDX in the P_QOH attribute. Then a query such as SELECT MIN(P_QOH) FROM PRODUCT could be resolved by reading only the first entry in the P_QOH_ NDX index, without the need to access any of the data blocks for the PRODUCT table. (Remember that the index defaults to ascending order.)
You learned in Section 11-3 that columns with low sparsity are not good candidates for index creation. However, in some cases an index in a low-sparsity column would be helpful. For example, assume that the EMPLOYEE table has 122,483 rows. If you want to find out how many female employees work at the company, you would write a query such as:
SELECT COUNT(EMP_SEX) FROM EMPLOYEE WHERE EMP_SEX = ‘F’;
If you do not have an index for the EMP_SEX column, the query would have to perform a full table scan to read all EMPLOYEE rows—and each full row includes attributes you do not need. However, if you have an index on EMP_SEX, the query can be answered by reading only the index data, without the need to access the employee data at all.
11-4a Using Hints to Affect Optimizer Choices
Although the optimizer generally performs very well under most circumstances, in some instances the optimizer might not choose the best execution plan. Remember, the optimizer makes decisions based on the existing statistics. If the statistics are old, the optimizer might not do a good job in selecting the best execution plan. Even with current statistics, the optimizer’s choice might not be the most efficient one. Sometimes the end user would like to change the optimizer mode for the current SQL statement. To do that, you need to use hints. Optimizer hints are special instructions for the optimizer that are embedded inside the SQL command text. Table 11.5 summarizes a few of the most common optimizer hints used in standard SQL.
Now that you are familiar with the way the DBMS processes SQL queries, you can turn your attention to some general SQL coding recommendations to facilitate the work of the query optimizer.
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TABLE 11.5
OPTIMIZER HINTS
HINT USAGE
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ALL_ROWS
Instructs the optimizer to minimize the overall execution time—that is, to minimize the time needed to return all rows in the query result set. This hint is generally used for batch mode processes. For example:
SELECT FROM WHERE
/*+ ALL_ROWS */ * PRODUCT
P_QOH < 10; FIRST_ROWS Instructs the optimizer to minimize the time needed to process the first set of rows—that is, to minimize the time needed to return only the first set of rows in the query result set. This hint is generally used for interactive mode processes. For example: SELECT FROM WHERE /*+ FIRST_ROWS */ * PRODUCT P_QOH < 10; INDEX(name) Forces the optimizer to use the P_QOH_NDX index to process this query. For example: SELECT FROM WHERE /*+ INDEX(P_QOH_NDX) */ * PRODUCT P_QOH < 10 11-5 SQL Performance Tuning SQL performance tuning is evaluated from the client perspective. Therefore, the goal is to illustrate some common practices used to write efficient SQL code. A few words of caution are appropriate: • Most current-generation relational DBMSs perform automatic query optimization at the server end. • Most SQL performance optimization techniques are DBMS-specific and, therefore, are rarely portable, even across different versions of the same DBMS. Part of the reason for this behavior is the constant advancement in database technologies. Does this mean that you should not worry about how a SQL query is written because the DBMS will always optimize it? No, because there is considerable room for improve- ment. (The DBMS uses general optimization techniques rather than focusing on specific techniques dictated by the special circumstances of the query execution.) A poorly written SQL query can, and usually will, bring the database system to its knees from a perfor- mance point of view. The majority of current database performance problems are related to poorly written SQL code. Therefore, although a DBMS provides general optimizing services, a carefully written query almost always outperforms a poorly written one. Although SQL data manipulation statements include many different commands such as INSERT, UPDATE, DELETE, and SELECT, most recommendations in this section are related to the use of the SELECT statement, and in particular, the use of indexes and how to write conditional expressions. 11-5a Index Selectivity Indexes are the most important technique used in SQL performance optimization. The key is to know when an index is used. As a general rule, indexes are likely to be used: • When an indexed column appears by itself in the search criteria of a WHERE or HAVING clause Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 532 Part 4 Advanced Database Concepts • When an indexed column appears by itself in a GROUP BY or ORDER BY clause • When a MAX or MIN function is applied to an indexed column • When the data sparsity on the indexed column is high Indexes are very useful when you want to select a small subset of rows from a large table based on a given condition. If an index exists for the column used in the selection, the DBMS may choose to use it. The objective is to create indexes with high selectivity. Index selectivity is a measure of the likelihood that an index will be used in query processing. Here are some general guidelines for creating and using indexes: • Create indexes for each single attribute used in a WHERE, HAVING, ORDER BY, or GROUP BY clause. If you create indexes in all single attributes used in search condi- tions, the DBMS will access the table using an index scan instead of a full table scan. For example, if you have an index for P_PRICE, the condition P_PRICE > 10.00 can be solved by accessing the index instead of sequentially scanning all table rows and evaluating P_PRICE for each row. Indexes are also used in join expressions, such as in CUSTOMER.CUS_CODE = INVOICE.CUS_CODE.
• Do not use indexes in small tables or tables with low sparsity. Remember, small tables and low-sparsity tables are not the same thing. A search condition in a table with low sparsity may return a high percentage of table rows anyway, making the index opera- tion too costly and making the full table scan a viable option. Using the same logic, do not create indexes for tables with few rows and few attributes—unless you must ensure the existence of unique values in a column.
• Declare primary and foreign keys so the optimizer can use the indexes in join opera- tions. All natural joins and old-style joins will benefit if you declare primary keys and foreign keys because the optimizer will use the available indexes at join time. (The declaration of a PK or FK, primary key or foreign key, will automatically create an index for the declared column.) Also, for the same reason, it is better to write joins using the SQL JOIN syntax. (See Chapter 8, Advanced SQL.)
• Declare indexes in join columns other than PK or FK. If you perform join operations on columns other than the primary and foreign keys, you might be better off declaring indexes in those columns.
You cannot always use an index to improve performance. For example, using the data shown in Table 11.6 in the next section, the creation of an index for P_MIN will not help the search condition P_QOH > P_MIN * 1.10. The reason is that in some DBMSs, indexes are ignored when you use functions in the table attributes. However, major data- bases such as Oracle, SQL Server, and DB2 now support function-based indexes. A function-based index is an index based on a specific SQL function or expression. For example, you could create an index on YEAR(INV_DATE). Function-based indexes are especially useful when dealing with derived attributes. For example, you could create an index on EMP_SALARY + EMP_COMMISSION.
How many indexes should you create? It bears repeating that you should not create an index for every column in a table. Too many indexes will slow down INSERT, UPDATE, and DELETE operations, especially if the table contains many thousands of rows. Fur- thermore, some query optimizers will choose only one index to be the driving index for a query, even if your query uses conditions in many different indexed columns. Which index does the optimizer use? If you use the cost-based optimizer, the answer will change with time as new rows are added to or deleted from the tables. In any case, you should create indexes in all search columns and then let the optimizer choose. It is important to constantly evaluate the index usage—monitor, test, evaluate, and improve it if performance is not adequate.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
index selectivity
A measure of how likely an index is to be used in query processing.
function-based index
A type of index based on a specific SQL function or expression.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 11 Database Performance Tuning and Query Optimization 533 11-5b Conditional Expressions
A conditional expression is normally placed within the WHERE or HAVING clauses of a SQL statement. Also known as conditional criteria, a conditional expression restricts the output of a query to only the rows that match the conditional criteria. Generally, the conditional criteria have the form shown in Table 11.6.
TABLE 11.6
CONDITIONAL CRITERIA
OPERAND1 CONDITIONAL OPERATOR OPERAND2
In Table 11.6, note that an operand can be:
• A simple column name such as P_PRICE or V_STATE
• A literal or a constant such as the value 10.00 or the text ‘FL’
• An expression such as P_MIN * 1.10
Most of the query optimization techniques mentioned below are designed to make the optimizer’s work easier. The following common practices are used to write efficient conditional expressions in SQL code.
• Use simple columns or literals as operands in a conditional expression—avoid the use of conditional expressions with functions whenever possible. Comparing the contents of a single column to a literal is faster than comparing to expressions. For example, P_PRICE > 10.00 is faster than P_QOH > P_MIN * 1.10 because the DBMS must evaluate the P_MIN * 1.10 expression first. The use of functions in expressions also adds to the total query execution time. For example, if your condition is UPPER (V_NAME) = ‘JIM’, try to use V_NAME = ‘Jim’ if all names in the V_NAME column are stored with proper capitalization.
• Numeric field comparisons are faster than character, date, and NULL comparisons. In search conditions, comparing a numeric attribute to a numeric literal is faster than comparing a character attribute to a character literal. In general, the CPU handles numeric comparisons (integer and decimal) faster than character and date compari- sons. Because indexes do not store references to null values, NULL conditions involve additional processing, and therefore tend to be the slowest of all conditional operands.
• Equality comparisons are generally faster than inequality comparisons. For example, P_PRICE = 10.00 is processed faster because the DBMS can do a direct search using the index in the column. If there are no exact matches, the condition is evaluated as false. However, if you use an inequality symbol (>, >=, <, <=), the DBMS must per- form additional processing to complete the request, because there will almost always be more “greater than” or “less than” values in the index than “equal” values. With the exception of NULL, the slowest of all comparison operators is LIKE with wildcard symbols, as in V_CONTACT LIKE “%glo%”. Also, using the “not equal” symbol (< >) yields slower searches, especially when the sparsity of the data is high—that is, when there are many more different values than there are equal values.
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P_PRICE
>
10.00
V_STATE
=
FL
V_CONTACT
LIKE
Smith%
P_QOH
>
P_MIN * 1.10

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Note
• Whenever possible, transform conditional expressions to use literals. For example, if your condition is P_PRICE − 10 = 7, change it to read P_PRICE = 17. Also, if you have a composite condition such as:
P_QOH < P_MIN AND P_MIN = P_REORDER AND P_QOH = 10 change it to read: P_QOH = 10 AND P_MIN = P_REORDER AND P_MIN > 10
• When using multiple conditional expressions, write the equality conditions first. Note that this was done in the previous example. Remember, equality conditions are faster to process than inequality conditions. Although most RDBMSs will automatically do this for you, paying attention to this detail lightens the load for the query optimizer. The optimizer will not have to do what you have already done.
• If you use multiple AND conditions, write the condition most likely to be false first. If you use this technique, the DBMS will stop evaluating the rest of the conditions as soon as it finds a conditional expression that is evaluated as false. Remember, for multiple AND conditions to be found true, all conditions must be evaluated as true. If one of the conditions evaluates to false, the whole set of conditions will be evaluated as false. If you use this technique, the DBMS will not waste time unnecessarily evalu- ating additional conditions. Naturally, the use of this technique implies knowledge of the sparsity of the data set. For example, look at the following condition list:
P_PRICE > 10 AND V_STATE = ‘FL’
If you know that only a few vendors are located in Florida, you could rewrite this condition as:
V_STATE = ‘FL’ AND P_PRICE > 10
• When using multiple OR conditions, put the condition most likely to be true first. By doing this, the DBMS will stop evaluating the remaining conditions as soon as it finds a conditional expression that is evaluated as true. Remember, for multiple OR conditions to evaluate to true, only one of the conditions must be evaluated as true.
• Whenever possible, try to avoid the use of the NOT logical operator. It is best to trans- form a SQL expression that contains a NOT logical operator into an equivalent expression. For example:
NOT (P_PRICE > 10.00) can be written as P_PRICE <= 10.00. Also, NOT (EMP_SEX = 'M') can be written as EMP_SEX = 'F'. 11-6 Query Formulation Queries are usually written to answer questions. For example, if an end user gives you a sample output and tells you to match that output format, you must write the correspond- ing SQL. To get the job done, you must carefully evaluate what columns, tables, and Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Oracle does not evaluate queries as described here. Instead, Oracle evaluates conditions from last to first. Chapter 11 Database Performance Tuning and Query Optimization 535 computations are required to generate the desired output. To do that, you must have a good understanding of the database environment and the database that will be the focus of your SQL code. This section focuses on SELECT queries because they are the queries you will find in most applications. To formulate a query, you would normally follow these steps: 1. Identifywhatcolumnsandcomputationsarerequired.Thefirststepisneededtoclearly determine what data values you want to return. Do you want to return just the names and addresses, or do you also want to include some computations? Remember that all columns in the SELECT statement should return single values. a. Do you need simple expressions? For example, do you need to multiply the price by the quantity on hand to generate the total inventory cost? You might need some single attribute functions such as DATE(), SYSDATE(), or ROUND(). b. Do you need aggregate functions? If you need to compute the total sales by prod- uct, you should use a GROUP BY clause. In some cases, you might need to use a subquery. c. Determine the granularity of the raw data required for your output. Sometimes, you might need to summarize data that is not readily available in any table. In such cases, you might consider breaking the query into multiple subqueries and storing those subqueries as views. Then you could create a top-level query that joins those views and generates the final output. 2. Identify the source tables. Once you know what columns are required, you can deter- mine the source tables used in the query. Some attributes appear in more than one table. In those cases, try to use the least number of tables in your query to minimize the number of join operations. 3. Determine how to join the tables. Once you know what tables you need in your query statement, you must properly identify how to join the tables. In most cases, you will use some type of natural join, but in some instances, you might need to use an outer join. 4. Determine what selection criteria are needed. Most queries involve some type of selection criteria. In this case, you must determine what operands and operators are needed in your criteria. Ensure that the data type and granularity of the data in the comparison criteria are correct. a. Simple comparison. In most cases, you will be comparing single values—for example, P_PRICE > 10.
b. Single value to multiple values. If you are comparing a single value to multiple values, you might need to use an IN comparison operator—for example, V_STATE IN (‘FL’, ‘TN’, ‘GA’).
c. Nested comparisons. In other cases, you might need to have some nested selection criteria involving subqueries—for example, P_PRICE >= (SELECT AVG (P_PRICE) FROM PRODUCT).
d. Grouped data selection. On other occasions, the selection criteria might apply not to the raw data but to the aggregate data. In those cases, you need to use the HAVING clause.
5. Determine the order in which to display the output. Finally, the required output might be ordered by one or more columns. In those cases, you need to use the ORDER BY clause. Remember that the ORDER BY clause is one of the most resource-intensive operations for the DBMS.
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11-7 DBMS Performance Tuning
DBMS performance tuning includes global tasks such as managing the DBMS processes in primary memory (allocating memory for caching purposes) and managing the structures in physical storage (allocating space for the data files).
Fine-tuning the performance of the DBMS also includes applying several practices examined in the previous section. For example, the DBA must work with developers to ensure that the queries perform as expected—creating the indexes to speed up query response time and generating the database statistics required by cost-based optimizers.
DBMS performance tuning at the server end focuses on setting the parameters used for:
• Data cache. The data cache size must be set large enough to permit as many data requests as possible to be serviced from the cache. Each DBMS has settings that con- trol the size of the data cache; some DBMSs might require a restart. This cache is shared among all database users. The majority of primary memory resources will be allocated to the data cache.
• SQL cache. The SQL cache stores the most recently executed SQL statements (after the SQL statements have been parsed by the optimizer). Generally, if you have an applica- tion with multiple users accessing a database, the same query will likely be submitted by many different users. In those cases, the DBMS will parse the query only once and execute it many times, using the same access plan. In that way, the second and subse- quent SQL requests for the same query are served from the SQL cache, skipping the parsing phase.
• Sort cache. The sort cache is used as a temporary storage area for ORDER BY or GROUP BY operations, as well as for index-creation functions.
• Optimizer mode. Most DBMSs operate in one of two optimization modes: cost-based or rule-based. Others automatically determine the optimization mode based on whether database statistics are available. For example, the DBA is responsible for gen- erating the database statistics that are used by the cost-based optimizer. If the statistics are not available, the DBMS uses a rule-based optimizer.
From the performance point of view, it would be optimal to have the entire database stored in primary memory to minimize costly disk access. This is why several data- base vendors offer in-memory database options for their main products. In-memory database systems are optimized to store large portions (if not all) of the database in primary (RAM) storage rather than secondary (disk) storage. These systems are becom- ing popular because increasing performance demands of modern database applications (such as Business Analytics and Big Data), diminishing costs, and technology advances of components (such as flash-memory and solid state drives.) Even though these type of databases “eliminate” disk access bottlenecks, they are still subject to query optimization and performance tuning rules, especially when faced with poorly designed databases or poorly written SQL statements.
Although in-memory databases are carving a niche in selected markets, most data- base implementations still rely on data stored on disk drives. That is why managing the physical storage details of the data files plays an important role in DBMS performance tuning. Note the following general recommendations for physical storage of databases:
• Use I/O accelerators. This type of device uses flash solid-state drives (SSD) to store the database. A solid-state drive does not have any moving parts and, therefore performs I/O operations at a higher speed than traditional rotating disk drives. I/O accelerators deliver high transaction performance rates and reduce contention caused by typical storage drives.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
in-memory database
A database optimized to store large portions (if not all) of the database in primary (RAM) storage rather than secondary (disk) storage.
I/O accelerator
A device used to improve throughput for input/output operations.
Copyright 2017 Cengage Lea Editorial review has deemed that

• Use RAID (Redundant Array of Independent Disks) to provide both performance improvement and fault tolerance, and a balance between them. Fault tolerance means that in case of failure, data can be reconstructed and retrieved. RAID systems use multiple disks to create virtual disks (storage volumes) formed by several individual disks. Table 11.7 describes the most common RAID configurations.
TABLE 11.7
COMMON RAID LEVELS
RAID LEVEL DESCRIPTION
Chapter 11 Database Performance Tuning and Query Optimization 537
0
The data blocks are spread over separate drives. Also known as striped array. Provides increased performance but no fault tolerance. Requires a minimum of two drives.
1
The same data blocks are written (duplicated) to separate drives. Also referred to as mirroring or duplexing. Provides increased read performance and fault tolerance via data redundancy. Requires a minimum of two drives.
3
The data is striped across separate drives, and parity data is computed and stored in a dedicated drive. (Parity data is specially generated data that permits the reconstruction of corrupted or missing data.) Provides good read performance and fault tolerance via parity data. Requires a minimum of three drives.
5
The data and the parity data is striped across separate drives. Provides good read performance and fault tolerance via parity data. Requires a minimum of three drives.
1+0
The data blocks are spread over separate drives and mirrored (duplicated). This arrangement provides both speed and fault tolerance. This is the recommended RAID configuration for most database installations (if cost is not an issue).
• Minimize disk contention. Use multiple, independent storage volumes with indepen- dent spindles (rotating disks) to minimize hard disk cycles. Remember, a database is composed of many table spaces, each with a particular function. In turn, each table space is composed of several data files in which the data is actually stored. A database should have at least the following table spaces:
– System table space. This is used to store the data dictionary tables. It is the most frequently accessed table space and should be stored in its own volume.
– User data table space. This is used to store end-user data. You should create as many user data table spaces and data files as are required to balance performance and usability. For example, you can create and assign a different user data table space for each application and each distinct group of users, but this is not necessary for each user.
– Index table space. This is used to store indexes. You can create and assign a different index table space for each application and each group of users. The index table space data files should be stored on a storage volume that is separate from user data files or system data files.
– Temporary table space. This is used as a temporary storage area for merge, sort, or set aggregate operations. You can create and assign a different temporary table space for each application and each group of users.
– Rollback segment table space. This is used for transaction-recovery purposes.
• Put high-usage tables in their own table spaces so the database minimizes conflict
with other tables.
RAID
An acronym for Redundant Array of Independent Disks. RAID systems use multiple disks to create virtual disks (storage volumes) from several individual disks. RAID systems provide performance improvement, fault tolerance, and a balance between the two.
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• Assign separate data files in separate storage volumes for the indexes, system, and high-usage tables. This ensures that index operations will not conflict with end-user data or data dictionary table access operations. Another advantage of this approach is that you can use different disk block sizes in different volumes. For example, the data volume can use a 16 K block size, while the index volume can use an 8 K block size. Remember that the index record size is generally smaller, and by changing the block size you will reduce contention and minimize I/O operations. This is very important; many database administrators overlook indexes as a source of contention. By using separate storage volumes and different block sizes, the I/O operations on data and indexes will happen asynchronously (at different times); more importantly, the likelihood of write operations blocking read operations is reduced, as page locks tend to lock fewer records.
• Take advantage of the various table storage organizations available in the database. For example, in Oracle consider the use of index-organized tables (IOT); in SQL Server, consider clustered index tables. An index-organized table (or clustered index table) is a table that stores the end-user data and the index data in consecutive locations on permanent storage. This type of storage organization provides a perfor- mance advantage to tables that are commonly accessed through a given index order, because the index contains the index key as well as the data rows. Therefore, the DBMS tends to perform fewer I/O operations.
• Partition tables based on usage. Some RDBMSs support the horizontal partitioning of tables based on attributes. (See Chapter 12, Distributed Database Management Systems.) By doing so, a single SQL request can be processed by multiple data processors. Put the table partitions closest to where they are used the most.
• Use denormalized tables where appropriate. In other words, you might be able to improve performance by taking a table from a higher normal form to a lower normal form—typically, from third to second normal form. This technique adds data duplica- tion, but it minimizes join operations. (Denormalization was discussed in Chapter 6, Normalization of Database Tables.)
• Store computed and aggregate attributes in tables. In short, use derived attributes in your tables. For example, you might add the invoice subtotal, the amount of tax, and the total in the INVOICE table. Using derived attributes minimizes computations in queries and join operations, especially during the execution of aggregate queries.
11-8 Query Optimization Example
Now that you have learned the basis of query optimization, you are ready to test your new knowledge. A simple example illustrates how the query optimizer works and how you can help it work. The example is based on the QOVENDOR and QOPRODUCT tables, which are similar to tables you used in previous chapters. However, the QO prefix is used for the table name to ensure that you do not overwrite previous tables.
To perform this query optimization example, you will use the Oracle SQL*Plus interface. Some preliminary work must be done before you can start testing query optimization, as explained in the following steps:
1. LogintoOracleSQL*Plususingtheusernameandpasswordprovidedbyyourinstructor.
2. Create a fresh set of tables, using the QRYOPTDATA.SQL script file (available at www.cengagebrain.com). This step is necessary so that Oracle has a new set of tables and the new tables contain no statistics. At the SQL> prompt, type:
@path\QRYOPTDATA.SQL
where path is the location of the file in your computer.
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index organized table
In a DBMS, a type
of table storage organization that stores end-user data and index data in consecutive locations in permanent storage. Also known as cluster-indexed table.
clustered index table
See index organized table.
Copyright 2017 Cengage Le Editorial review has deemed that

Chapter 11 Database Performance Tuning and Query Optimization 539
3. Create the PLAN_TABLE, which is a special table used by Oracle to store the access plan information for a given query. End users can then query the PLAN_ TABLE to see how Oracle will execute the query. To create the PLAN_TABLE, run the UTLXPLAN.SQL script file in the RDBMS\ADMIN folder of your Oracle RDBMS installation. The UTLXPLAN.SQL script file is also available at www. cengagebrain.com. At the SQL prompt, type:
@path\UTLXPLAN.SQL
You use the EXPLAIN PLAN command to store the execution plan of a SQL query in the PLAN_TABLE. Then, you use the SELECT * FROM TABLE(DBMS_XPLAN. DISPLAY) command to display the access plan for a given SQL statement.
Note
To see the access plan used by the DBMS to execute your query, use the EXPLAIN PLAN and SELECT statements, as shown in Figure 11.5. Note that the first SQL state- ment generates the statistics for the QOVENDOR table. Also, the initial access plan in Figure 11.5 uses a full table scan on the QOVENDOR table, and the cost of the plan is 4.
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Oracle 12c, MySQL, and SQL Server all default to cost-based optimization. In Oracle, if table statistics are not available, the DBMS will fall back to a rule-based optimizer.
FIGURE11.5 INITIALEXPLAINPLAN

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Now create an index on V_AREACODE (note that V_AREACODE is used in the ORDER BY clause) and see how it affects the access plan generated by the cost-based optimizer. The results are shown in Figure 11.6.
In Figure 11.6, note that the new access plan cuts the cost of executing the query by 30 percent! Also note that this new plan scans the QOV_NDX1 index and accesses the QOVENDOR rows, using the index row ID. (Remember that access by row ID is one of the fastest access methods.) In this case, the creation of the QOV_NDX1 index had a positive impact on overall query optimization results.
At other times, indexes do not necessarily help in query optimization, such as when you have indexes on small tables or when the query accesses a great percentage of table rows anyway. Note what happens when you create an index on V_NAME. The new access plan is shown in Figure 11.7. (Note that V_NAME is used on the WHERE clause as a conditional expression operand.)
As you can see in Figure 11.7, creation of the second index did not help the query optimization. However, on some occasions an index might be used by the optimizer, but it is not executed because of the way the query is written. For example, Figure 11.8 shows the access plan for a different query using the V_NAME column.
FIGURE11.6 EXPLAINPLANAFTERINDEXONV_AREACODE
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Chapter 11 Database Performance Tuning and Query Optimization 541
FIGURE11.7 EXPLAINPLANAFTERINDEXONV_NAME
FIGURE11.8 ACCESSPLANUSINGINDEXONV_NAME
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In Figure 11.8, note that the access plan for this new query uses the QOV_NDX2 index on the V_NAME column. What would happen if you wrote the same query, using the UPPER function on V_NAME? The results are illustrated in Figure 11.9.
As Figure 11.9 shows, the use of a function on an indexed column caused the DBMS to perform additional operations that could potentially increase the cost of the query. The same query might produce different costs if your tables contain many more rows and if the index sparsity is different.
Now use the QOPRODUCT table to demonstrate how an index can help when aggre- gate function queries are being run. For example, Figure 11.10 shows the access plan for a SELECT statement using the MAX(P_PRICE) aggregate function. This plan uses a full table scan with a total cost of 3.
A cost of 3 is very low already, but you could improve the previous query performance by creating an index on P_PRICE. Figure 11.11 shows how the plan cost is reduced by two-thirds after the index is created and the QOPRODUCT table is analyzed. Also note that the second version of the access plan uses only the index QOP_NDX2 to answer the query; the QOPRODUCT table is never accessed.
FIGURE11.9 ACCESSPLANUSINGFUNCTIONSONINDEXEDCOLUMNS
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Chapter 11 Database Performance Tuning and Query Optimization 543
FIGURE11.10 FIRSTEXPLAINPLAN:AGGREGATEFUNCTIONONANON-INDEXEDCOLUMN
FIGURE11.11 SECONDEXPLAINPLAN:AGGREGATEFUNCTIONONANINDEXEDCOLUMN
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544 Part 4
Advanced Database Concepts
Although the few examples in this section show the importance of proper index selection for query optimization, you also saw examples in which index creation does not improve query performance. As a DBA, you should be aware that the main goal is to optimize overall database performance—not just for a single query but for all requests and query types. Most database systems provide advanced graphical tools for performance monitoring and testing. For example, Figures 11.12, 11.13, and 11.4 show the graphical representation of the access plan using Oracle, MySQL, and MS SQL Server tools.
FIGURE11.12 ORACLETOOLSFORQUERYOPTIMIZATION
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Chapter 11 Database Performance Tuning and Query Optimization 545
FIGURE11.13 MYSQLTOOLSFORQUERYOPTIMIZATION
FIGURE11.14 MICROSOFTSQLSERVERTOOLSFORQUERYOPTIMIZATION
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546 Part 4 Advanced Database Concepts
Summary
• Database performance tuning refers to a set of activities and procedures designed to ensure that an end-user query is processed by the DBMS in the least amount of time. SQL performance tuning refers to activities on the client side that are designed to gen- erate SQL code that returns the correct answer in the least amount of time, using the minimum amount of resources at the server end. DBMS performance tuning refers to activities on the server side that are oriented so the DBMS is properly configured to respond to clients’ requests in the fastest way possible while making optimum use of existing resources.
• Database statistics refer to a number of measurements gathered by the DBMS that describe a snapshot of the database objects’ characteristics. The DBMS gathers statistics about objects such as tables, indexes, and available resources, which include the number of processors used, processor speed, and temporary space available. The DBMS uses the statistics to make critical decisions about improving query processing efficiency.
• DBMSs process queries in three phases. In the parsing phase, the DBMS parses the SQL query and chooses the most efficient access/execution plan. In the execution phase, the DBMS executes the SQL query using the chosen execution plan. In the fetching phase, the DBMS fetches the data and sends the result set back to the client.
• Indexes are crucial in the process that speeds up data access. Indexes facilitate search- ing, sorting, and using aggregate functions and join operations. The improvement in data access speed occurs because an index is an ordered set of values that contains the index key and pointers. Data sparsity refers to the number of different values a column could have. Indexes are recommended in high-sparsity columns used in search conditions.
• During query optimization, the DBMS must choose what indexes to use, how to perform join operations, which table to use first, and so on. Each DBMS has its own algorithms for determining the most efficient way to access the data. The two most common approaches are rule-based and cost-based optimization.
• A rule-based optimizer uses preset rules and points to determine the best approach to execute a query. A cost-based optimizer uses sophisticated algorithms based on statistics about the objects being accessed to determine the best approach to execute a query. In this case, the optimizer process adds up the processing cost, the I/O costs, and the resource costs (RAM and temporary space) to determine the total cost of a given execution plan.
• SQL performance tuning deals with writing queries that make good use of the statistics. In particular, queries should make good use of indexes. Indexes are very useful when you want to select a small subset of rows from a large table based on a condition.
• Query formulation deals with how to translate business questions into specific SQL code to generate the required results. To do this, you must carefully evaluate which columns, tables, and computations are required to generate the desired output.
• DBMS performance tuning includes tasks such as managing the DBMS processes in primary memory (allocating memory for caching purposes) and managing the structures in physical storage (allocating space for the data files).
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Chapter 11 Database Performance Tuning and Query Optimization 547
1. What is SQL performance tuning?
2. What is database performance tuning?
3. What is the focus of most performance-tuning activities, and why does that focus exist?
4. What are database statistics, and why are they important?
5. How are database statistics obtained?
6. What database statistics measurements are typical of tables, indexes, and resources?
7. How is the processing of SQL DDL statements (such as CREATE TABLE) different from the processing required by DML statements?
8. In simple terms, the DBMS processes a query in three phases. What are the phases, and what is accomplished in each phase?
9. If indexes are so important, why not index every column in every table? (Include a brief discussion of the role played by data sparsity.)
10. What is the difference between a rule-based optimizer and a cost-based optimizer?
11. What are optimizer hints, and how are they used?
12. What are some general guidelines for creating and using indexes?
13. Most query optimization techniques are designed to make the optimizer’s work easier. What factors should you keep in mind if you intend to write conditional expressions in SQL code?
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Key Terms
access plan
automatic query optimization
bitmap index
B-tree index
buffer cache
clustered index table
cost-based optimizer
database performance tuning
database statistics
data cache
data files
data sparsity
DBMS performance tuning
dynamic query optimization
dynamic statistical generation mode
extents
file group
function-based index
hash index
in-memory database
index-organized table
index selectivity
input/output (I/O) request
I/O accelerator
manual query optimization
manual statistical generation mode
optimizer hints
procedure cache
query optimizer
query processing bottleneck
RAID
rule-based optimizer
rule-based query optimization algorithm
static query optimization
statistically based query optimization algorithm
SQL cache
SQL performance tuning table space
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
Review Questions

548 Part 4 Advanced Database Concepts
14. What recommendations would you make for managing the data files in a DBMS
with many tables and indexes?
15. What does RAID stand for, and what are some commonly used RAID levels?
Problems
Problems 1 and 2 are based on the following query:
SELECT FROM WHERE ORDER BY
EMP_LNAME, EMP_FNAME, EMP_AREACODE, EMP_SEX EMPLOYEE
EMP_SEX = ‘F’ AND EMP_AREACODE = ‘615’ EMP_LNAME, EMP_FNAME;
1. What is the likely data sparsity of the EMP_SEX column?
2. What indexes should you create? Write the required SQL commands.
3. Using Table 11.4 as an example, create two alternative access plans. Use the following assumptions:
a. There are 8,000 employees.
b. There are 4,150 female employees.
c. There are 370 employees in area code 615.
d. There are 190 female employees in area code 615.
Problems 4−6 are based on the following query:
SELECT FROM WHERE
EMP_LNAME, EMP_FNAME, EMP_DOB, YEAR(EMP_DOB) AS YEAR EMPLOYEE
YEAR(EMP_DOB) = 1976;
4. What is the likely data sparsity of the EMP_DOB column?
5. Should you create an index on EMP_DOB? Why or why not?
6. What type of database I/O operations will likely be used by the query? (See Table 11.3.)
Problems 7−10 are based on the ER model shown in Figure P11.7 and on the query shown after the figure.
SELECT FROM WHERE
P_CODE, P_PRICE
PRODUCT
P_PRICE >= (SELECT AVG(P_PRICE) FROM PRODUCT);
7. Assuming there are no table statistics, what type of optimization will the DBMS use?
8. What type of database I/O operations will likely be used by the query? (See Table 11.3.)
9. What is the likely data sparsity of the P_PRICE column?
10. Should you create an index? Why or why not?
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SELECT FROM GROUP BY HAVING
P_CODE, SUM(LINE_UNITS)
LINE
P_CODE
SUM(LINE_UNITS) > (SELECT MAX(LINE_UNITS) FROM LINE);
11. What is the likely data sparsity of the LINE_UNITS column?
12. Should you create an index? If so, what would the index column(s) be, and why would you create the index? If not, explain your reasoning.
13. ShouldyoucreateanindexonP_CODE?Ifso,writetheSQLcommandtocreatethe index. If not, explain your reasoning.
14. Write the command to create statistics for this table.
Problems 15 and 16 are based on the following query:
SELECT FROM WHERE
P_CODE, P_QOH*P_PRICE PRODUCT
P_QOH*P_PRICE > (SELECT AVG(P_QOH*P_PRICE) FROM PRODUCT);
Chapter 11 Database Performance Tuning and Query Optimization 549
FIGURE P11.7 THE CH11_SALECO ER MODEL
Problems 11−14 are based on the following query:
15. What is the likely data sparsity of the P_QOH and P_PRICE columns?
16. Should you create an index? If so, what would the index column(s) be, and why should you create the index?
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550 Part 4
Advanced Database Concepts
Problems 17−21 are based on the following query:
SELECT FROM WHERE ORDER BY
V_CODE, V_NAME, V_CONTACT, V_STATE VENDOR
V_STATE = ‘TN’
V_NAME;
17. What indexes should you create and why? Write the SQL command to create the indexes.
18. Assume that 10,000 vendors are distributed as shown in Table P11.18. What percentage of rows will be returned by the query?
TABLE P11.18
STATE NUMBER OF VENDORS STATE NUMBER OF VENDORS
AK 15 MS 47
AL 55 NC 358
AZ 100 NH 25
CA 3244 NJ 645
CO 345 NV 16
FL 995 OH 821
GA 75 OK 62
HI 68 PA 425
IL 89 RI 12
IN 12 SC 65
KS 19 SD 74
KY 45 TN 113
LA 29 TX 589
MD 208 UT 36
MI 745 VA 375
MO 35 WA 258
19. What type of I/O database operations would most likely be used to execute the query?
20. Using Table 11.4 as an example, create two alternative access plans.
21. Assume that you have 10,000 different products stored in the PRODUCT table and that you are writing a web-based interface to list all products with a quantity on hand (P_QOH) that is less than or equal to the minimum quantity, P_MIN. What optimizer hint would you use to ensure that your query returns the result set to the web interface in the least time possible? Write the SQL code.
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Chapter 11 Database Performance Tuning and Query Optimization 551 Problems 22−24 are based on the following query:
SELECT FROM WHERE
ORDER BY
P_CODE, P_DESCRIPT, P_PRICE, P.V_CODE, V_STATE PRODUCT P, VENDOR V
P.V_CODE = V.V_CODE
AND V_STATE = ‘NY’
AND V_AREACODE = ‘212’ P_PRICE;
22. What indexes would you recommend?
23. WritethecommandsrequiredtocreatetheindexesyourecommendedinProblem22.
24. Write the command(s) used to generate the statistics for the PRODUCT and VENDOR tables.
Problems 25 and 26 are based on the following query:
SELECT FROM WHERE ORDER BY
P_CODE, P_DESCRIPT, P_QOH, P_PRICE, V_CODE PRODUCT
V_CODE = ‘21344’
P_CODE;
25. What index would you recommend, and what command would you use?
26. How should you rewrite the query to ensure that it uses the index you created in your solution to Problem 25?
Problems 27 and 28 are based on the following query:
SELECT FROM WHERE
ORDER BY
P_CODE, P_DESCRIPT, P_QOH, P_PRICE, V_CODE PRODUCT
P_QOH < P_MIN AND P_MIN = P_REORDER AND P_REORDER = 50 P_QOH; 27. UsetherecommendationsgiveninSection11-5btorewritethequeryandproduce the required results more efficiently. 28. Whatindexeswouldyourecommend?Writethecommandstocreatethoseindexes. Problems 29−32 are based on the following query: SELECT FROM WHERE GROUP BY CUS_CODE, MAX(LINE_UNITS*LINE_PRICE) CUSTOMER NATURAL JOIN INVOICE NATURAL JOIN LINE CUS_AREACODE = '615' CUS_CODE; Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 552 Part 4 Advanced Database Concepts 29. Assuming that you generate 15,000 invoices per month, what recommendation would you give the designer about the use of derived attributes? 30. AssumingthatyoufollowtherecommendationsyougaveinProblem29,howwould you rewrite the query? 31. What indexes would you recommend for the query you wrote in Problem 30, and what SQL commands would you use? 32. How would you rewrite the query to ensure that the index you created in Problem 31 is used? Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 12 Distributed Database Management Systems In this chapter, you will learn: • About distributed database management systems (DDBMSs) and their components • How database implementation is affected by different levels of data and process distribution • How transactions are managed in a distributed database environment • How distributed database design draws on data partitioning and replication to balance performance, scalability, and availability • About the trade-offs of implementing a distributed data system Preview In this chapter, you will learn that a single database can be divided into several fragments stored on different computers within a geographically dispersed network. Processing also can be dispersed among several different network sites, or nodes. The growth of distributed database systems has been fostered by the increased global- ization of business operations, the accumulation of massive organizational data sets, and technological changes that have made distributed network-based services practical, more reliable, and cost-effective. The distributed database management system (DDBMS) treats a distributed database as a single logical database; therefore, the basic design concepts you learned in earlier chap- ters apply. However, the distribution of data among different sites in a computer network adds to the system’s complexity. For example, the design of a distributed database must consider the location of the data, partitioning the data into fragments, and replication of those fragments. Although a distributed database system requires a more sophisticated DBMS, the greater complexity of a distributed database system should be transparent to the end user. In today’s web-centric environment, any distributed data system must be highly scal- able; in other words, it must grow dynamically as demand increases. To accommodate such dynamic growth, trade-offs must be made to achieve some desirable properties. Data Files and Available Formats CH12_Text P P P P Data Files Available on cengagebrain.com MS Access Oracle MS SQL My SQL MS Access Oracle MS SQL My SQL Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 554 Part 4 Advanced Database Concepts 12-1 The Evolution of Distributed Database Management Systems A distributed database management system (DDBMS) governs the storage and pro- cessing of logically related data over interconnected computer systems in which both data and processing are distributed among several sites. To understand how and why the DDBMS is different from the DBMS, it is useful to briefly examine the changes in the business environment that set the stage for the development of the DDBMS. During the 1970s, corporations implemented centralized database management sys- tems to meet their structured information needs. The use of a centralized database required that corporate data be stored in a single central site, usually a mainframe computer. Data access was provided through dumb terminals. The centralized approach, illustrated in Figure 12.1, worked well to fill the structured information needs of corporations, but it fell short when quickly moving events required faster response times and equally quick access to information. The slow progression from information request to approval to specialist to user simply did not serve decision makers well in a dynamic environment. What was needed was quick, unstructured access to databases, using ad hoc queries to generate on-the-spot information. The last two decades gave birth to a series of crucial social and technological changes that affected the nature of the systems and the data they use: • Business operations became global; with this change, competition expanded from the shop on the next corner to the web store in cyberspace. • Customer demands and market needs favored an on-demand transaction style, mostly based on web-based services. • Rapid social and technological changes fueled by low-cost, smart mobile devices increased the demand for complex and fast networks to interconnect them. As a con- sequence, corporations have increasingly adopted advanced network technologies as the platform for their computerized solutions. See Chapter 15, Database Connectivity and Web Technologies, for a discussion of cloud-based services. • Data realms are converging in the digital world more frequently. As a result, appli- cations must manage multiple types of data, such as voice, video, music, and images. Such data tends to be geographically distributed and remotely accessed from diverse locations via location-aware mobile devices. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE12.1 CENTRALIZEDDATABASEMANAGEMENTSYSTEM DBMS Request Reply End user Data Read Application issues a data request to the DBMS distributed database management system (DDBMS) A DBMS that supports a database distributed across several different sites; a DDBMS governs the storage and processing of logically related data over interconnected computer systems in which both data and processing functions are distributed among several sites. Copyright 2017 Cengage Lea Editorial review has deemed that e s a b a t a L o c a l d • The advent of social media as a way to reach new customers and open new markets has fueled the need to store large amounts of digital data and created a revolution in the way data is managed and mined for knowledge. Businesses are looking for new ways to gain business intelligence through the analysis of vast stores of structured and unstructured data. These factors created a dynamic business environment in which companies had to respond quickly to competitive and technological pressures. As large business units restructured to form leaner, quickly reacting, dispersed operations, two database requirements became obvious: • Rapid ad hoc data access became crucial in the quick-response decision-making environment. • Distributed data access was needed to support geographically dispersed business units. During recent years, these factors became even more firmly entrenched. However, the way they were addressed was strongly influenced by the following factors: • The growing acceptance of the Internet as the platform for data access and distribution. The web is effectively the repository for distributed data. • The mobile wireless revolution. The widespread use of mobile wireless digital devices includes smartphones and tablets. These devices have created high demand for data access. They access data from geographically dispersed locations and require varied data exchanges in multiple formats, such as data, voice, video, music, and pictures. Although distributed data access does not necessarily imply distributed databases, per- formance and failure tolerance requirements often lead to the use of data replication techniques similar to those in distributed databases. • The accelerated growth of companies using “applications as a service.” This new type of service provides remote applications to companies that want to outsource their application development, maintenance, and operations. The company data is gener- ally stored on central servers and is not necessarily distributed. Just as with mobile data access, this type of service may not require fully distributed data functionality; however, other factors such as performance and failure tolerance often require the use of data replication techniques similar to those in distributed databases. • The increased focus on mobile business intelligence. More and more companies are embracing mobile technologies within their business plans. As companies use social networks to get closer to customers, the need for on-the-spot decision making increases. Although a data warehouse is not usually a distributed database, it does rely on techniques such as data replication and distributed queries that facilitate data extraction and integration. (You will learn more about this topic in Chapter 13, Business Intelligence and Data Warehouses.) • Emphasis on Big Data analytics. The era of mobile communications unraveled an avalanche of data from many sources and of many types. Today’s customers have significant influence on the spending habits of communities, and organizations are investing in ways to harvest such data to “discover” new ways to effectively and efficiently reach customers. At this point, the long-term impact of the Internet and the mobile revolution on distributed database design and management is just starting to be felt. Perhaps the success of the Internet and mobile technologies will foster the use of distributed databases as bandwidth becomes a less troublesome bottleneck. Perhaps the resolution of bandwidth problems will simply confirm the centralized database standard. In any case, distributed Online Content To learn more about the Internet’s impact on data access and distribution, see Appendix I, Databases in Electronic Commerce, at www.cengagebrain.com. Chapter 12 Distributed Database Management Systems 555 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 556 Part 4 Advanced Database Concepts database concepts and components are likely to find a place in future database develop- ment, particularly for specialized mobile and location-aware applications. The distributed database is especially desirable because centralized database manage- ment is subject to problems such as: • Performance degradation because of a growing number of remote locations over greater distances. • High costs associated with maintaining and operating large central (mainframe) data- base systems and physical infrastructure. • Reliability problems created by dependence on a central site (single point of failure syndrome) and the need for data replication. • Scalability problems associated with the physical limits imposed by a single location, such as physical space, temperature conditioning, and power consumption. • Organizational rigidity imposed by the database, which means it might not support the flexibility and agility required by modern global organizations. The dynamic business environment and the centralized database’s shortcomings spawned a demand for applications based on accessing data from different sources at multiple locations. Such a multiple-source/multiple-location database environment is best managed by a DDBMS. 12-2 DDBMS Advantages and Disadvantages Distributed database management systems deliver several advantages over traditional systems. At the same time, they are subject to some problems. Table 12.1 summarizes the advantages and disadvantages associated with a DDBMS. Distributed databases are being used successfully in many web staples such as Google and Amazon, but they still have a long way to go before they yield the full flexibility and power they theoretically possess. The remainder of this chapter explores the basic components and concepts of the distributed database. Because the distributed database is usually based on the relational database model, relational terminology is used to explain the basic concepts and com- ponents. Even though some of the most widely used distributed databases are part of the NoSQL movement (see Chapter 2, Data Models), the basic concepts and fundamentals of distributed data still apply to them. 12-3 Distributed Processing and Distributed Databases In distributed processing, a database’s logical processing is shared among two or more physically independent sites that are connected through a network. For example, the data input/output (I/O), data selection, and data validation might be performed on one computer, and a report based on that data might be created on another computer. A basic distributed processing environment is illustrated in Figure 12.2, which shows that a distributed processing system shares the database processing chores among three sites connected through a communications network. Although the database resides at only one site (Miami), each site can access the data and update the database. The data- base is located on Computer A, a network computer known as the database server. A distributed database, on the other hand, stores a logically related database over two or more physically independent sites. The sites are connected via a computer rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. distributed processing Sharing the logical processing of a database over two or more sites connected by a network. distributed database A logically related database that is stored in two or more physically independent sites. Copyright 2017 Cengage Lea Editorial review has deemed that TABLE 12.1 DISTRIBUTED DBMS ADVANTAGES AND DISADVANTAGES ADVANTAGES DISADVANTAGES Chapter 12 Distributed Database Management Systems 557 Data is located near the site of greatest demand. The data in a distributed database system is dispersed to match business requirements. Complexity of management and control. Applications must recognize data location, and they must be able to stitch together data from various sites. Database admin- istrators must have the ability to coordinate database activities to prevent database degradation due to data anomalies. Faster data access. End users often work with only the nearest stored subset of the data. Technological difficulty. Data integrity, transaction management, concurrency control, security, backup, recovery, and query optimization must all be addressed and resolved. Faster data processing. A distributed database system spreads out the system’s workload by processing data at several sites. Security. The probability of security lapses increases when data is located at multiple sites. The responsibility of data management will be shared by different people at several sites. Growth facilitation. New sites can be added to the net- work without affecting the operations of other sites. Lack of standards. There are no standard communication protocols at the database level. For example, different database vendors employ different and often incompat- ible techniques to manage the distribution of data and processing in a DDBMS environment. Improved communications. Because local sites are smaller and located closer to customers, local sites foster better communication among departments and between customers and company staff. Increased storage and infrastructure requirements. Multiple copies of data are required at different sites, thus requiring additional storage space. Reduced operating costs. It is more cost-effective to add nodes to a network than to update a mainframe system. Development work is done more cheaply and quickly on low-cost PCs than on mainframes. Increased training cost. Training costs are generally higher in a distributed model than they would be in a centralized model, sometimes even to the extent of offsetting opera- tional and hardware savings. User-friendly interface. PCs and workstations are usually equipped with an easy-to-use graphical user interface (GUI). The GUI simplifies training and use for end users. Costs. Distributed databases require duplicated infrastruc- ture to operate, such as physical location, environment, personnel, software, and licensing. Less danger of a single-point failure. When one of the computers fails, the workload is picked up by other workstations. Data is also distributed at multiple sites. Processor independence. The end user can access any available copy of the data, and an end user’s request is processed by any processor at the data location. network. In contrast, the distributed processing system uses only a single-site database but shares the processing chores among several sites. In a distributed database system, a database is composed of several parts known as database fragments. The database fragments are located at different sites and can be replicated among various sites. Each database fragment is, in turn, managed by its local database process. An example of a distributed database environment is shown in Figure 12.3. The database in Figure 12.3 is divided into three database fragments (E1, E2, and E3) located at different sites. The computers are connected through a network system. In a fully distributed database, the users Alan, Betty, and Hernando do not need to know the name or location of each database fragment in order to access the database. Also, the database fragment A subset of a distributed database. Although the fragments may be stored at different sites within a computer network, the set of all fragments is treated as a single database. See also horizontal fragmentation and vertical fragmentation. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 558 Part 4 Advanced Database Concepts FIGURE12.2 DISTRIBUTEDPROCESSINGENVIRONMENT Site 1 Miami user Joe Site 2 New York user Donna Computer B Computer A DBMS Site 3 Atlanta user Victor Computer C Communications network Database records are processed in different locations FIGURE12.3 DISTRIBUTEDDATABASEENVIRONMENT Site 1 Miami user Alan Computer B DBMS Computer A Communications network DBMS DBMS Computer C Site 2 New York user Betty Site 3 Atlanta user Hernando users might be at sites other than Miami, New York, or Atlanta and still be able to access the database as a single logical unit. As you examine Figures 12.2 and 12.3, keep the following points in mind: • Distributed processing does not require a distributed database, but a distributed data- base requires distributed processing. (Each database fragment is managed by its own local database process.) • Distributed processing may be based on a single database located on a single com- puter. For the management of distributed data to occur, copies or parts of the database processing functions must be distributed to all data storage sites. • Both distributed processing and distributed databases require a network of intercon- nected components. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. e s a b a t a d E m p l o y e e Update payroll data Generate payroll report 23 1 E E E Chapter 12 Distributed Database Management Systems 559 12-4 Characteristics of Distributed Database Management Systems A DDBMS governs the storage and processing of logically related data over intercon- nected computer systems in which both data and processing functions are distributed among several sites. A DBMS must have at least the following functions to be classified as distributed: • Application interface to interact with the end user, application programs, and other DBMSs within the distributed database • Validation to analyze data requests for syntax correctness • Transformation to decompose complex requests into atomic data request components • Query optimization to find the best access strategy (which database fragments must be accessed by the query, and how must data updates, if any, be synchronized?) • Mapping to determine the data location of local and remote fragments • I/O interface to read or write data from or to permanent local storage • Formatting to prepare the data for presentation to the end user or to an application program • Security to provide data privacy at both local and remote databases • Backup and recovery to ensure the availability and recoverability of the database in case of a failure • DB administration features for the database administrator • Concurrency control to manage simultaneous data access and to ensure data consistency across database fragments in the DDBMS • Transaction management to ensure that the data moves from one consistent state to another; this activity includes the synchronization of local and remote transactions as well as transactions across multiple distributed segments A fully distributed database management system must perform all of the functions of a centralized DBMS, as follows: 1. Receive the request of an application or end user. 2. Validate,analyze,anddecomposetherequest.Therequestmightincludemathematical and logical operations such as the following: Select all customers with a balance greater than $1,000. The request might require data from only a single table, or it might require access to several tables. 3. Map the request’s logical-to-physical data components. 4. Decompose the request into several disk I/O operations. 5. Search for, locate, read, and validate the data. 6. Ensure database consistency, security, and integrity. 7. Validate the data for the conditions, if any, specified by the request. 8. Present the selected data in the required format. In addition, a distributed DBMS must handle all necessary functions imposed by the distribution of data and processing, and it must perform those additional functions trans- parently to the end user. The DDBMS’s transparent data access features are illustrated in Figure 12.4. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 560 Part 4 Advanced Database Concepts FIGURE12.4 AFULLYDISTRIBUTEDDATABASEMANAGEMENTSYSTEM User Mary Site 1 Distributed processing Site 2 Single logical database User Tom Communications network The single logical database in Figure 12.4 consists of two database fragments, A1 and A2, located at Sites 1 and 2, respectively. Mary can query the database as if it were a local database; so can Tom. Both users “see” only one logical database and do not need to know the names of the fragments. In fact, the end users do not even need to know that the database is divided into fragments, nor do they need to know where the fragments are located. To better understand the different types of distributed database scenarios, first consider the components of the distributed database system. 12-5 DDBMS Components The DDBMS must include at least the following components: • Computer workstations or remote devices (sites or nodes) that form the network system. The distributed database system must be independent of the computer system hardware. • Network hardware and software components that reside in each workstation or device. The network components allow all sites to interact and exchange data. Because the components—computers, operating systems, network hardware, and so on—are likely to be supplied by different vendors, it is best to ensure that distributed data- base functions can be run on multiple platforms. • Communications media that carry the data from one node to another. The DDBMS must be communications media-independent; that is, it must be able to support several types of communications media. • The transaction processor (TP) is the software component found in each computer or device that requests data. The transaction processor receives and processes the application’s remote and local data requests. The TP is also known as the application processor (AP) or the transaction manager (TM). • The data processor (DP) is the software component residing on each computer or device that stores and retrieves data located at the site. The DP is also known as the data manager (DM). A data processor may even be a centralized DBMS. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. transaction processor (TP) In a DDBMS, the software component on each computer that requests data. The TP is responsible for the execution and coordination of all database requests issued by a local application that accesses data on any DP. Also called transaction manager (TM) or application processor (AP). application processor (AP) See transaction processor (TP). transaction manager (TM) See transaction processor (TP). data processor (DP) The resident software component that stores and retrieves data through a DDBMS. The DP is responsible for managing the local data in the computer and coordinating access to that data. Also known as data manager (DM). data manager (DM) See data processor (DP). Copyright 2017 Cengage Lea Editorial review has deemed that t t n n e e m m g g a a r r f 12 f D D a a t t a a b b a a s s e A e A Figure 12.5 illustrates the placement of the components and the interaction among them. The communication among TPs and DPs is made possible through a specific set of rules, or protocols, used by the DDBMS. The protocols determine how the distributed database system will: • Interface with the network to transport data and commands between DPs and TPs. • Synchronize all data received from DPs (TP side) and route retrieved data to the appropriate TPs (DP side). • Ensure common database functions in a distributed system. Such functions include data security, transaction management and concurrency control, data partitioning and synchronization, and data backup and recovery. DPs and TPs should be added to the system transparently without affecting its operation. A TP and a DP can reside on the same computer, allowing the end user to access both local and remote data transparently. In theory, a DP can be an independent centralized DBMS with proper interfaces to support remote access from other independent DBMSs in the network. 12-6 Levels of Data and Process Distribution Current database systems can be classified on the basis of how process distribution and data distribution are supported. For example, a DBMS may store data in a single site (using a centralized DB) or in multiple sites (using a distributed DB), and it may support data processing at one or more sites. Table 12.2 uses a simple matrix to classify data- base systems according to data and process distribution. These types of processes are discussed in the sections that follow. 12-6a Single-Site Processing, Single-Site Data In the single-site processing, single-site data (SPSD) scenario, all processing is done on a single host computer, and all data is stored on the host computer’s local disk system. single-site processing, single-site data (SPSD) A scenario in which all processing is done on a single host computer and all data is stored on the host computer’s local disk. Chapter 12 Distributed Database Management Systems 561 FIGURE12.5 DISTRIBUTEDDATABASESYSTEMCOMPONENTS José Peter Mary Dedicated data processor TP TP TP DP DP TP TP DP DP DP Dedicated data processor Amy Chantal Communications network Note: Each TP can access data on any DP, and each DP handles all requests for local data from any TP. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 562 Part 4 Advanced Database Concepts TABLE 12.2 DATABASE SYSTEMS: LEVELS OF DATA AND PROCESS DISTRIBUTION ADVANTAGES SINGLE-SITE DATA MULTIPLE-SITE DATA Processing cannot be done on the end user’s side of the system. Such a scenario is typical of most mainframe and midrange UNIX/Linux server DBMSs. The DBMS is on the host computer, which is accessed by terminals connected to it (see Figure 12.6). This scenario is also typical of the first generation of single-user microcomputer databases. Using Figure 12.6 as an example, you can see that the functions of the TP and DP are embedded within the DBMS on the host computer. The DBMS usually runs under a time-sharing, multitasking operating system, which allows several processes to run concurrently on a host computer accessing a single DP. All data storage and data pro- cessing are handled by a single host computer. 12-6b Multiple-Site Processing, Single-Site Data Under the multiple-site processing, single-site data (MPSD) scenario, multiple pro- cesses run on different computers that share a single data repository. Typically, the MPSD scenario requires a network file server running conventional applications that are accessed through a network. Many multiuser accounting applications running under a personal computer network fit such a description (see Figure 12.7). As you examine Figure 12.7, note that: • The TP on each workstation acts only as a redirector to route all network data requests to the file server. • The end user sees the file server as just another hard disk. Because only the data storage input/output (I/O) is handled by the file server’s computer, the MPSD offers limited capabilities for distributed processing. Single-site process Host DBMS Not applicable (Requires multiple processes) Multiple-site process File server Client/server DBMS (LAN DBMS) Fully distributed Client/server DDBMS FIGURE 12.6 SINGLE-SITE PROCESSING, SINGLE-SITE DATA (CENTRALIZED) DBMS Host computer Front-end processor Communication through DSL or T-1 line Dumb terminals Remote dumb terminal multiple-site processing, single- site data (MPSD) A scenario in which multiple processes run on different computers sharing a single data repository. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. T1 T2 T3 e s a b D a t a • The end user must make a direct reference to the file server to access remote data. All record- and file-locking activities are performed at the end-user location. • All data selection, search, and update functions take place at the workstation, thus requiring that entire files travel through the network for processing at the workstation. Such a requirement increases network traffic, slows response time, and increases communication costs. The inefficiency of the last condition can be illustrated easily. For example, suppose that the file server computer stores a CUSTOMER table containing 100,000 data rows, 50 of which have balances greater than $1,000. Suppose that Site A issues the following SQL query: client/server architecture A hardware and software system composed of clients, servers, and middleware. Features a user of resources (client) and a provider of resources (server). multiple-site processing, multiple- site data (MPMD) A scenario describing a fully distributed database management system with support for multiple data processors and transaction processors at multiple sites. homogeneous DDBMS A system that integrates only one type of centralized database management system over a network. heterogeneous DDBMS A system that integrates different types of centralized database management systems over a network. Chapter 12 Distributed Database Management Systems 563 Online Content For more information about client/server archi- tecture, see Appendix F, Client/Server Systems, FIGURE12.7 MULTIPLE-SITEPROCESSING,SINGLE-SITEDATA File Server Site A Site B Site C Communications network available at cengagebrain.com. www. SELECT FROM WHERE * CUSTOMER CUS_BALANCE > 1000;
All 100,000 CUSTOMER rows must travel through the network to be evaluated at Site A. A variation of the multiple-site processing, single-site data approach is known as client/server architecture. Client/server architecture is similar to that of the network file server except that all database processing is done at the server site, thus reducing net- work traffic. Although both the network file server and the client/server systems perform multiple-site processing, the client/server system’s processing is distributed. Note that the network file server approach requires the database to be located at a single site. In contrast, the client/server architecture is capable of supporting data at multiple sites.
12-6c Multiple-Site Processing, Multiple-Site Data
The multiple-site processing, multiple-site data (MPMD) scenario describes a fully distributed DBMS with support for multiple data processors and transaction proces- sors at multiple sites. Depending on the level of support for various types of databases, DDBMSs are classified as either homogeneous or heterogeneous.
Homogeneous DDBMSs integrate multiple instances of the same DBMS over a network—for example, multiple instances of Oracle 11g running on different platforms. In contrast, heterogeneous DDBMSs integrate different types of DBMSs over a network, but all support the same data model. For example, Table 12.3 lists several relational data- base systems that could be integrated within a DDBMS. A fully heterogeneous DDBMS will support different DBMSs, each one supporting a different data model, running under different computer systems.
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
DP
TP TP
TP

564 Part 4 Advanced Database Concepts
TABLE 12.3
DATABASE SYSTEMS: LEVELS OF DATA AND PROCESS DISTRIBUTION
PLATFORM DBMS OPERATING SYSTEM NETWORK COMMUNICATIONS PROTOCOL
Distributed database implementations are better understood as an abstraction layer on top of a DBMS. This abstraction layer provides additional functionality that enables support for distributed database features, including straightforward data links, repli- cation, advanced data fragmentation, synchronization, and integration. In fact, most database vendors provide for increasing levels of data fragmentation, replication, and integration. Therefore, the support for distributed databases can be better seen as a continuous spectrum that goes from homogeneous to fully heterogeneous distributed data management. Consequently, at any point on this spectrum, a DDBMS is subject to certain restrictions. For example:
• Remote access is provided on a read-only basis and does not support write privileges.
• Restrictions are placed on the number of remote tables that may be accessed in a single transaction.
• Restrictions are placed on the number of distinct databases that may be accessed.
• Restrictions are placed on the database model that may be accessed. Thus, access may
be provided to relational databases but not to network or hierarchical databases.
The preceding list of restrictions is by no means exhaustive. The DDBMS technology continues to change rapidly, and new features are added frequently. Managing data at multiple sites leads to a number of issues that must be addressed and understood. The next section examines several key features of distributed database management systems.
12-7 Distributed Database Transparency Features
A distributed database system should provide some desirable transparency features that make all the system’s complexities hidden to the end user. In other words, the end user should have the sense of working with a centralized DBMS. For this reason, the minimum desirable DDBMS transparency features are:
• Distribution transparency allows a distributed database to be treated as a single logical database. If a DDBMS exhibits distribution transparency, the user does not need to know:
– The data is partitioned—meaning the table’s rows and columns are split vertically or horizontally and stored among multiple sites.
– The data is geographically dispersed among multiple sites.
– The data is replicated among multiple sites.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
IBM 3090
DB2
MVS
APPC LU 6.2
IBM AS/400
SQL/400
OS/400
3270
RISC computer
Informix
UNIX
TCP/IP
Intel Xeon CPU
Oracle
Windows Server
TCP/IP
fully heterogeneous distributed database system (fully heterogeneous DDBMS)
A system that integrates different types of database management systems (hierarchical, network, and relational) over a network. It supports different database management systems that may even support different data models running under different computer systems. See also heterogeneous DDBMS and homogeneous DDBMS.
distribution transparency
A DDBMS feature that allows a distributed database to look like a single logical database to an end user.
Copyright 2017 Cengage Lea Editorial review has deemed that

• Transaction transparency allows a transaction to update data at more than one network site. Transaction transparency ensures that the transaction will be either entirely completed or aborted, thus maintaining database integrity.
• Failure transparency ensures that the system will continue to operate in the event of a node or network failure. Functions that were lost because of the failure will be picked up by another network node. This is a very important feature, particularly in organizations that depend on web presence as the backbone for maintaining trust in their business.
• Performance transparency allows the system to perform as if it were a centralized DBMS. The system will not suffer any performance degradation due to its use on a network or because of the network’s platform differences. Performance transparency also ensures that the system will find the most cost-effective path to access remote data. The system should be able to “scale out” in a transparent manner or increase performance capacity by adding more transaction or data-processing nodes, without affecting the overall performance of the system.
• Heterogeneity transparency allows the integration of several different local DBMSs (relational, network, and hierarchical) under a common, or global, schema. The DDBMS is responsible for translating the data requests from the global schema to the local DBMS schema.
The following sections discuss each of these transparency features in greater detail.
12-8 DistributionTransparency
Distribution transparency allows a physically dispersed database to be managed as though it were a centralized database. The level of transparency supported by the DDBMS varies from system to system. Three levels of distribution transparency are recognized:
• Fragmentation transparency is the highest level of distribution transparency. The end user or programmer does not need to know that a database is partitioned. Therefore, neither fragment names nor fragment locations are specified prior to data access.
• Location transparency exists when the end user or programmer must specify the database fragment names but does not need to specify where those fragments are located.
• Local mapping transparency exists when the end user or programmer must specify both the fragment names and their locations.
Transparency features are summarized in Table 12.4.
TABLE 12.4
SUMMARY OF TRANSPARENCY FEATURES
IF THE SQL STATEMENT REQUIRES:
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Chapter 12 Distributed Database Management Systems 565
transaction transparency
A DDBMS property
that ensures database transactions will maintain the distributed database’s integrity and consistency, and that a transaction will be completed only when all database sites involved complete their part of the transaction.
failure transparency
A feature that allows continuous operation of a DDBMS, even if a network node fails.
performance transparency
A DDBMS feature that allows a system to perform as though it were a centralized DBMS.
heterogeneity transparency
A feature that allows
a system to integrate several centralized DBMSs into one logical DDBMS.
fragmentation transparency
A DDBMS feature that allows a system to treat a distributed database as a single database even though it is divided into two or more fragments.
location transparency
A property of a DDBMS in which database access requires the user to know only the name of the database fragments. (Fragment locations need not be known.)
FRAGMENT NAME?
LOCATION NAME?
THEN THE DBMS SUPPORTS
LEVEL OF DISTRIBUTON TRANSPARENCY
Yes
Yes
Local mapping transparency
Low
Yes
No
Location transparency
Medium
No
No
Fragmentation transparency
High

566 Part 4 Note
Advanced Database Concepts
As you examine Table 12.4, notice that there is no reference to a situation in which the fragment name is “No” and the location name is “Yes.” The reason is simple: you cannot have a location name that fails to reference an existing fragment. If you don’t need to specify a fragment name, its location is clearly irrelevant.
To illustrate the use of various transparency levels, suppose you have an EMPLOYEE table that contains the attributes EMP_NAME, EMP_DOB, EMP_ADDRESS, EMP_ DEPARTMENT, and EMP_SALARY. The EMPLOYEE data is distributed over three different locations: New York, Atlanta, and Miami. The table is divided by location; that is, New York employee data is stored in fragment E1, Atlanta employee data is stored in fragment E2, and Miami employee data is stored in fragment E3 (see Figure 12.8).
Now suppose that the end user wants to list all employees born before January 1, 1960. To focus on the transparency issues, also suppose that the EMPLOYEE table is fragmented and each fragment is unique. The unique fragment condition indicates that each row is unique, regardless of the fragment in which it is located. Finally, assume that no portion of the database is replicated at any other site on the network.
Depending on the level of distribution transparency support, you may examine three query cases.
Case 1: The Database Supports Fragmentation Transparency
The query conforms to a nondistributed database query format; that is, it does not specify fragment names or locations. The query reads:
FIGURE12.8 FRAGMENTLOCATIONS
Fragment Location
Distributed DBMS
EMPLOYEE table
E1 E2 E3
New York Atlanta Miami
SELECT FROM WHERE
*
EMPLOYEE
EMP_DOB < '01-JAN-1979'; Case 2: The Database Supports Location Transparency Fragment names must be specified in the query, but the fragment’s location is not specified. The query reads: SELECT * FROM E1 rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. local mapping transparency A property of a DDBMS in which database access requires the user to know both the name and location of the fragments. unique fragment In a DDBMS, a condition in which each row is unique, regardless of which fragment it is located in. Copyright 2017 Cengage Lea Editorial review has deemed that WHERE UNION SELECT FROM WHERE UNION SELECT FROM WHERE EMP_DOB < '01-JAN-1979' * E2 EMP_DOB < '01-JAN-1979' * E3 EMP_DOB < '01-JAN-1979' Case 3: The Database Supports Local Mapping Transparency Both the fragment name and its location must be specified in the query. Using pseudo-SQL: SELECT FROM WHERE UNION SELECT FROM WHERE UNION SELECT FROM WHERE Note * El NODE NY EMP_DOB < '01-JAN-1979'; * E2 NODE ATL EMP_DOB < '01-JAN-1979'; * E3 NODE MIA EMP_DOB < '01-JAN-1979'; Chapter 12 Distributed Database Management Systems 567 As you examine the preceding query formats, you can see how distribution transparency affects the way end users and programmers interact with the database. Distribution transparency is supported by a distributed data dictionary (DDD) or a distributed data catalog (DDC). The DDC contains the description of the entire database as seen by the database administrator. The database description, known as the distributed global schema, is the common database schema used by local TPs to translate user requests into subqueries (remote requests) that will be processed by different DPs. The DDC is itself distributed, and it is replicated at the network nodes. Therefore, the DDC must maintain consistency through updating at all sites. Keep in mind that some of the current DDBMS implementations impose lim- itations on the level of transparency support. For instance, you might be able to distribute a database, but not a table, across multiple sites. Such a condition indicates that the DDBMS supports location transparency but not fragmentation transparency. distributed data dictionary (DDD) See distributed data catalog. distributed data catalog (DDC) A data dictionary that contains the description (fragment names and locations) of a distributed database. distributed global schema The database schema description of a distributed database as seen by the database administrator. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. NODE indicates the location of the database fragment. NODE is used for illustration purposes and is not part of the standard SQL syntax. 568 Part 4 Advanced Database Concepts 12-9 TransactionTransparency Transaction transparency is a DDBMS property that ensures database transactions will maintain the distributed database’s integrity and consistency. Remember that a DDBMS database transaction can update data stored in many different computers connected in a network. Transaction transparency ensures that the transaction will be completed only when all database sites involved in the transaction complete their part of the transaction. Distributed database systems require complex mechanisms to manage transactions and ensure the database’s consistency and integrity. To understand how the transactions are managed, you should know the basic concepts governing remote requests, remote transactions, distributed transactions, and distributed requests. 12-9a Distributed Requests and Distributed Transactions1 Whether or not a transaction is distributed, it is formed by one or more database requests. The basic difference between a nondistributed transaction and a distributed transaction is that the distributed transaction can update or request data from several different remote sites on a network. To better understand distributed transactions, begin by learning the difference between remote and distributed transactions, using the BEGIN WORK and COMMIT WORK transaction format. Assume the existence of location transparency to avoid having to specify the data location. A remote request, illustrated in Figure 12.9, lets a single SQL statement access the data that are to be processed by a single remote database processor. In other words, the SQL statement (or request) can reference data at only one remote site. Similarly, a remote transaction, composed of several requests, accesses data at a single remote site. A remote transaction is illustrated in Figure 12.10. As you examine Figure 12.10, note the following remote transaction features: • The transaction updates the PRODUCT and INVOICE tables (located at Site B). • The remote transaction is sent to the remote Site B and executed there. 1The details of distributed requests and transactions were originally described by David McGoveran and Colin White, “Clarifying client/server,” DBMS 3(12), November 1990, pp. 78–89. arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE12.9 AREMOTEREQUEST Site A SELECT * FROM CUSTOMER Network Site B Comment: The request is directed to the CUSTOMER table at Site B. WHERE CUS_STATE = ‘AL’; remote request A DDBMS feature that allows a single SQL statement to access data in a single remote DP. remote transaction A DDBMS feature that allows a transaction (formed by several requests) to access data in a single remote DP. Copyright 2017 Cengage Le Editorial review has deemed that TP R E M O DP T C U S Chapter 12 Distributed Database Management Systems 569 FIGURE12.10 AREMOTETRANSACTION Site B INSERT INTO INVOICE (CUS_NUM, INV_DATE, INV_TOTAL) VALUES ‘100’, ‘15-FEB-2016’, 120.00; COMMIT WORK; Site A BEGIN WORK; UPDATE PRODUCT Network SET PROD_QTY = PROD_QTY – 1 WHERE PROD_NUM = ‘231785’; • The transaction can reference only one remote DP. • Each SQL statement (or request) can reference only one (the same) remote DP at a time, and the entire transaction can reference and be executed at only one remote DP. A distributed transaction can reference several different local or remote DP sites. Although each single request can reference only one local or remote DP site, the trans- action as a whole can reference multiple DP sites because each request can reference a different site. The distributed transaction process is illustrated in Figure 12.11. FIGURE12.11 ADISTRIBUTEDTRANSACTION Site A TP Network Site B DP Site C DP BEGIN WORK; UPDATE PRODUCT SET PROD_QTY=PROD_QTY – 1 WHERE PROD_NUM = ‘231785’; INSERT INTO INVOICE (CUS_NUM, INV_DATE, INV_TOTAL) VALUES (‘100’, ‘15-FEB-2016’, 120.00); UPDATE CUSTOMER SET CUS_BALANCE = CUS_BALANCE + 120 WHERE CUS_NUM = ‘100’; COMMIT WORK; Note the following features in Figure 12.11: • The transaction references two remote sites, B and C. • The first two requests, UPDATE PRODUCT and INSERT INTO INVOICE, are processed by the DP at the remote Site C, and the last request (UPDATE CUSTOMER) is processed by the DP at the remote Site B. • Each request can access only one remote site at a time. distributed transaction A database transaction that accesses data in several remote data processors (DPs) in a distributed database. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. E E E C C I I O D O D C C T T U U R M O DP T C U S I I N N V V TP O O P P R R 570 Part 4 Advanced Database Concepts The third characteristic may create problems. For example, suppose the PRODUCT table is divided into two fragments, PRODl and PROD2, located at Sites B and C, respectively. Given that scenario, the preceding distributed transaction cannot be executed because the following request cannot access data from more than one remote site: SELECT FROM WHERE * PRODUCT PROD_NUM = '231785'; FIGURE12.12 ADISTRIBUTEDREQUEST Site A TP BEGIN WORK; SELECT CUS_NUM, INV_TOTAL FROM CUSTOMER, INVOICE WHERE CUS_NUM = ‘100’ AND Network Site B DP Site C DP INVOICE.CUS_NUM = CUSTOMER.CUS_NUM; COMMIT WORK; distributed request A database request that allows a single SQL statement to access data in several remote data processors (DPs) in a distributed database. Therefore, the DBMS must be able to support a distributed request. A distributed request lets a single SQL statement reference data located at several different local or remote DP sites. Because each request (SQL statement) can access data from more than one local or remote DP site, a transaction can access several sites. The ability to execute a distributed request provides fully distributed database processing because you can: • Partition a database table into several fragments. • Reference one or more of those fragments with only one request. In other words, there is fragmentation transparency. The location and partition of the data should be transparent to the end user. Figure 12.12 illustrates a distributed request. As you examine the figure, note that the transaction uses a single SELECT statement to reference two tables, CUSTOMER and INVOICE. The two tables are located at two different sites, B and C. The distributed request feature also allows a single request to reference a physically partitioned table. For example, suppose that a CUSTOMER table is divided into two fragments, C1 and C2, located at Sites B and C, respectively. Further suppose that the end user wants to obtain a list of all customers whose balances exceed $250. The request is illustrated in Figure 12.13. Full-fragmentation transparency support is provided only by a DDBMS that supports distributed requests. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. R E E M O C U S T C I O D I N V T C U P R O Understanding the different types of database requests in distributed database systems helps you address the transaction transparency issue more effectively. Transaction transparency ensures that distributed transactions are treated as centralized transactions, ensuring their serializability. (Review Chapter 10, Transaction Management and Concurrency Control, if necessary.) That is, the execution of concurrent transactions, whether they are distributed or not, will take the database from one consistent state to another. 12-9b Distributed Concurrency Control Concurrency control becomes especially important in distributed databases because multisite, multiple-process operations are more likely to create data inconsistencies and deadlocked transactions than single-site systems. For example, the TP component of a DDBMS must ensure that all parts of the transaction are completed at all sites before a final COMMIT is issued to record the transaction. Suppose that a transaction updates data at three DP sites. The first two DP sites com- plete the transaction and commit the data at each local DP; however, the third DP site cannot commit the transaction. Such a scenario would yield an inconsistent database, with its inevitable integrity problems, because committed data cannot be uncommitted! This problem is illustrated in Figure 12.14. The solution to this problem is a two-phase commit protocol, which you will explore next. 12-9c Two-Phase Commit Protocol Centralized databases require only one DP. All database operations take place at only one site, and the consequences of database operations are immediately known to the DBMS. In contrast, distributed databases make it possible for a transaction to access data at several sites. A final COMMIT must not be issued until all sites have committed their parts of the transaction. The two-phase commit protocol (2PC) guarantees that if a portion of a transaction operation cannot be committed, all changes made at the other sites participating in the transaction will be undone to maintain a consistent database state. two-phase commit protocol (2PC) In a DDBMS, an algorithm used to ensure atomicity of transactions and database consistency as well as integrity in distributed transactions. Chapter 12 Distributed Database Management Systems 571 FIGURE12.13 ANOTHERDISTRIBUTEDREQUEST Site A TP Network Site B DP Site C DP SELECT * FROM CUSTOMER C1 C2 WHERE CUS_BALANCE > 250;
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FIGURE 12.14 THE EFFECT OF A PREMATURE COMMIT
Data is committed
Site A
Site B
Site C
DP
LOCK (X) WRITE (X) COMMIT
DP
LOCK (Y) WRITE (Y) COMMIT
DP
LOCK (Z) …
… ROLLBACK
Can’t roll back Sites A and B
Rollback at Site C
Each DP maintains its own transaction log. The two-phase commit protocol requires that the transaction log entry for each DP be written before the database fragment is actually updated (see Chapter 10). Therefore, the two-phase commit protocol requires a DO-UNDO-REDO protocol and a write-ahead protocol.
The DO-UNDO-REDO protocol is used by the DP to roll transactions back and forward with the help of the system’s transaction log entries. The DO-UNDO-REDO protocol defines three types of operations:
• DO performs the operation and records the “before” and “after” values in the transaction log.
• UNDO reverses an operation, using the log entries written by the DO portion of the sequence.
• REDO redoes an operation, using the log entries written by the DO portion of the sequence.
To ensure that the DO, UNDO, and REDO operations can survive a system crash while they are being executed, a write-ahead protocol is used. The write-ahead protocol forces the log entry to be written to permanent storage before the actual operation takes place.
The two-phase commit protocol defines the operations between two types of nodes: the coordinator and one or more subordinates, or cohorts. The participating nodes agree on a coordinator. Generally, the coordinator role is assigned to the node that initiates the transaction. However, different systems implement various, more sophisti- cated election methods. The protocol is implemented in two phases, as illustrated in the following sections.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
DO-UNDO-REDO protocol
A protocol used by a data processor (DP) to roll back or roll forward transactions with the help of a system’s transaction log entries.
write-ahead protocol
A protocol that ensures transaction logs are written to permanent storage before any database data is actually updated.
coordinator
The transaction processor (TP) node
that coordinates the execution of a two-phase COMMIT in a DDBMS.
subordinate
In a DDBMS, a data processor (DP) node that participates in a distributed transaction using the two-phase COMMIT protocol.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 12 Distributed Database Management Systems 573 The coordinator sends a PREPARE TO COMMIT message to all subordinates.
1. Thesubordinatesreceivethemessage,writethetransactionlogusingthewrite-ahead protocol, and send an acknowledgment message (YES/PREPARED TO COMMIT or NO/NOT PREPARED) to the coordinator.
2. The coordinator makes sure that all nodes are ready to commit, or it aborts the action.
If all nodes are PREPARED TO COMMIT, the transaction goes to Phase 2. If one
or more nodes reply NO or NOT PREPARED, the coordinator broadcasts an ABORT message to all subordinates.
Phase 2: The Final COMMIT
1. ThecoordinatorbroadcastsaCOMMITmessagetoallsubordinatesandwaitsforthe replies.
2. Each subordinate receives the COMMIT message and then updates the database using the DO protocol.
3. The subordinates reply with a COMMITTED or NOT COMMITTED message to the coordinator.
If one or more subordinates do not commit, the coordinator sends an ABORT message, thereby forcing them to UNDO all changes.
The objective of the two-phase commit is to ensure that each node commits its part of the transaction; otherwise, the transaction is aborted. If one of the nodes fails to com- mit, the information necessary to recover the database is in the transaction log, and the database can be recovered with the DO-UNDO-REDO protocol. (Remember that the log information was updated using the write-ahead protocol.)
12-10 Performance and Failure Transparency
One of the most important functions of a database is its ability to make data available. Web-based distributed data systems demand high availability, which means not only that data is accessible but that requests are processed in a timely manner. For example, the average Google search has a subsecond response time. When was the last time you entered a Google query and waited more than a couple of seconds for the results?
Performance transparency allows a DDBMS to perform as if it were a centralized database. In other words, no performance degradation should be incurred due to data distribution. Failure transparency ensures that the system will continue to operate in the case of a node or network failure. Although these are two separate issues, they are interrelated in that a failing node or congested network path could cause performance problems. Therefore, both issues are addressed in this section.
Note
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Phase 1: Preparation
Chapter 11, Database Performance Tuning and Query Optimization, provides additional details about query optimization.

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The objective of query optimization is to minimize the total cost associated with the execution of a request. The costs associated with a request are a function of the following:
• Access time (I/O) cost involved in accessing the data from multiple remote sites
• Communication cost associated with data transmission among nodes in distributed
database systems
• CPU time cost associated with the processing overhead of managing distributed transactions
Although costs are often classified either as communication or processing costs, it is difficult to separate the two. Not all query optimization algorithms use the same param- eters, and not all algorithms assign the same weight to each parameter. For example, some algorithms minimize total time, others minimize the communication time, and still others do not factor in the CPU time, considering its cost insignificant relative to other costs.
As you learned in Chapter 11, a centralized database evaluates every data request to find the most-efficient way to access the data. This is a reasonable requirement, con- sidering that all data is locally stored and all active transactions working on the data are known to the central DBMS. In contrast, in a DDBMS, transactions are distributed among multiple nodes; therefore, determining what data is being used becomes more complex. Hence, resolving data requests in a distributed data environment must take the following points into consideration:
• Data distribution. In a DDBMS, query translation is more complicated because the DDBMS must decide which fragment to access. (Distribution transparency was explained earlier in this chapter.) In this case, a TP executing a query must choose what fragments to access, create multiple data requests to the chosen remote DPs, combine the DP responses, and present the data to the application.
• Data replication. In addition, the data may also be replicated at several different sites. The data replication makes the access problem even more complex because the data- base must ensure that all copies of the data are consistent. Therefore, an important characteristic of query optimization in distributed database systems is that it must provide replica transparency. Replica transparency refers to the DDBMS’s ability to hide multiple copies of data from the user. This ability is particularly important with data update operations. If a read-only request is being processed, it can be satisfied by accessing any available remote DP. However, processing a write request also involves “synchronizing” all existing fragments to maintain data consistency. The two-phase commit protocol you learned about in Section 12-9c ensures that the transaction will complete successfully. However, if data is replicated at other sites, the DDBMSs must also ensure the consistency of all the fragments—that is, all fragments should be mutually consistent. To accomplish this, a DP captures all changes and pushes them to each remote replica. This introduces delays in the system and basically means that not all data changes are immediately seen by all replicas. (The implications of this issue are explained in Section 12-12, The CAP Theorem.)
• Network and node availability. The response time associated with remote sites can- not be easily predetermined because some nodes finish their part of the query in less time than others and network path performance varies because of bandwidth and traffic loads. Hence, to achieve performance transparency, the DDBMS should consider issues such as network latency, the delay imposed by the amount of time required for a data packet to make a round trip from point A to point B, or network partitioning, the delay imposed when nodes become suddenly unavailable due to a network failure.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
replica transparency
The DDBMS’s ability to hide the existence of multiple copies of data from the user.
network latency
The delay imposed by the amount of time required for a data packet to make a round trip from point A to point B.
network partitioning
The delay that occurs when nodes become suddenly unavailable due to a network failure. In distributed databases, the system must account for the possibility of this condition.
Copyright 2017 Cengage Lea Editorial review has deemed that

Carefully planning how to partition a database and where to locate the database fragments can help ensure the performance and consistency of a distributed database. The following section discusses issues for distributed database design.
12-11 Distributed Database Design
Whether the database is centralized or distributed, the design principles and concepts described in Chapters 3, 4, and 6 are still applicable. However, the design of a distributed database introduces three new issues:
• How to partition the database into fragments
• Which fragments to replicate
• Where to locate those fragments and replicas
Data fragmentation and data replication deal with the first two issues, and data allocation deals with the third issue. Ideally, data in a distributed database should be evenly distributed to maximize performance, increase availability (reduce bottlenecks), and provide location awareness, which is an ever-increasing requirement for mobile applications.
12-11a Data Fragmentation
Data fragmentation allows you to break a single object into two or more segments, or fragments. The object might be a user’s database, a system database, or a table. Each fragment can be stored at any site over a computer network. Information about data fragmentation is stored in the distributed data catalog (DDC), from which it is accessed by the TP to process user requests.
Data fragmentation strategies, as discussed here, are based at the table level and consist of dividing a table into logical fragments. You will explore three types of data fragmentation strategies: horizontal, vertical, and mixed. (Keep in mind that a frag- mented table can always be re-created from its fragmented parts by a combination of unions and joins.)
• Horizontal fragmentation refers to the division of a relation into subsets (fragments) of tuples (rows). Each fragment is stored at a different node, and each fragment has unique rows. However, the unique rows all have the same attributes (columns). In short, each fragment represents the equivalent of a SELECT statement, with the WHERE clause on a single attribute.
• Vertical fragmentation refers to the division of a relation into attribute (column) subsets. Each subset (fragment) is stored at a different node, and each fragment has unique columns—with the exception of the key column, which is common to all fragments. This is the equivalent of the PROJECT statement in SQL.
• Mixed fragmentation refers to a combination of horizontal and vertical strategies. In other words, a table may be divided into several horizontal subsets (rows), each one having a subset of the attributes (columns).
To illustrate the fragmentation strategies, use the CUSTOMER table for the XYZ Company, as depicted in Figure 12.15. The table contains the attributes CUS_NUM, CUS_NAME, CUS_ADDRESS, CUS_STATE, CUS_LIMIT, CUS_BAL, CUS_RATING, and CUS_DUE.
data fragmentation
A characteristic of a DDBMS that allows
a single object to be broken into two or more segments or fragments. The object might be a user’s database, a system database, or a table. Each fragment can be stored at any site on a computer network.
horizontal fragmentation
The distributed database design process that breaks a table into subsets of unique rows.
vertical fragmentation
In distributed database design, the process that breaks a table into a subset of columns from the original table. Fragments must share a common primary key.
mixed fragmentation
A combination of horizontal and vertical strategies for data fragmentation, in which a table may be divided into several rows and each row has a subset of the attributes (columns).
Chapter 12 Distributed Database Management Systems 575
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FIGURE12.15 ASAMPLECUSTOMERTABLE
Table name: CUSTOMER Database name: Ch12_Text
TABLE 12.5
Horizontal Fragmentation In this case, a table is divided into multiple subsets of rows. There are various ways to partition a table horizontally:
• Round-robin partitioning. Rows are assigned to a given fragment in a round-robin fashion (F1, F2, F3, … , Fn) to ensure an even distribution of rows among all fragments. However, this is not a good strategy if you require “location awareness”—the ability to determine which DP node will process a query based on the geospatial location of the requester. For example, you would want all queries from Florida customers to be resolved from a fragment that stores only Florida customers. Of course, you also would like this fragment to be located in a node close to Florida.
• Range partitioning based on a partition key. A partition key is one or more attributes in a table that determine the fragment in which a row will be stored. For example, if you want to provide location awareness, a good partition key would be the customer state field. This is the most common and useful data partitioning strategy.
Take a closer look at how to use a partition key to partition a table. Suppose that the XYZ Company’s corporate management requires information about its customers in all three states, but company locations in each state (TN, FL, and GA) require data regarding local customers only. Based on such requirements, you decide to distribute the data by state. Therefore, you define the horizontal fragments to conform to the structure shown in Table 12.5.
HORIZONTAL FRAGMENTATION OF THE CUSTOMER TABLE BY STATE
FRAGMENT NAME
LOCATION CONDITION NODE NAME CUSTOMER NUMBER OF ROWS NUMBERS
The partition key will be the CUS_STATE field. Each horizontal fragment may have a different number of rows, but each fragment must have the same attributes. The resulting fragments yield the three tables depicted in Figure 12.16.
Vertical Fragmentation You may also divide the CUSTOMER relation into vertical fragments that are composed of a collection of attributes. For example, suppose that the company is divided into two departments: the service department and the collections department. Each department is located in a separate building, and each has an interest in only a few of the CUSTOMER table’s attributes. In this case, the fragments are defined as shown in Table 12.6.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
CUST_H1
Tennessee
CUS_STATE = ‘TN’
NAS
10, 12
2
CUST_H2
Georgia
CUS_STATE = ‘GA’
ATL
15
1
CUST_H3
Florida
CUS_STATE = ‘FL’
TAM
11, 13, 14
3
partition key
In partitioned databases, one or more attributes in a table that determine the fragment in which a row will be stored.
Copyright 2017 Cengage Le Editorial review has deemed that

Chapter 12 Distributed Database Management Systems 577
FIGURE 12.16 TABLE FRAGMENTS IN THREE LOCATIONS
Table name: CUST_H1
Table name: CUST_H2 Table name: CUST_H3
Location: Tennessee
Location: Georgia Location: Florida
Node: NAS
Node: ATL Node: TAM
Database name: Ch12_Text
TABLE 12.6
VERTICAL FRAGMENTATION OF THE CUSTOMER TABLE
FRAGMENT NAME LOCATION NODE NAME ATTRIBUTE NAMES
Each vertical fragment must have the same number of rows, but the inclusion of the different attributes depends on the key column. The vertical fragmentation results are displayed in Figure 12.17. Note that the key attribute (CUS_NUM) is common to both fragments CUST_V1 and CUST_V2.
Mixed Fragmentation The XYZ Company’s structure requires that the CUSTOMER data be fragmented horizontally to accommodate the various company locations; within the locations, the data must be fragmented vertically to accommodate the two departments (service and collection). In short, the CUSTOMER table requires mixed fragmentation.
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CUST_V1
Service Bldg
SVC
CUS_NUM, CUS_NAME, CUS_ADDRESS, CUS_STATE
CUST_V2
Collection Bldg.
ARC
CUS_NUM, CUS_LIMIT, CUS_BAL, CUS_RATING, CUS_DUE
FIGURE 12.17 VERTICALLY FRAGMENTED TABLE CONTENTS
Database name: Ch12_Text Table name: CUST_V1 Location: Service Building Node: SVC
Table name: CUST_V2 Location: Collection Building Node: ARC

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Mixed fragmentation requires a two-step procedure. First, horizontal fragmenta- tion is introduced for each site based on the location within a state (CUS_STATE). The horizontal fragmentation yields the subsets of customer tuples (horizontal fragments) that are located at each site. Because the departments are located in different buildings, vertical fragmentation is used within each horizontal fragment to divide the attributes, thus meeting each department’s information needs at each subsite. Mixed fragmentation yields the results displayed in Table 12.7.
TABLE 12.7
MIXED FRAGMENTATION OF THE CUSTOMER TABLE
FRAGMENT NAME
LOCATION HORIZONTAL NODE RESULTING VERTICAL CRITERIA ATTRIBUTES CRITERIA NAME ROWS AT SITE AT EACH FRAGMENT
CUST_M1
TN-Service
CUS_STATE = TN
NAS-S
10, 12
CUS_NUM, CUS_NAME CUS_ADDRESS, CUS_STATE
CUST_M2
TN-Collection
CUS_STATE = TN
NAS-C
10, 12
CUS_NUM, CUS_LIMIT, CUS_BAL, CUS_RATING, CUS_DUE
CUST_M3
GA-Service
CUS_STATE = GA
ATL-S
15
CUS_NUM, CUS_NAME CUS_ADDRESS, CUS_STATE
CUST_M4
GA-Collection
CUS_STATE = GA
ATL-C
15
CUS_NUM, CUS_LIMIT, CUS_BAL, CUS_RATING, CUS_DUE
CUST_M5
FL-Service
CUS_STATE = FL
TAM-S
11, 13, 14
CUS_NUM, CUS_NAME CUS_ADDRESS, CUS_STATE
CUST_M6
FL-Collection
CUS_STATE = FL
TAM-C
11, 13, 14
CUS_NUM, CUS_LIMIT, CUS_BAL, CUS_RATING, CUS_DUE
Each fragment displayed in Table 12.7 contains customer data by state and, within each state, by department location to fit each department’s data requirements. The tables corresponding to the fragments listed in Table 12.7 are shown in Figure 12.18.
12-11b Data Replication
Data replication refers to the storage of data copies at multiple sites served by a computer network. Fragment copies can be stored at several sites to serve specific information requirements. Because the existence of fragment copies can enhance data availability and response time, data copies can help to reduce communication and total query costs.
Suppose database A is divided into two fragments, A1 and A2. Within a replicated distributed database, the scenario depicted in Figure 12.19 is possible: fragment A1 is stored at Sites S1 and S2, while fragment A2 is stored at Sites S2 and S3.
Replicated data is subject to the mutual consistency rule, which requires that all copies of data fragments be identical. Therefore, to maintain data consistency among the replicas, the DDBMS must ensure that a database update is performed at all sites where replicas exist.
There are basically two styles of replication:
• Push replication. After a data update, the originating DP node sends the changes to the replica nodes to ensure that data is immediately updated. This type of replication focuses on maintaining data consistency. However, it decreases data availability due to the latency involved in ensuring data consistency at all nodes.
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data replication
The storage of duplicated database fragments at multiple sites on a DDBMS. Duplication of the fragments is transparent to the end user. Data replication provides fault tolerance
and performance enhancements.
mutual consistency rule
A data replication rule that requires all copies of data fragments to be identical.
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Chapter 12 Distributed Database Management Systems 579
FIGURE 12.18 TABLE CONTENTS AFTER THE MIXED FRAGMENTATION PROCESS
Table name: CUST_M1
Table name: CUST_M2
Table name: CUST_M3
Table name: CUST_M4
Table name: CUST_M5
Table name: CUST_M6
Location: TN-Service
Location: TN-Collection
Location: GA-Service
Location: GA-Collection
Location: FL-Service
Location: FL-Collection
Database name: Ch12_Text Node: NAS-S
Node: NAS-C
Node: ATL-S
Node: ATL-C
Node: TAM-S
Node: TAM-C
FIGURE12.19 DATAREPLICATION
Site S1 Site S2 Site S3
• Pull replication. After a data update, the originating DP node sends “messages” to the replica nodes to notify them of the update. The replica nodes decide when to apply the updates to their local fragment. In this type of replication, data updates propagate more slowly to the replicas. The focus is on maintaining data availability. However, this style of replication allows for temporary data inconsistencies.
Although replication has some benefits, such as improved data availability, better load distribution, improved data failure tolerance, and reduced query costs, it also imposes additional DDBMS processing overhead because each data copy must be maintained by the system. Furthermore, because the data is replicated at another site, there are associ- ated storage costs and increased transaction times (as data must be updated at several
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DP
DP
DP
1
1
2
2
A
A
A
A

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fully replicated database
In a DDBMS, the distributed database that stores multiple copies of each database fragment at multiple sites.
partially replicated database
A distributed database in which copies of only some database fragments are stored at multiple sites.
unreplicated database
A distributed database in which each database fragment is stored at a single site.
data allocation
In a distributed DBMS, the process of deciding where to locate data fragments.
centralized data allocation
A data allocation strategy in which the entire database is stored at one site. Also known as a centralized database.
partitioned data allocation
A data allocation strategy of dividing a database into two or more fragments that are stored at two or more sites.
replicated data allocation
A data allocation strategy in which copies of one or more database fragments are stored at several sites.
sites concurrently to comply with the mutual consistency rule). To illustrate the replica overhead imposed on a DDBMS, consider the processes that the DDBMS must perform to use the database:
• If the database is fragmented, the DDBMS must decompose a query into subqueries to access the appropriate fragments.
• If the database is replicated, the DDBMS must decide which copy to access. A READ operation selects the nearest copy to satisfy the transaction. A WRITE operation requires that all copies be selected and updated to satisfy the mutual consistency rule.
• The TP sends a data request to each selected DP for execution.
• The DP receives and executes each request and sends the data back to the TP. • The TP assembles the DP responses.
The problem becomes more complex when you consider additional factors such as network topology and communication throughputs.
Three replication scenarios exist: a database can be fully replicated, partially replicated, or unreplicated.
• A fully replicated database stores multiple copies of each database fragment at multiple sites. In this case, all database fragments are replicated. A fully replicated database can be impractical due to the amount of overhead it imposes on the system.
• A partially replicated database stores multiple copies of some database fragments at multiple sites. Most DDBMSs are able to handle the partially replicated database well.
• An unreplicated database stores each database fragment at a single site. Therefore, there are no duplicate database fragments.
Several factors influence the decision to use data replication:
• Database size. The amount of data replicated will have an impact on the storage require- ments and the data transmission costs. Replicating large amounts of data requires a window of time and higher network bandwidth that could affect other applications.
• Usage frequency. The frequency of data usage determines how frequently the data needs to be updated. Frequently used data should be updated more often, for example, than large data sets that are used only every quarter.
• Costs. Costs include those for performance, software overhead, and management associated with synchronizing transactions and their components versus fault- tolerance benefits that are associated with replicated data.
When the usage frequency of remotely located data is high and the database is large, data replication can reduce the cost of data requests. Data replication information is stored in the DDC, whose contents are used by the TP to decide which copy of a database fragment to access. The data replication makes it possible to restore lost data.
12-11c Data Allocation
Data allocation describes the process of deciding where to locate data. Data allocation
strategies are as follows:
• With centralized data allocation, the entire database is stored at one site.
• With partitioned data allocation, the database is divided into two or more disjointed parts (fragments) and stored at two or more sites.
• With replicated data allocation, copies of one or more database fragments are stored at several sites.
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Data distribution over a computer network is achieved through data partitioning, through data replication, or through a combination of both. Data allocation is closely related to the way a database is divided or fragmented. Most data allocation studies focus on one issue: which data to locate where.
Data allocation algorithms consider a variety of factors, including:
• Performance and data availability goals
• Size, number of rows, and number of relations that an entity maintains with other entities
• Types of transactions to be applied to the database and the attributes accessed by each of those transactions
• Disconnected operation for mobile users
In some cases, the design might consider the use of loosely disconnected fragments for mobile users, particularly for read-only data that does not require frequent updates and for which the replica update windows may be longer. (A replica update window is the amount of time available to perform a data-processing task that cannot be executed concurrently with other tasks.)
Most algorithms include information such as network topology, network bandwidth and throughput, data size, and location. No optimal or universally accepted algorithm exists yet, and each database vendor implements its own version to showcase the strengths of its respective products.
12-12 The CAP Theorem
In a 2000 symposium on distributed computing, Dr. Eric Brewer stated in his presen- tation that “in any highly distributed data system there are three commonly desirable properties: consistency, availability, and partition tolerance. However, it is impossible for a system to provide all three properties at the same time.”2 The initials CAP stand for the three desirable properties. Consider these three properties in more detail:
• Consistency. In a distributed database, consistency takes a bigger role. All nodes should see the same data at the same time, which means that the replicas should be immedi- ately updated. However, this involves dealing with latency and network partitioning delays, as you learned in Section 12-10.
• Availability. Simply speaking, a request is always fulfilled by the system. No received request is ever lost. If you are buying tickets online, you do not want the system to stop in the middle of the operation. This is a paramount requirement of all web-centric organizations.
• Partition tolerance. The system continues to operate even in the event of a node failure. This is the equivalent of failure transparency in distributed databases (see Section 12-7). The system will fail only if all nodes fail.
Do not mistake transaction management consistency (which you learned in Chapter 10) with CAP consistency. Transaction management consistency refers to the result when executing a transaction yields a database that complies with all integrity con- straints. Consistency in CAP is based on the assumption that all transaction operations
2 Eric A. Brewer, “Towards robust distributed systems,” University of California at Berkeley and Inktomi Corporation, presentation at the Principles of Distributed Computing, ACM Symposium, July 2000. This theorem was later proven by Seth Gilbert and Nancy Lynch of MIT in their paper, “Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services,” ACM SIGACT News, 33(2), 2002, pp. 51–59.
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take place at the same time in all nodes, as if they were executing in a single-node database. (“All nodes see the same data at the same time.”)
Although the CAP theorem focuses on highly distributed web-based systems, its implications are widespread for all distributed systems, including databases.
In Chapter 10, you learned that there are four database transaction properties: atomicity, consistency, isolation, and durability. The ACID properties ensure that all successful transactions result in a consistent database state—one in which all data operations always return the same results. For centralized and small distributed databases, latency is not an issue. As the business grows and the need for availability increases, database latency becomes a bigger problem. It is more difficult for a highly distributed database to ensure ACID transactions without paying a high price in network latency or data contention (delays imposed by concurrent data access).
For example, imagine that you are using Amazon.com to buy tickets for a Manches- ter United–Barcelona soccer game in Washington, D.C. You may spend a few minutes browsing through the available tickets and checking the stadium website to see which seats have the best view. At the same time, other users from all over the world may be doing exactly the same thing. By the time you click the checkout button, the tickets you selected may already have been purchased by someone else! In this case, you will start again and select other tickets until you get the ones you want. The website is designed to work this way because Amazon prefers the small probability of having a few customers restart their transactions to having to lock the database to ensure consistency and leaving thousands of customers waiting for their webpages to refresh. If you have noticed the small countdown clock when using Ticketmaster to buy concert tickets, you have seen the same principle at work.
As this example shows, when dealing with highly distributed systems, some companies tend to forfeit the consistency and isolation components of the ACID proper- ties to achieve higher availability. This trade-off between consistency and availability has generated a new type of distributed data systems in which data is basically available, soft state, eventually consistent (BASE). BASE refers to a data consistency model in which data changes are not immediate but propagate slowly through the system until all replicas are eventually consistent. For example, NoSQL databases provide a highly distributed database with eventual consistency (see Chapter 2, Data Models). In practice, the emergence of NoSQL distributed databases now provides a spectrum of consistency that ranges from the highly consistent (ACID) to the eventually consistent (BASE), as shown in Table 12.8.
basically available, soft state, eventually consistent (BASE)
A data consistency model in which
data changes are
not immediate but propagate slowly through the system until all replicas are eventually consistent.
TABLE 12.8
DISTRIBUTED DATABASE SPECTRUM
DBMS TYPE CONSISTENCY AVAILABILITY PARTITION TRANSACTION TRADE-OFF TOLERANCE MODEL
Centralized DBMS
High
High
N/A
ACID
No distributed data processing
Relational DBMS
High
Relaxed
High
ACID (2PC)
Sacrifices availability to ensure consistency and isolation.
NoSQL DDBMS
Relaxed
High
High
BASE
Sacrifices consistency to ensure availability
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Chapter 12 Distributed Database Management Systems 583
12-13 C. J. Date’s 12 Commandments for Distributed Databases
The notion of distributed databases has been around for over 20 years. With the rise of relational databases, most vendors implemented their own versions of distributed databases, generally highlighting their respective product’s strengths. To make compar- isons easier, C. J. Date formulated 12 “commandments” or basic principles of distrib- uted databases.3 Although no current DDBMS conforms to all of them, they constitute a useful target. The 12 rules are shown in Table 12.9.
TABLE 12.9
C. J. DATE’S 12 COMMANDMENTS FOR DISTRIBUTED DATABASES
RULE NUMBER RULE NAME RULE EXPLANATION
1
Local-site independence
Each local site can act as an independent, autonomous, centralized DBMS. Each site is responsible for security, concurrency control, backup, and recovery.
2
Central-site independence
No site in the network relies on a central site or any other site. All sites have the same capabilities.
3
Failure independence
The system is not affected by node failures. The system is in continuous operation even in the case of a node failure or an expansion of the network.
4
Location transparency
The user does not need to know the location of data to retrieve that data.
5
Fragmentation transparency
Data fragmentation is transparent to the user, who sees only one logical database. The user does not need to know the name of the database fragments to retrieve them.
6
Replication transparency
The user sees only one logical database. The DDBMS transparently selects the database fragment to access. To the user, the DDBMS manages all fragments transparently.
7
Distributed query processing
A distributed query may be executed at several different DP sites. Query optimization is performed transparently by the DDBMS.
8
Distributed transac- tion processing
A transaction may update data at several different sites, and the transaction is executed transparently.
9
Hardware independence
The system must run on any hardware platform.
10
Operating system independence
The system must run on any operating system platform.
11
Network independence
The system must run on any network platform.
12
Database independence
The system must support any vendor’s database product.
3 C. J. Date, “Twelve rules for a distributed database,” Computerworld 2(23), June 8, 1987, pp. 77–81.
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584 Part 4 Advanced Database Concepts
Summary
• A distributed database stores logically related data in two or more physically indepen- dent sites connected via a computer network. The database is divided into fragments, which can be a horizontal set of rows or a vertical set of attributes. Each fragment can be allocated to a different network node.
• Distributed processing is the division of logical database processing among two or more network nodes. Distributed databases require distributed processing. A distrib- uted database management system (DDBMS) governs the processing and storage of logically related data through interconnected computer systems.
• The main components of a DDBMS are the transaction processor (TP) and the data processor (DP). The transaction processor component is the resident software on each computer node that requests data. The data processor component is the resident software on each computer that stores and retrieves data.
• Current database systems can be classified by the extent to which they support pro- cessing and data distribution. Three major categories are used to classify distributed database systems: single-site processing, single-site data (SPSD); multiple-site process- ing, single-site data (MPSD); and multiple-site processing, multiple-site data (MPMD).
• A homogeneous distributed database system integrates only one particular type of DBMS over a computer network. A heterogeneous distributed database system inte- grates several different types of DBMSs over a computer network.
• DDBMS characteristics are best described as a set of transparencies: distribution, transaction, performance, failure, and heterogeneity. All transparencies share the common objective of making the distributed database behave as though it were a cen- tralized database system; that is, the end user sees the data as part of a single, logical centralized database and is unaware of the system’s complexities.
• A transaction is formed by one or more database requests. An undistributed transac- tion updates or requests data from a single site. A distributed transaction can update or request data from multiple sites.
• Distributed concurrency control is required in a network of distributed databases. A two- phase COMMIT protocol is used to ensure that all parts of a transaction are completed.
• A distributed DBMS evaluates every data request to find the optimum access path in a distributed database. The DDBMS must optimize the query to reduce associated access costs, communication costs, and CPU costs.
• The design of a distributed database must consider the fragmentation and replication of data. The designer must also decide how to allocate each fragment or replica to obtain better overall response time and to ensure data availability to the end user. Ideally, a distributed database should evenly distribute data to maximize performance, availability, and location awareness.
• A database can be replicated over several different sites on a computer network. The replication of the database fragments has the objective of improving data availability, thus decreasing access time. A database can be partially, fully, or not replicated. Data allocation strategies are designed to determine the location of the database fragments or replicas.
• The CAP theorem states that a highly distributed data system has some desirable properties of consistency, availability, and partition tolerance. However, a system can only provide two of these properties at a time.
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Chapter 12 Distributed Database Management Systems 585
1. Describe the evolution from centralized DBMSs to distributed DBMSs.
2. List and discuss some of the factors that influenced the evolution of the DDBMS.
3. What are the advantages of the DDBMS?
4. What are the disadvantages of the DDBMS?
5. Explain the difference between a distributed database and distributed processing.
6. What is a fully distributed database management system?
7. What are the components of a DDBMS?
8. List and explain the transparency features of a DDBMS.
9. Define and explain the different types of distribution transparency.
10. Describe the different types of database requests and transactions.
11. Explaintheneedforthetwo-phasecommitprotocol.Thendescribethetwophases.
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Key Terms
application processor (AP)
basically available, soft state, eventually consistent (BASE)
centralized data allocation client/server architecture coordinator
data allocation
data fragmentation
data manager (DM)
data processor (DP)
data replication
database fragments
distributed database
distributed database man- agement system (DDBMS)
distributed data catalog (DDC)
distributed data dictionary (DDD)
distributed global schema distributed processing distributed request
distributed transaction distribution transparency DO-UNDO-REDO protocol failure transparency fragmentation transparency fully heterogeneous DDBMS fully replicated database heterogeneity transparency heterogeneous DDBMS homogeneous DDBMS horizontal fragmentation local mapping transparency location transparency mixed fragmentation
multiple-site processing, multiple-site data (MPMD)
multiple-site processing, single-site data (MPSD)
mutual consistency rule network latency network partitioning
partially replicated database
partitioned data allocation
partition key
performance transparency
remote request
remote transaction
replica transparency
replicated data allocation
single-site processing, single-site data (SPSD)
subordinates
transaction manager (TM)
transaction processor (TP)
transaction transparency
two-phase commit protocol (2PC)
unique fragment unreplicated database vertical fragmentation write-ahead protocol
Online Content
Flashcards and crossword puzzle for key term practice are available at www. cengagebrain.com.
Review Questions

586 Part 4
Advanced Database Concepts
12. What is the objective of query optimization functions?
13. To which transparency feature are the query optimization functions related?
14. Whatissuesshouldbeconsideredwhenresolvingdatarequestsinadistributeddata environment?
15. Describe the three data fragmentation strategies. Give some examples of each.
16. What is data replication, and what are the three replication strategies?
17. What are the two basic styles of data replication?
18. What trade-offs are involved in building highly distributed data environments?
19. How does a BASE system differ from a traditional distributed database system?
Problems
Problem 1 is based on the DDBMS scenario in Figure P12.1.
FIGURE P12.1 THE DDBMS SCENARIO FOR PROBLEM 1
TABLES FRAGMENTS
LOCATION
CUSTOMER N/A A PRODUCT PROD_A A PROD_B B INVOICE N/A B INV_LINE N/A B
Site A
Site B
Site C
1. Specify the minimum types of operations the database must support to perform the following operations. These operations include remote requests, remote transactions, distributed transactions, and distributed requests.
At Site C
a. SELECT FROM
* CUSTOMER;
*
INVOICE INV_TOT < 1000; * PRODUCT PROD_ QOH < 10; Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. b. SELECT FROM WHERE c. SELECT FROM WHERE d. BEGIN WORK; UPDATE CUSTOMER CUS_BAL = CUS_BAL + 100 CUS_NUM = '10936'; INTO INVOICE(INV_NUM, CUS_NUM, INV_ DATE, INV_TOTAL) VALUES ('986391', '10936', '15-FEB-2016', 100); INTO LINE(INV_NUM, PROD_NUM, LINE_PRICE) VALUES('986391', '1023', 100); PRODUCT PROD_QOH = PROD_ QOH –1 PROD_NUM = '1023'; INTO CUSTOMER(CUS_NUM, CUS_NAME, CUS_ ADDRESS, CUS_BAL) VALUES ('34210', 'Victor Ephanor', '123 Main St.', 0.00); INTO INVOICE(INV_NUM, CUS_NUM, INV_ DATE, INV_TOTAL) VALUES ('986434', '34210', '10-AUG-2016', 2.00); CUS_NUM, CUS_NAME, INV_TOTAL CUSTOMER, INVOICE CUSTOMER.CUS_NUM = INVOICE.CUS_NUM; * INVOICE INV_TOTAL > 1000;
*
PRODUCT PROD_QOH < 10; * CUSTOMER; CUS_NAME, INV_TOTAL CUSTOMER, INVOICE INV_TOTAL > 1000 AND CUSTOMER.CUS_NUM =
INVOICE.CUS_NUM;
*
PRODUCT PROD_QOH < 10; and constraints exist for a magazine publishing company: a. The company publishes one regional magazine in each of four states: Florida (FL), South Carolina (SC), Georgia (GA), and Tennessee (TN). b. The company has 300,000 customers (subscribers) distributed throughout the four states listed in Problem 2a. c. On the first day of each month, an annual subscription INVOICE is printed and sent to each customer whose subscription is due for renewal. The INVOICE entity contains a REGION attribute to indicate the customer’s state of residence (FL, SC, GA, TN): CUSTOMER (CUS_NUM, CUS_NAME, CUS_ADDRESS, CUS_CITY, CUS_ ZIP, CUS_SUBSDATE) INVOICE (INV_NUM, INV_REGION, CUS_NUM, INV_DATE, INV_TOTAL) Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. SET WHERE INSERT INSERT UPDATE SET WHERE COMMIT WORK; e. BEGIN WORK; INSERT INSERT COMMIT WORK; At Site A f. SELECT FROM WHERE g. SELECT FROM WHERE h. SELECT FROM WHERE At Site B i. SELECT FROM j. SELECT FROM WHERE k. SELECT FROM WHERE 2. The following data structure Chapter 12 Distributed Database Management Systems 587 588 Part 4 Advanced Database Concepts The company is aware of the problems associated with centralized manage- ment and has decided to decentralize management of the subscriptions into the company’s four regional subsidiaries. Each subscription site will handle its own customer and invoice data. The management at company headquarters, however, will have access to customer and invoice data to generate annual reports and to issue ad hoc queries such as: • Listing all current customers by region • Listing all new customers by region • Reporting all invoices by customer and by region Given these requirements, how must you partition the database? 3. Given the scenario and requirements in Problem 2, answer the following questions: a. What recommendations will you make regarding the type and characteristics of the required database system? b. What type of data fragmentation is needed for each table? c. What criteria must be used to partition each database? d. Design the database fragments. Show an example with node names, location, fragment names, attribute names, and demonstration data. e. What type of distributed database operations must be supported at each remote site? f. What type of distributed database operations must be supported at the headquarters site? Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses In this chapter, you will learn: • How business intelligence provides a comprehensive business decision support framework • About business intelligence architecture, its evolution, and reporting styles • About the relationship and differences between operational data and decision support data • What a data warehouse is and how to prepare data for one • What star schemas are and how they are constructed • About data analytics • About online analytical processing (OLAP) • How SQL extensions are used to support OLAP-type data manipulations Preview Business intelligence (BI) is the collection of best practices and software tools developed to support business decision making in this age of globalization, emerging markets, rapid change, and increasing regulation. The complexity and range of information required to support business decisions has increased, and operational database structures were unable to support all of these requirements. Therefore, a new data storage facility, called a data warehouse, developed. The data warehouse extracts its data from operational databases as well as from external sources, providing a more comprehensive data pool. Additionally, new ways to analyze and present decision support data were developed. Online analytical processing (OLAP) provides advanced data analysis and visualization tools, including multidimensional data analysis. This chapter explores the main concepts and components of business intelligence and decision support systems that gather, gener- ate, and present information for business decision makers, focusing especially on the use of data warehouses. Data Files and Available Formats MS Access Oracle MS SQL My SQL MS Access Oracle MS SQL My SQL CH13_Text P P P PCH13_PI CH13_P3 CH13_P4 P P P P P P P P P P P P CH13_SaleCo_DW P P P P Data Files Available on cengagebrain.com Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 590 Part 4 Advanced Database Concepts 13-1 The Need for Data Analysis Organizations tend to grow and prosper as they gain a better understanding of their environment. Most managers need to track daily transactions to evaluate how the busi- ness is performing. By tapping into the operational database, management can develop an understanding of how the company is performing and evaluate whether the current strategies meet organizational goals. In addition, analyzing the company data can pro- vide insightful information about short-term tactical evaluations and strategic questions, such as: Are our sales promotions working? What market percentage are we controlling? Are we attracting new customers? Tactical and strategic decisions are also shaped by constant pressure from external and internal forces, including globalization, the cultural and legal environment, and technology. Organizations are always looking for a competitive advantage through product devel- opment, market positioning, sales promotions, and customer service. Thanks to the Internet, customers are more informed than ever about the products they want and the prices they are willing to pay. Technology advances allow customers to place orders using their smart phones while they commute to work in the morning. Decision makers can no longer wait a couple of days for a report to be generated; they are compelled to make quick decisions if they want to remain competitive. Every day, TV ads offer low-price warranties, instant price matching, and so on. How can companies survive on lower margins and still make a profit? The key is in having the right data at the right time to support the decision-making process. This process takes place at all levels of an organization. For example, transaction- processing systems, based on operational databases, are tailored to serve the information needs of people who deal with short-term inventory, accounts payable, and purchasing. Middle-level managers, general managers, vice presidents, and presidents focus on stra- tegic and tactical decision making. Those managers require summarized information designed to help them make decisions in a complex business environment. Companies and software vendors addressed these multilevel decision support needs by creating autonomous applications for particular groups of users, such as those in finance, customer management, human resources, and product support. Applications were also tailored to different industries such as education, retail, health care, and finance. This approach worked well for some time, but changes in the business world, such as globalization, expanding markets, mergers and acquisitions, increased regulation, and new technologies, called for new ways of integrating and managing decision support across levels, sectors, and geographic locations. This more comprehensive and integrated decision support framework within organizations became known as business intelligence. 13-2 Business Intelligence Business intelligence (BI)1 is a term that describes a comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support business decision making. This intelligence is based on learning and understanding the facts about the business environment. BI is a framework that allows a business to transform data into information, information into knowledge, and knowledge into wisdom. BI has the potential to positively affect a company’s culture by creating continuous business per- formance improvement through active decision support at all levels in an organization. 1 In 1989, while working at Gartner, Inc., Howard Dresner popularized BI as an umbrella term to describe a set of concepts and methods to improve business decision making by using fact-based support systems (www.computerworld.com/s/article/266298/BI_at_age_17). rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. business intelligence (BI) A comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support business decision making. Copyright 2017 Cengage Lea Editorial review has deemed that This business insight empowers users to make sound decisions based on the accumu- lated knowledge of the business. BI’s initial adopters were high-volume industries such as financial services, insurance, and healthcare companies. As BI technology evolved, its usage spread to other industries such as telecommunications, retail/merchandising, manufacturing, media, government, and even education. Table 13.1 lists some companies that have implemented BI tools and shows how the tools benefited the companies. You will learn about these tools later in the chapter. TABLE 13.1 SOLVING BUSINESS PROBLEMS AND ADDING VALUE WITH BI TOOLS COMPANY PROBLEM BENEFIT Chapter 13 Business Intelligence and Data Warehouses 591 CiCi’s Enterprises Eighth-largest pizza chain in the United States; operates 650 pizza restaurants in 30 states Source: Cognos Corp. www.cognos.com • Information access was cumbersome and time-consuming • Needed to increase accuracy in the creation of marketing budgets • Needed an easy, reliable, and efficient way to access daily data • Provided accurate, timely budgets in less time • Provided analysts with access to data for decision-making purposes • Received in-depth view of product performance by store to reduce waste and increase profits NASDAQ Largest U.S. electronic stock market trading organization Source: Oracle Corp. www.oracle.com • Inability to provide real-time, ad hoc query and standard reporting for executives, business analysts, and other users • Excessive storage costs for many terabytes of data • Reduced storage costs by moving to a multitier storage solution • Implemented new data warehouse center with support for ad hoc query and reporting, and near real-time data access for end users Pfizer Global pharmaceutical company Source: Oracle Corp. www.oracle.com • Needed a way to control costs and adjust to tougher market conditions, international competition, and increasing government regulations • Needed better analytical capabilities and flexible decision-making framework • Ability to get and integrate financial data from multiple sources in a reliable way • Streamlined, standards-based financial analysis to improve forecasting process • Faster and smarter decision making for business strategy formulation Swisscom Switzerland’s leading telecommunications provider Source: Microsoft Corp. www.microsoft.com • Needed a tool to help employees monitor service-level compliance • Had a time-consuming process to generate performance reports • Needed a way to integrate data from 200 different systems • Ability to monitor performance using dashboard technology • Quick and easy access to real-time performance data • Managers have closer and better control over costs Implementing BI in an organization involves capturing not only internal and external business data, but also the metadata, or knowledge about the data. In practice, BI is a complex proposition that requires a deep understanding and alignment of the business processes, business data, and information needs of users at all levels in an organization. (See Appendix O, Data Warehouse Implementation Factors.) BI is not a product by itself, but a framework of concepts, practices, tools, and tech- nologies that help a business better understand its core capabilities, provide snapshots of the company situation, and identify key opportunities to create competitive advantage. In general, BI provides a framework for: • • • • Collecting and storing operational data Aggregating the operational data into decision support data Analyzing decision support data to generate information Presenting such information to the end user to support business decisions Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 592 Part 4 Advanced Database Concepts • Making business decisions, which in turn generate more data that is collected, stored, and so on (restarting the process) • Monitoring results to evaluate outcomes of the business decisions, which again provides more data to be collected, stored, and so on • Predicting future behaviors and outcomes with a high degree of accuracy The preceding points represent a system-wide view of the flow of data, processes, and outcomes within the BI framework. In practice, the first point, collecting and stor- ing operational data, does not fall into the realm of a BI system per se; rather, it is the function of an operational system. However, the BI system will use the operational data as input material from which information will be derived. The rest of the processes and outcomes explained in the preceding points are oriented toward generating knowledge, and they are the focus of the BI system. In the following section, you will learn about the basic BI architecture. 13-2a Business Intelligence Architecture BI covers a range of technologies and applications to manage the entire data life cycle from acquisition to storage, transformation, integration, presentation, analysis, monitor- ing, and archiving. BI functionality ranges from simple data gathering and transforma- tion to very complex data analysis and presentation. BI architecture ranges from highly integrated single-vendor systems to loosely integrated, multivendor environments. However, some common functions are expected in most BI implementations. Like any critical business IT infrastructure, the BI architecture is composed of many interconnected parts: people, processes, data, and technology working together to facil- itate and enhance a business’s management and governance. Figure 13.1 depicts how all these components fit together within the BI framework. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE13.1 BUSINESSINTELLIGENCEFRAMEWORK People Business Intelligence Framework Processes External Operational Data visualization Monitoring Data and alerting analytics Query & reporting Data store Data warehouse data Extraction, transformation, and loading data Management Governance ETL t r a m D a t a The general BI framework depicted in Figure 13.1 has six basic components that encompass the functionality required on most current-generation BI systems. You will learn more about these components later in this and future chapters. The components are briefly described in Table 13.2. TABLE 13.2 BASIC BI ARCHITECTURAL COMPONENTS COMPONENT DESCRIPTION Chapter 13 Business Intelligence and Data Warehouses 593 ETL tools Data extraction, transformation, and loading (ETL) tools collect, filter, integrate, and aggregate internal and external data to be saved into a data store optimized for decision support. Internal data is generated by the company during its day-to-day operations, such as product sales history, invoicing, and payments. The external data sources provide data that cannot be found within the company but is relevant to the business, such as stock prices, market indicators, marketing information (such as demographics), and competitors’ data. Such data is generally located in external databases provided by industry groups or companies that market the data. Data store The data store is optimized for decision support and is generally represented by a data warehouse or a data mart. The data is stored in structures that are optimized for data analysis and query speed. Query and reporting This component performs data selection and retrieval, and it is used by the data analyst to create queries that access the database and create the required reports. Depending on the implementation, the query and reporting tool accesses the operational database, or more commonly, the data store. Data visualization This component presents data to the end user in a variety of meaningful and innovative ways. This tool helps the end user select the most appropriate presentation format, such as summary reports, maps, pie or bar graphs, mixed graphs, and static or interactive dashboards. Data monitoring and alerting This component allows real-time monitoring of business activities. The BI system will present the concise information in a single integrated view for the data analyst. This integrated view could include specific metrics about the system performance or activities, such as number of orders placed in the last four hours, number of customer complaints by product by month, and total revenue by region. Alerts can be placed on a given metric; once the value of a metric goes below or above a certain baseline, the system will perform a given action, such as emailing shop floor managers, presenting visual alerts, or starting an application. Data analytics This component performs data analysis and data-mining tasks using the data in the data store. This tool advises the user as to which data analysis tool to select and how to build a reliable business data model. Business models are generated by special algorithms that identify and enhance the understanding of business situations and problems. Data analysis can be either explanatory or predictive. Explanatory analysis uses the existing data in the data store to discover relationships and their types, and predictive analysis creates statistical models of the data that allow predictions of future values and events. Chapter 14, Big Data Analytics and NoSQL, covers these topics in more detail. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. extraction, transformation, and loading (ETL) In a data warehousing environment, the integrated processes of getting data from original sources into the data warehouse. ETL includes retrieving data from original data sources (extraction), manipulating the data into an appropriate form (transformation), and storing the data in the data warehouse (loading). 594 Part 4 Advanced Database Concepts Each BI component shown in Table 13.2 has generated a fast-growing market for specialized tools. Thanks to technological advancements, the components can interact with other components to form a truly open architecture. As a matter of fact, you can integrate multiple tools from different vendors into a single BI framework. Table 13.3 shows a sample of common BI tools and vendors. TABLE 13.3 SAMPLE OF BUSINESS INTELLIGENCE TOOLS TOOL DESCRIPTION SAMPLE VENDORS Dashboards and business activity monitoring Dashboards use web-based technologies to present key business performance indicators or information in a single integrated view, generally using graphics that are clear, concise, and easy to understand. Salesforce IBM/Cognos BusinessObjects Information Builders iDashboards Portals Portals provide a unified, single point of entry for information distribution. Portals are a web-based technology that use a web browser to integrate data from multiple sources into a single webpage. Many different types of BI functionality can be accessed through a portal. Oracle Portal Actuate Microsoft SAP Data analysis and reporting tools These advanced tools are used to query multiple and diverse data sources to create integrated reports. Microsoft Reporting Services MicroStrategy SAS WebReportStudio Data-mining tools These tools provide advanced statistical analysis to uncover problems and opportunities hidden within business data. Chapter 14 covers data mining in more detail. SAP Teradata MicroStrategy MS Analytics Services Data warehouses (DW ) The data warehouse is the foundation of a BI infrastructure. Data is captured from the production system and placed in the DW on a near real-time basis. BI provides company-wide integration of data and the capability to respond to business issues in a timely manner. Microsoft Oracle IBM/Cognos Teradata OLAP tools Online analytical processing provides multidimensional data analysis. IBM/Cognos BusinessObjects Oracle Microsoft Data visualization These tools provide advanced visual analysis and techniques to enhance understanding and create additional insight of business data and its true meaning. Dundas Tableau QlikView Actuate dashboard In business intelligence, a web-based system that presents key business performance indicators or information in a single, integrated view with clear and concise graphics. portal In terms of business intelligence, a unified, single point of entry for information distribution. Note You will learn about data warehouses and OLAP tools later in this chapter, and learn about data mining in Chapter 14. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. As depicted in Figure 13.1, BI integrates people and processes using technology at all levels of the organization. A sound BI strategy adds value to an organization by provid- ing the right data, in the right format, to the right people, at the right time. Such value is derived from how end users apply such information in their daily activities, and partic- ularly in their daily business decision making. The focus of traditional information systems was on operational automation and reporting; in contrast, BI tools focus on the strategic and tactical use of information. To achieve this goal, BI recognizes that technology alone is not enough. Therefore, BI uses an arrangement of best management practices to manage data as a corporate asset. One of the most recent developments in this area is the use of master data management techniques. Master data management (MDM) is a collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization. MDM’s main goal is to provide a comprehensive and consistent definition of all data within an organization. MDM ensures that all company resources (people, procedures, and IT systems) that work with data have uniform and consistent views of the company’s data. An added benefit of this meticulous approach to data management and decision making is that it provides a framework for business governance. Governance is a method or process of government. In this case, BI provides a method for controlling and monitoring business health and for consistent decision making. Furthermore, hav- ing such governance creates accountability for business decisions. In the present age of business flux, accountability is increasingly important. Had governance been as pivotal to business operations a few years back, crises precipitated by Enron, WorldCom, Arthur Andersen, and the 2008 financial meltdown might have been avoided. Monitoring a business’s health is crucial to understanding where the company is and where it is headed. To do this, BI makes extensive use of a special type of metrics known as key performance indicators. Key performance indicators (KPIs) are quantifiable numeric or scale-based measurements that assess the company’s effectiveness or success in reaching its strategic and operational goals. Many different KPIs are used by different industries. Some examples of KPIs are: • General. Year-to-year measurements of profit by line of business, same-store sales, product turnovers, product recalls, sales by promotion, and sales by employee • Finance. Earnings per share, profit margin, revenue per employee, percentage of sales to account receivables, and assets to sales • Human resources. Applicants to job openings, employee turnover, and employee longevity • Education. Graduation rates, number of incoming freshmen, student retention rates, publication rates, and teaching evaluation scores KPIs are determined after the main strategic, tactical, and operational goals are defined for a business. To tie the KPI to the strategic master plan of an organization, a KPI is compared to a desired goal within a specific time frame. For example, if you are in an academic environment, you might be interested in ways to measure student satisfaction or retention. In this case, a sample goal would be to increase the final exam grades of graduating high school seniors by Fall 2019. Another sample KPI would be to increase the returning student rate from freshman year to sophomore year from 60 percent to 75 percent by 2019. In this case, such performance indicators would be measured and monitored on a year-to-year basis, and plans to achieve such goals would be set in place. Although BI has an unquestionably important role in modern business operations, the manager must initiate the decision support process by asking the appropriate questions. master data management (MDM) In business intelligence, a collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization. governance In business intelligence, the methods for controlling and monitoring business health and promoting consistent decision making. key performance indicators (KPIs) In business intelligence, quantifiable numeric or scale-based measurements that assess a company’s effectiveness or success in reaching strategic and operational goals. Examples of KPIs are product turnovers, sales by promotion, sales by employee, and earnings per share. Chapter 13 Business Intelligence and Data Warehouses 595 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 596 Part 4 Advanced Database Concepts The BI environment exists to support the manager; it does not replace the management function. If the manager fails to ask the appropriate questions, problems will not be identified and solved, and opportunities will be missed. In spite of the very powerful BI presence, the human component is still at the center of business technology. Having a well-implemented BI environment (people, processes, technology, manage- ment, and governance) positions a company to react quickly to changes in the environ- ment. Today’s customers are more connected than ever with other customers (current or potential), companies, and organizations. In certain industries, social media plays a key role in marketing, brand recognition, and development. A simple tweet could generate millions of dollars in new sales or could cost a company millions of dollars in revenue. Companies monitor social media data to identify trends and quickly react to current or future threats or opportunities. Data visualization is abstracting data to provide information in a visual format that enhances the user’s ability to effectively comprehend the meaning of the data. The goal of data visualization is to allow the user to see the big picture in the most efficient way pos- sible. Tables with hundreds, thousands, or millions of rows of data cannot be processed by the human mind. Providing summarized tabular data to managers does not give them the insight into the meaning of the data that they need to make informed decisions. Data visualization aggregates the data into a format that provides at-a-glance insight into overall trends and patterns. Data visualization techniques can range from simple to very complex, and many are familiar. Techniques include: pie charts, line graphs, bar charts, scatter plots, Gantt charts, heat maps, and more. An example of a heat map is shown in Figure 13.2. This heat map was created using Tableau (www.tableau.com), a data visualization tool, to analyze sales for a company. data visualization Abstracting data to provide information in a visual format that enhances the user’s ability to effectively comprehend the meaning of the data. FIGURE 13.2 VISUALIZING SALES TOTAL BY zip CODE Courtesy of Tableau Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. The size of the circles is determined by the dollar value of the sales summed for all sales in each zip code, such that larger total sales produce a larger circle. The circles are then mapped against a geographical map of the United States based on the zip code. The figure makes it easy for a manager to quickly see the region of the northeastern United States that has the greatest sale penetration. In addition to specialized data visualization software such as Tableau, R, and Gephi, common productivity tools such as Microsoft Excel can often provide surpris- ingly powerful data visualization. Excel has long provided basic charting abilities and PivotTable and PivotChart capabilities for visualizing spreadsheet data. More recently, the introduction of the PowerPivot add-in has eliminated row and column data limitations and allows for the integration of data from multiple sources. This puts powerful data visualization capabilities on the desktop of most business users. Note The main BI architectural components were illustrated in Figure 13.1 and further explained in Tables 13.2 and 13.3. However, the heart of the BI system is its advanced information generation and decision support capabilities. A BI system’s advanced deci- sion support functions come to life via its intuitive and informational user interface, and particularly its reporting capabilities. A modern BI system provides three distinctive reporting styles: • Advanced reporting. A BI system presents insightful information about the organiza- tion in a variety of presentation formats. Furthermore, the reports provide interactive features that allow the end user to study the data from multiple points of view—from highly summarized to very detailed data. The reports present key actionable informa- tion used to support decision making. • Monitoring and alerting. After a decision has been made, the BI system offers ways to monitor the decision’s outcome. The BI system provides the end user with ways to define metrics and other key performance indicators to evaluate different aspects of an organization. In addition, exceptions and alerts can be set to warn managers promptly about deviations or problem areas. • Advanced data analytics. A BI system provides tools to help the end user discover relationships, patterns, and trends hidden within the organization’s data. These tools are used to create two types of data analysis: explanatory and predictive. Explanatory analysis provides ways to discover relationships, trends, and patterns among data, while predictive analysis provides the end user with ways to create models that predict future outcomes. Understanding the architectural components of a BI framework is the first step in properly implementing BI in an organization. A good BI infrastructure promises many benefits to an organization, as outlined in the next section. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 597 Data visualization plays an important role in discovering and understanding the meaning of data. New ways to present data are being constantly developed. Good data visualizations can be used in any discipline. For example, see the video from Dr. Hans Rosling, (www.youtube.com/watch?v=jbkSRLYSojo) in which he uses public health data to visualize the history of the world in the last 200 years. 598 Part 4 Advanced Database Concepts 13-2b Business Intelligence Benefits As you have learned in previous sections, a properly implemented BI architecture could provide a framework for continuous performance improvements and business decision making. Improved decision making is the main goal of BI, but BI provides other benefits: • Integrating architecture. Like any other IT project, BI has the potential of becoming the integrating umbrella for a disparate mix of IT systems within an organization. This architecture could support all types of company-generated data from operational to executive, as well as diverse hardware such as mainframes, servers, desktops for managers and executives, and mobile devices on the shop floor. • Common user interface for data reporting and analysis. BI front ends can provide up-to-the-minute consolidated information using a common interface for all com- pany users. IT departments no longer have to provide multiple training options for diverse interfaces. End users benefit from similar or common interfaces in different devices that use multiple clever and insightful presentation formats. • Common data repository fosters single version of company data. In the past, multiple IT systems supported different aspects of an organization’s operations. Such systems collected and stored data in separate data stores. Keeping the data synchronized and up to date has always been difficult. BI provides a framework to integrate such data under a common environment and present a single version of the data. • Improved organizational performance. BI can provide competitive advantages in many different areas, from customer support to manufacturing processes. Such advantages can be reflected in added efficiency, reduced waste, increased sales, reduced employee and customer turnover, and most importantly, an increased bottom line for the business. Achieving all these benefits takes a lot of human, financial, and technological resources, not to mention time. BI benefits are not achieved overnight, but are the result of a focused company-wide effort that could take a long time. As a matter of fact, as you will learn in the next section, the BI field has evolved over a long period of time itself. 13-2c Business Intelligence Evolution Providing useful information to end users has been a priority of IT systems since main- frame computing became an integral part of corporations. Business decision support has evolved over many decades. Following computer technology advances, business intelligence started with centralized reporting systems and evolved into today’s highly integrated BI environments. Table 13.4 summarizes the evolution of BI systems. Using Table 13.4 as a guide, you can trace business intelligence from the mainframe environment to the desktop and then to the more current, cloud-based, mobile BI environments. (Chapter 15, Database Connectivity and Web Technologies, provides a detailed discussion of cloud-based systems.) The precursor of the modern BI environment was the first-generation decision sup- port system. A decision support system (DSS) is an arrangement of computerized tools used to assist managerial decision making. A DSS typically has a much narrower focus and reach than a BI solution. At first, decision support systems were the realm of a few selected managers in an organization. Over time, and with the introduction of the desktop computer, decision support systems migrated to more agile platforms, such as midrange computers, high-end servers, commodity servers, appliances, and cloud-based offerings. This evolution effectively changed the reach of decision support systems; BI is no longer limited to a small group of top-level managers with training in statistical rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. decision support system (DSS) An arrangement of computerized tools used to assist managerial decision making within a business. Copyright 2017 Cengage Lea Editorial review has deemed that TABLE 13.4 BUSINESS INTELLIGENCE EVOLUTION SYSTEM TYPE DATA SOURCE DATA EXTRACTION/ INTEGRATION PROCESS DATA STORE END-USER QUERY TOOL END USER PRESENTATION TOOL Chapter 13 Business Intelligence and Data Warehouses 599 Traditional mainframe- based online transaction processing (OLTP) Operational data None Reports read and summarized data directly from operational data None Temporary files used for reporting purposes Very basic Predefined reporting formats Basic sorting, totaling, and averaging Very basic Menu-driven, predefined reports, text and numbers only Managerial information system (MIS) Operational data Basic extraction and aggregation Read, filter, and summarize operational data into intermediate data store Lightly aggregated data in RDBMS Same as above, in addition to some ad hoc reporting using SQL Same as above, in addition to some ad hoc columnar report definitions First-generation departmental decision support system (DSS) Operational data External data Data extraction and integration process populates DSS data store Run periodically First DSS database generation Usually RDBMS Query tool with some analytical capabilities and predefined reports Spreadsheet style Advanced presentation tools with plotting and graphics capabilities First-generation BI Operational data External data Advanced data extraction and integration Access diverse data sources, filters, aggregations, classifications, scheduling, and conflict resolution Data warehouse RDBMS technology Optimized for query purposes Star schema model Same as above Same as above, in addition to multidimensional presentation tools with drill-down capabilities Second- generation BI Online analytical processing (OLAP) Same as above Same as above Data warehouse stores data in MDBMS Cubes with multiple dimensions Adds support for end-user- based data analytics Same as above, but uses cubes and multidimensional matrixes; limited by cube size Dashboards Scorecards Portals Third-generation Mobile, cloud- based, and Big Data Same as above Includes social media and machine- generated data Same as above Cloud-based Same as above Cloud-based Hadoop and NoSQL databases Advanced analytics Limited ad hoc interactions Mobile devices: smartphones and tablets Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 600 Part 4 Advanced Database Concepts modeling. Instead, BI is now available to all users in an organization, from line managers to the shop floor to mobile agents in the field. You can also use Table 13.4 to track the evolution of information dissemination styles used in business intelligence. • Starting in the late 1970s, the need for information distribution was filled by central- ized reports running on mainframes, minicomputers, or even central server environ- ments. Such reports were predefined and took considerable time to process. • With the introduction of desktop computers in the 1980s, a new style of information distribution, the spreadsheet, emerged as the dominant format for decision support systems. In this environment, managers downloaded information from centralized data stores and manipulated the data in desktop spreadsheets. • As the use of spreadsheets multiplied, IT departments tried to manage the flow of data in a more formal way using enterprise reporting systems. These systems were developed in the early 1990s and basically integrated all data into an IT umbrella that started with the first-generation DSS. The systems still used spreadsheet-like features with which end users were familiar. • Once DSSs were established, the evolution of business intelligence flourished with the introduction of the data warehouse and online analytical processing systems (OLAPs) in the mid-1990s. • Rapid changes in information technology and the Internet revolution led to the introduction of advanced BI systems such as web-based dashboards in the early and mid 2000s and mobile BI later in the decade. With mobile BI, end users access BI reports via native applications that run on a mobile smart device, such as a smart- phone or tablet. • More recently, the social media revolution has generated large amounts of data. At the same time sensor-generated data is being collected and stored. Companies are using Big Data analytics tools to leverage such data and obtain critical information otherwise unavailable to them. Figure 13.3 depicts the evolution of BI information dissemination. FIGURE13.3 EVOLUTIONOFBIINFORMATIONDISSEMINATIONFORMATS 1970s Centralized reporting 1980s Spreadsheets 1990s Enterprise reporting OLAP 2000s Dashboards Mobile BI 2010s+ Big Data analytics/Hadoop /NoSQL © Antun Hirsman/Shutterstock.com Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Note Chapter 13 Business Intelligence and Data Warehouses 601 The OLAP environment is covered in Section 13-6 of this chapter. Mobile BI technology is poised to have a significant impact on the way BI information is disseminated and processed. If the number of students using smartphones to com- municate with friends, update their Facebook status, and send tweets on Twitter is any indicator, you can expect the next generation of consumers and workers to be highly mobile. Leading corporations are therefore starting to push decision making to agents in the field to facilitate customer relationships, sales and ordering, and product support. Such mobile technologies are so portable and interactive that some users call them “disruptive” technologies. BI information technology has evolved from centralized reporting styles to the cur- rent, mobile BI and Big Data analytics style in the span of just a few years. The rate of technological change is not slowing down; to the contrary, technology advancements are accelerating the adoption of BI to new levels. The next section illustrates some BI technology trends. 13-2d Business Intelligence Technology Trends Several technological advances are driving the growth of business intelligence technol- ogies. These advances create new generations of more affordable products and services that are faster and easier to use. In turn, such products and services open new markets and work as driving forces in the increasing adoption of business intelligence technolo- gies within organizations. Some of the more remarkable technological trends are: • Data storage improvements. Newer data storage technologies, such as solid state drives (SSD) and Serial Advanced Technology Attachment (SATA) drives, offer increased performance and larger capacity that make data storage faster and more affordable. Currently you can buy single drives with a capacity approaching 4 terabytes. • Business intelligence appliances. Vendors now offer plug-and-play appliances opti- mized for data warehouse and BI applications. These new appliances offer improved price-performance ratios, simplified administration, rapid installation, scalability, and fast integration. Some of these vendors include IBM, Netezza, Greenplum, and AsterData. • Business intelligence as a service. Vendors now offer data warehouses and BI as a service. These cloud-based services allow any corporation to rapidly develop a data warehouse store without the need for hardware, software, or extra personnel. These prepackaged services offer “pay-as-you-go” models for specific industries and capaci- ties, and they provide an opportunity for organizations to pilot-test a BI project with- out incurring large time or cost commitments. Such services are offered by Netezza, AppNexus, AsterData, MicroStrategy, and Kognitio. • Big Data analytics. The Big Data phenomenon is creating a new market for data analytics. Organizations are turning to social media as the new source for informa- tion and knowledge to gain competitive advantages. Examples of Big Data analytics vendors include Vertica, AsterData, and Netezza. You’ll learn more about Big Data analytics in Chapter 14. • Personal analytics. OLAP brought data analytics to the desktop of every end user in an organization. Mobile BI is extending business decision making outside the walls Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 602 Part 4 Advanced Database Concepts of the organization. BI can now be deployed to mobile users who are closer to cus- tomers. The main requirement is for the BI end user to have a key understanding of the business. Some personal analytics vendors include MicroStrategy, QlikView, and Actuate. There is a growing trend toward self-service, personalized data analytics. It is not so far-fetched to imagine that in a few years, end users will have smart data ana- lytics agents on their smartphones tailored to their personal interests. Such personal agents will provide users with up-to-the-minute “intelligent knowledge” about their personal interests. One constant in this relentless technological evolution is the need for better decision support data and the importance of understanding the difference between decision sup- port data and operational data. 13-3 Decision Support Data Although BI is used at strategic and tactical managerial levels within organizations, its effectiveness depends on the quality of data gathered at the operational level. Yet, opera- tional data is seldom well suited to the decision support tasks. The differences between operational data and decision support data are examined in the next section. 13-3a Operational Data Versus Decision Support Data Operational data and decision support data serve different purposes. Therefore, it is not surprising to learn that their formats and structures differ. Most operational data is stored in a relational database in which the structures (tables) tend to be highly normalized. Operational data storage is optimized to support transactions that rep- resent daily operations. For example, each time an item is sold, it must be accounted for. Customer data, inventory data, and other similar data need frequent updating. To provide effective update performance, operational systems store data in many tables, each with a minimum number of fields. Thus, a simple sales transaction might be rep- resented by five or more different tables, such as INVOICE, INVOICE LINE, DIS- COUNT, STORE, and DEPARTMENT. Although such an arrangement is excellent in an operational database, it is not efficient for query processing. For example, to extract a simple invoice, you would have to join several tables. Whereas operational data is useful for capturing daily business transactions, decision support data gives tactical and strategic business meaning to the operational data. From the data analyst’s point of view, decision support data differs from operational data in three main areas: time span, granularity, and dimensionality. • Time span. Operational data covers a short time frame. In contrast, decision support data tends to cover a longer time frame. Managers are seldom interested in a specific sales invoice to Customer X; rather, they tend to focus on sales generated during the last month, the last year, or the last five years. • Granularity (level of aggregation). Decision support data must be presented at different levels of aggregation, from highly summarized to nearly atomic. For exam- ple, if managers analyze regional sales, they must be able to access data showing the sales by region, by city within the region, by store within the city within the region, and so on. In that case, summarized data to compare the regions is required, along with data in a structure that enables a manager to drill down, or decompose, the data into more atomic components—that is, finer-grained data at lower levels of aggregation. In contrast, when you roll up the data, you are aggregating the data to a higher level. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. drill down To decompose data into more atomic components—that is, data at lower levels of aggregation. This approach is used primarily in a decision support system to focus on specific geographic areas, business types, and so on. roll up (1) To aggregate data into summarized components, that is, higher levels of aggregation. (2) In SQL, an OLAP extension used with the GROUP BY clause to aggregate data by different dimensions. Rolling up the data is the exact opposite of drilling down the data. Copyright 2017 Cengage Lea Editorial review has deemed that • Dimensionality. Operational data focuses on representing individual transactions rather than the effects of the transactions over time. In contrast, data analysts tend to include many data dimensions and are interested in how the data relates over those dimensions. For example, an analyst might want to know how Product X fared rela- tive to Product Z during the past six months by region, state, city, store, and customer. In that case, both place and time are part of the picture. Figure 13.4 shows how decision support data can be examined from multiple dimensions such as product, region, and year, using a variety of filters to produce each dimension. The ability to analyze, extract, and present information in meaningful ways is one of the differences between decision support data and transaction-at-a-time operational data. Note From the designer’s point of view, the differences between operational and decision support data are as follows: • Operational data represents transactions as they happen in real time. Decision sup- port data is a snapshot of the operational data at a given point in time. Therefore, decision support data is historic, representing a time slice of the operational data. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 603 FIGURE 13.4 TRANSFORMING OPERATIONAL DATA INTO DECISION SUPPORT DATA Operational Data Operational data has a narrow time span, low granularity, and single focus. Such data is usually represented in tabular format, in which each row represents a single transaction. This format often makes it difficult to derive useful information. Region Decision Support Data Time Product Agent Decision support system (DSS) data focuses on a broader time span, tends to have high levels of granularity, and can be examined in multiple dimensions. For example, note these possible aggregations: Sales The decision support data in Figure 13.4 shows the output for the solution to Problem 2 at the end of this chapter. 604 Part 4 Advanced Database Concepts • Operational and decision support data are different in terms of transaction type and transaction volume. Whereas operational data is characterized by update transac- tions, decision support data is mainly characterized by read-only transactions. Deci- sion support data also requires periodic updates to load new data that is summarized from the operational data. Finally, the concurrent transaction volume in operational data tends to be very high compared with the low to medium levels in decision support data. • Operational data is commonly stored in many tables, and the stored data rep- resents information about a given transaction only. Decision support data is gener- ally stored in a few tables derived from the operational data. The decision support data does not include the details of each operational transaction. Instead, decision support data represents transaction summaries; therefore, the decision support database stores data that is integrated, aggregated, and summarized for decision support purposes. • The degree to which decision support data is summarized is very high when con- trasted with operational data. Therefore, you will see a great deal of derived data in decision support databases. For example, rather than storing all 10,000 sales trans- actions for a given store on a given day, the decision support database might simply store the total number of units sold and the total sales dollars generated during that day. Decision support data might be collected to monitor such aggregates as total sales for each store or for each product. The purpose of the summaries is simple: they are used to establish and evaluate sales trends and product sales comparisons and to provide other data that serves decision needs. (How well are items selling? Should this product be discontinued? Has the advertising been effective as measured by increased sales?) • The data models that govern operational data and decision support data are differ- ent. The operational database’s frequent and rapid data updates make data anoma- lies a potentially devastating problem. Therefore, the data in a relational transaction (operational) system generally requires normalized structures that yield many tables, each of which contains the minimum number of attributes. In contrast, the decision support database is not subject to such transaction updates, and the focus is on query- ing capability. Therefore, decision support databases tend to be non-normalized and include few tables, each of which contains a large number of attributes. • The frequency and complexity of query activity in the operational database tends to be low to allow additional processing cycles for the more crucial update trans- actions. Therefore, queries against operational data typically are narrow in scope and low in complexity, and high speed is critical. In contrast, decision support data exists for the sole purpose of serving query requirements. Queries against decision support data typically are broad in scope and high in complexity, and less speed is needed. • Finally, decision support data is characterized by very large amounts of data. The large data volume is the result of two factors. First, data is stored in non-normalized struc- tures that are likely to display many data redundancies and duplications. Second, the same data can be categorized in many different ways to represent different snapshots. For example, sales data might be stored in relation to product, store, customer, region, and manager. Table 13.5 summarizes the differences between operational and decision support data from the database designer’s point of view. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. TABLE 13.5 CONTRASTING OPERATIONAL AND DECISION SUPPORT DATA CHARACTERISTICS CHARACTERISTIC OPERATIONAL DATA DECISION SUPPORT DATA Chapter 13 Business Intelligence and Data Warehouses 605 Data currency Current operations Real-time data Historic data Snapshot of company data Time component (week/month/year) Granularity Atomic-detailed data Summarized data Summarization level Low; some aggregate yields High; many aggregation levels Data model Highly normalized Mostly relational DBMSs Non-normalized Complex structures Some relational, but mostly multidimensional DBMSs Transaction type Mostly updates Mostly query Transaction volumes High-update volumes Periodic loads and summary calculations Transaction speed Updates are critical Retrievals are critical Query activity Low to medium High Query scope Narrow range Broad range Query complexity Simple to medium Very complex Data volumes Hundreds of gigabytes Terabytes to petabytes The many differences between operational data and decision support data are good indicators of decision support database requirements, which are described in the next section. 13-3b Decision Support Database Requirements A decision support database is a specialized DBMS tailored to provide fast answers to complex queries. There are three main requirements for a decision support database: the database schema, data extraction and filtering, and database size. Database Schema The decision support database schema must support complex (non-normalized) data representations. As noted earlier, the decision support database must contain data that is aggregated and summarized. In addition to meeting those requirements, the queries must be able to extract multidimensional time slices. If you are using an RDBMS, the conditions suggest using non-normalized and even duplicated data. To see why this must be true, take a look at the 10-year sales history for a single store containing a single department. At this point, the data is fully normalized within the single table, as shown in Table 13.6. This structure works well when you have only one store with only one department. However, it is very unlikely that such a simple environment has much need for a decision support database. A decision support database becomes a factor when you are dealing with more than one store, each of which has more than one department. To support all of the decision support requirements, the database must contain data for all of the stores and all of their departments—and the database must be able to support multi- dimensional queries that track sales by stores, by departments, and over time. For sim- plicity, suppose that there are only two stores (A and B) and two departments (1 and 2) within each store. Also, change the time dimension to include yearly data. Table 13.7 shows the sales figures under the specified conditions. Only 2006, 2012, and 2015 are shown; ellipses (...) are used to indicate that data values were omitted. You can see in Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 606 Part 4 Advanced Database Concepts TABLE 13.6 TEN-YEAR SALES HISTORY FOR A SINGLE DEPARTMENT, IN MILLIONS OF DOLLARS YEAR SALES 2006 8,227 2007 9,109 2008 10,104 2009 11,553 2010 10,018 2011 11,875 2012 12,699 2013 14,875 2014 16,301 2015 19,986 TABLE 13.7 YEARLY SALES SUMMARIES, TWO STORES AND TWO DEPARTMENTS PER STORE, IN MILLIONS OF DOLLARS YEAR STORE DEPARTMENT SALES 2006 A 1 1,985 2006 A 2 2,401 2006 B 1 1,879 2006 B 2 1,962 ... ... ... ... 2012 A 1 3,912 2012 A 2 4,158 2012 B 1 3,426 2012 B 2 1,203 ... ... ... ... 2015 A 1 7,683 2015 A 2 6,912 2015 B 1 3,768 2015 B 2 1,623 Table 13.7 that the number of rows and attributes already multiplies quickly and that the table exhibits multiple redundancies. Now suppose that the company has 10 departments per store and 20 stores nation- wide, and suppose that you want to access yearly sales summaries. Now you are dealing with 200 rows and 12 monthly sales attributes per row. (Actually, there are 13 attributes per row if you add each store’s sales total for each year.) The decision support database schema must also be optimized for query (read-only) retrievals. To optimize query speed, the DBMS must support features such as bitmap indexes and data partitioning. In addition, the DBMS query optimizer must be enhanced to support the non-normalized and complex structures in decision support databases. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Data Extraction and Filtering The decision support database is created largely by extracting data from the operational database and by importing additional data from external sources. Thus, the DBMS must support advanced data extraction and data- filtering tools. To minimize the impact on the operational database, the data extraction capabilities should allow batch and scheduled data extraction, and should support differ- ent data sources: flat files and hierarchical, network, and relational databases, as well as multiple vendors. Data-filtering capabilities must include the ability to check for incon- sistent data or data validation rules. Finally, to filter and integrate the operational data into the decision support database, the DBMS must support advanced data integration, aggregation, and classification. Using data from multiple external sources also usually means having to solve data- formatting conflicts. For example, data such as Social Security numbers and dates can occur in different formats; measurements can be based on different scales, and the same data elements can have different names. In short, data must be filtered and purified to ensure that only the pertinent decision support data is stored in the database and that it is stored in a standard format. DatabaseSize Decisionsupportdatabasestendtobeverylarge;gigabyteandtera- byte ranges are not unusual. For example, Walmart has more than 4 petabytes of data in its data warehouses. Therefore, the DBMS must be capable of supporting very large databases (VLDBs). To support a VLDB adequately, the DBMS might be required to support advanced storage technologies, and even more importantly, to support multiple-processor technologies, such as a symmetric multiprocessor (SMP) or a massively parallel processor (MPP). The complex information requirements and the ever-growing demand for sophisti- cated data analysis sparked the creation of a new type of data repository. This repository, called a data warehouse, contains data in formats that facilitate data extraction, data analysis, and decision making. It has become the foundation for a new generation of decision support systems. 13-4 The Data Warehouse Bill Inmon, the acknowledged “father” of the data warehouse, defines the term as “an integrated, subject-oriented, time-variant, nonvolatile collection of data that provides support for decision making.”2 (Italics were added for emphasis.) To understand that definition, take a more detailed look at its components. • Integrated. The data warehouse is a centralized, consolidated database that inte- grates data derived from the entire organization and from multiple sources with diverse formats. Data integration implies that all business entities, data elements, data characteristics, and business metrics are described in the same way throughout the enterprise. Although this requirement sounds logical, you would be amazed to discover how many different measurements for “sales performance” can exist within an organization; the same scenario can be true for any other business element. For instance, the status of an order might be indicated with text labels such as “open,” “received,” “canceled,” and “closed” in one department and as “1,” “2,” “3,” and “4” in another department. A student’s status might be defined as “freshman,” “sophomore,” “junior,” or “senior” in the accounting department and as “FR,” “SO,” “JR,” or “SR” in the computer information systems department. To avoid the potential format tangle, 2Bill Inmon and Chuck Kelley, “The twelve rules of data warehouse for a client/server world,” Data Management Review 4(5), May 1994, pp. 6–16. very large database (VLDB) Database that contains huge amounts of data— gigabyte, terabyte, and petabyte ranges are not unusual. data warehouse An integrated, subject- oriented, time-variant, nonvolatile collection of data that provides support for decision making. Chapter 13 Business Intelligence and Data Warehouses 607 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 608 Part 4 Advanced Database Concepts the data in the data warehouse must conform to a common format that is accept- able throughout the organization. This integration can be time-consuming, but once accomplished, it enhances decision making and helps managers better understand the company’s operations. This understanding can be translated into recognition of strategic business opportunities. • Subject-oriented. Data warehouse data is arranged and optimized to provide answers to questions from diverse functional areas within a company. Data warehouse data is organized and summarized by topic, such as sales, marketing, finance, distribution, and transportation. For each topic, the data warehouse con- tains specific subjects of interest—products, customers, departments, regions, promotions, and so on. This form of data organization is quite different from the more functional or process-oriented organization of typical transaction systems. For example, an invoicing system designer concentrates on designing normalized data structures to support the business process by storing invoice components in two tables: INVOICE and INVLINE. In contrast, the data warehouse has a subject orientation. Data warehouse designers focus specifically on the data rather than on the processes that modify the data. (After all, data warehouse data is not sub- ject to numerous real-time data updates!) Therefore, instead of storing an invoice, the data warehouse stores its “sales by product” and “sales by customer” compo- nents because decision support activities require the retrieval of sales summaries by product or customer. • Time-variant. In contrast to operational data, which focuses on current transactions, warehouse data represents the flow of data through time. The data warehouse can even contain projected data generated through statistical and other models. It is also time-variant in the sense that when data is periodically uploaded to the data ware- house, all time-dependent aggregations are recomputed. For example, when data for previous weekly sales is uploaded to the data warehouse, the weekly, monthly, yearly, and other time-dependent aggregates for products, customers, stores, and other vari- ables are also updated. Because data in a data warehouse constitutes a snapshot of the company history as measured by its variables, the time component is crucial. The data warehouse contains a time ID that is used to generate summaries and aggregations by week, month, quarter, year, and so on. Once the data enters the data warehouse, the time ID assigned to the data cannot be changed. • Nonvolatile. Once data enters the data warehouse, it is never removed. Because the data in the warehouse represents the company’s history, the operational data, which represents the near-term history, is always added to it. Because data is never deleted and new data is continually added, the data warehouse is always growing. Therefore, the DBMS must be able to support multiterabyte or greater databases operating on multiprocessor hardware. Table 13.8 summarizes the differences between data warehouses and operational databases. In summary, the data warehouse is a read-only database optimized for data analysis and query processing. Typically, data is extracted from various sources and are then transformed and integrated—in other words, passed through a data filter— before being loaded into the data warehouse. As mentioned, this process is known as ETL. Figure 13.5 illustrates the ETL process to create a data warehouse from operational data. Although the centralized and integrated data warehouse can be an attractive prop- osition that yields many benefits, managers may be reluctant to embrace this strategy. Creating a data warehouse requires time, money, and considerable managerial effort. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. TABLE 13.8 CHARACTERISTICS OF DATA WAREHOUSE DATA AND OPERATIONAL DATABASE DATA CHARACTERISTIC OPERATIONAL DATABASE DATA DATA WAREHOUSE DATA Chapter 13 Business Intelligence and Data Warehouses 609 Integrated Similar data can have different representations or meanings. For example, Social Security numbers may be stored as ###-##-#### or as #########, and a given condition may be labeled as T/F or 0/1 or Y/N. A sales value may be shown in thousands or in millions. Provide a unified view of all data elements with a common definition and representation for all business units. Subject-oriented Data is stored with a functional, or process, orientation. For example, data may be stored for invoices, payments, and credit amounts. Data is stored with a subject orientation that facilitates multiple views of the data and decision making. For example, sales may be recorded by product, division, manager, or region. Time-variant Data is recorded as current transactions. For example, the sales data may be the sale of a product on a given date, such as $342.78 on 12-MAY-2016. Data is recorded with a historical perspective in mind. Therefore, a time dimension is added to facilitate data analysis and various time comparisons. Nonvolatile Data updates are frequent and common. For example, an inventory amount changes with each sale. Therefore, the data environment is fluid. Data cannot be changed. Data is added only periodically from historical systems. Once the data is properly stored, no changes are allowed. Therefore, the data environment is relatively static. FIGURE 13.5 THE ETL PROCESS Operational data Extraction Transformation • Filter • Transform • Integrate • Classify • Aggregate • Summarize Loading Data warehouse • Integrated • Subject-oriented • Time-variant • Nonvolatile Therefore, it is not surprising that many companies begin their foray into data ware- housing by focusing on more manageable data sets that are targeted to meet the special needs of small groups within the organization. These smaller data stores are called data marts. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 610 Part 4 Advanced Database Concepts 13-4a Data Marts A data mart is a small, single-subject data warehouse subset that provides decision sup- port to a small group of people. In addition, a data mart could be created from data extracted from a larger data warehouse for the specific purpose of supporting faster data access to a target group or function. That is, data marts and data warehouses can coexist within a business intelligence environment. Some organizations choose to implement data marts not only because of the lower cost and shorter implementation time but because of the technological advances and inevitable “people issues” that make data marts attractive. Powerful computers can pro- vide a customized decision support system to small groups in ways that might not be possible with a centralized system. Also, a company’s culture may predispose its employ- ees to resist major changes, but they might quickly embrace relatively minor changes that lead to demonstrably improved decision support. In addition, people at different organizational levels are likely to require data with different summarization, aggregation, and presentation formats. Data marts can serve as a test vehicle for companies exploring the potential benefits of data warehouses. By gradually migrating from data marts to data warehouses, a specific department’s decision support needs can be addressed within six months to one year, as opposed to the one- to three-year time frame usually required to implement a data warehouse. Information technology (IT) departments also benefit from this approach because their personnel can learn the issues and develop the skills required to create a data warehouse. The only difference between a data mart and a data warehouse is the size and scope of the problem being solved. The problem definitions and data requirements are essentially the same for both. To be useful, the data warehouse must conform to uniform structures and formats to avoid data conflicts and support decision making. 13-4b Twelve Rules That Define a Data Warehouse In 1994, Bill Inmon and Chuck Kelley created a set of rules to define a data warehouse. These rules summarize many of the points made in this chapter about data warehouses.3 The 12 rules for a data warehouse are shown in Table 13.9. Note how the 12 rules capture the complete data warehouse life cycle—from its introduction as an entity separate from the operational data store to its components, functionality, and management processes. Most data warehouse implementations are based on the relational database model, and their market share suggests that their popularity will not fade anytime soon. Relational data warehouses use the star schema design technique to handle multidimensional data. 13-5 Star Schemas The star schema is a data-modeling technique used to map multidimensional deci- sion support data into a relational database. In effect, the star schema creates the near equivalent of a multidimensional database schema from the existing relational database. Star schemas yield an easily implemented model for multidimensional data analysis while preserving the relational structures on which the operational database is built. The basic star schema has four components: facts, dimensions, attributes, and attribute hierarchies. 3 Bill Inmon, and Chuck Kelley, “The twelve rules of data warehouse for a client/server world,” Data Manage- ment Review 4(5), May 1994, pp. 6–16. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. data mart A small, single-subject data warehouse subset that provides decision support to a small group of people. star schema A data modeling technique used to map multidimensional decision support data into a relational database. The star schema represents data using a central table known as a fact table in a 1:M relationship with one or more dimension tables. Copyright 2017 Cengage Lea Editorial review has deemed that TABLE 13.9 TWELVE RULES FOR A DATA WAREHOUSE RULE NO. DESCRIPTION Chapter 13 Business Intelligence and Data Warehouses 611 1 The data warehouse and operational environments are separated. 2 The data warehouse data is integrated. 3 The data warehouse contains historical data over a long time. 4 The data warehouse data is snapshot data captured at a given point in time. 5 The data warehouse data is subject oriented. 6 The data warehouse data is mainly read-only with periodic batch updates from operational data. No online updates are allowed. 7 The data warehouse development life cycle differs from classical systems development. Data warehouse development is data-driven; the classical approach is process-driven. 8 The data warehouse contains data with several levels of detail: current detail data, old detail data, lightly summarized data, and highly summarized data. 9 The data warehouse environment is characterized by read-only transactions to very large data sets. The operational environment is characterized by numerous update transactions to a few data entities at a time. 10 The data warehouse environment has a system that traces data sources, transformations, and storage. 11 The data warehouse’s metadata is a critical component of this environment. The metadata identifies and defines all data elements. The metadata provides the source, transformation, integration, storage, usage, relationships, and history of each data element. 12 The data warehouse contains a chargeback mechanism for resource usage that enforces optimal use of the data by end users. 13-5a Facts Facts are numeric measurements (values) that represent a specific business aspect or activity. For example, sales figures are numeric measurements that represent product and service sales. Facts commonly used in business data analysis are units, costs, prices, and revenues. Facts are normally stored in a fact table that is the center of the star schema. The fact table contains facts that are linked through their dimensions, which are explained in the next section. Facts can also be computed or derived at run time. Such computed or derived facts are sometimes called metrics to differentiate them from stored facts. The fact table is updated periodically with data from operational databases. 13-5b Dimensions Dimensions are qualifying characteristics that provide additional perspectives to a given fact. Recall that dimensions are of interest because decision support data is almost always viewed in relation to other data. For instance, sales might be compared by product from region to region and from one time period to the next. The kind of problem typically addressed by a BI system might be to compare the sales of unit X by region for the first quarters of 2006 through 2016. In that example, sales have product, location, and time dimensions. In effect, dimensions are the magnifying glass through which you study the facts. Such dimensions are normally stored in dimension tables. Figure 13.6 depicts a star schema for sales with product, location, and time dimensions. facts In a data warehouse, the measurements (values) that measure a specific business aspect or activity. For example, sales figures are numeric measurements that represent product or service sales. Facts commonly used in business data analysis include units, costs, prices, and revenues. fact table In a data warehouse, the star schema table that contains facts linked and classified through their common dimensions. A fact table is in a one- to-many relationship with each associated dimension table. metrics In a data warehouse, numeric facts that measure a business characteristic of interest to the end user. dimensions In a star schema design, qualifying characteristics that provide additional perspectives to a given fact. dimension tables In a data warehouse, tables used to search, filter, or classify facts within a star schema. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 612 Part 4 Advanced Database Concepts FIGURE13.6 SIMPLESTARSCHEMA Product dimension HP calculator Sales fact $125,000 Time dimension Location dimension TABLE 13.10 13-5c Attributes Each dimension table contains attributes. Attributes are often used to search, filter, or classify facts. Dimensions provide descriptive characteristics about the facts through their attributes. Therefore, the data warehouse designer must define common business attri- butes that will be used by the data analyst to narrow a search, group information, or describe dimensions. Using a sales example, some possible attributes for each dimension are illustrated in Table 13.10. POSSIBLE ATTRIBUTES FOR SALES DIMENSIONS DIMENSION NAME DESCRIPTION POSSIBLE ATTRIBUTES Location Anything that provides a description of the location— for example, Nashville, Store 101, South Region, and TN Region, state, city, store, and so on Product Anything that provides a description of the product sold—for example, hair care product, shampoo, Natural Essence brand, 5.5-oz. bottle, and blue liquid Product type, product ID, brand, package, presentation, color, size, and so on Time Anything that provides a time frame for the sales fact—for example, the year 2016, the month of July, the date 07/29/2016, and the time 4:46 p.m. Year, quarter, month, week, day, time of day, and so on These product, location, and time dimensions add a business perspective to the sales facts. The data analyst can now group the sales figures for a given product, in a given region, and at a given time. The star schema, through its facts and dimensions, can pro- vide the data in a format suited for data analysis. Also, it can do so without imposing the burden of additional and unnecessary data, such as order number, purchase order number, and status that commonly exists in operational databases. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 2016 Conceptually, the sales example’s multidimensional data model is best represented by a three-dimensional cube. Of course, this does not imply that there is a limit on the num- ber of dimensions you can associate to a fact table. There is no mathematical limit to the number of dimensions used. However, using a three-dimensional model makes it easy to visualize the problem. The three-dimensional cube illustrated in Figure 13.7 represents a view of sales with product, location, and time dimensions. Keep in mind that this cube is only a conceptual representation of multidimensional data; it does not show how the data is physically stored in a data warehouse. Whatever the underlying database technology, one of the main features of multidi- mensional analysis is its ability to focus on specific “slices” of the cube. For example, the product manager may be interested in examining the sales of a product while the store manager is interested in examining the sales made by a particular store. In multidimen- sional terms, the ability to focus on slices of the cube to perform a more detailed anal- ysis is known as slice and dice. Figure 13.8 illustrates the slice-and-dice concept; note that each cut across the cube yields a slice. Intersecting slices produce small cubes that constitute the “dice” part of the slice-and-dice operation. Chapter 13 Business Intelligence and Data Warehouses 613 FIGURE 13.7 THREE-DIMENSIONAL VIEW OF SALES Time Conceptual three-dimensional cube of sales by product, location, and time Sales facts are stored in the intersection of each product, time, and location dimension. FIGURE13.8 SLICE-AND-DICEVIEWOFSALES Time Sales manager ’s view of sales data Product manager’s view of sales data Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. slice and dice The ability to focus on slices of a data cube (drill down or roll up) to perform a more detailed analysis. Product Product Location Location 614 Part 4 Advanced Database Concepts To slice and dice, it must be possible to identify each slice of the cube. To do so, you use the values of each attribute in a given dimension. For example, to use the location dimension, you might need to define a STORE_ID attribute to focus on a particular store. Given the requirement for attribute values in a slice-and-dice environment, re-exam- ine Table 13.10. Note that each attribute adds perspective to the sales facts, thus setting the stage for finding new ways to search, classify, and possibly aggregate information. For example, the location dimension adds a geographic perspective of where the sales took place: in which region, state, city, store, and so on. All of the attributes are selected with the objective of providing decision support data to end users so they can study sales by each of the dimension’s attributes. Time is an especially important dimension; it provides a framework from which sales patterns can be analyzed and possibly predicted. Also, the time dimension plays an important role when the data analyst is interested in studying sales aggregates by quarter, month, week, and so on. Given the importance and universality of the time dimension from a data analysis perspective, many vendors have added automatic time dimension management features to their data-warehousing products. 13-5d Attribute Hierarchies Attributes within dimensions can be ordered in a well-defined attribute hierarchy. The attribute hierarchy provides a top-down data organization that is used for two main purposes: aggregation and drill-down/roll-up data analysis. For example, Figure 13.9 shows how the location dimension attributes can be organized in a hierarchy by region, state, city, and store. The attribute hierarchy provides the capability to perform drill-down and roll-up searches in a data warehouse. For example, suppose a data analyst looks at the answers to the following query: How does the 2015 month-to-date sales performance compare to the 2016 month-to-date sales performance? The data analyst spots a sharp sales decline for March 2016, and thus might decide to drill down inside the month of March to see how sales by regions compared to the previous year. By doing that, the analyst can deter- mine whether the low March sales were reflected in all regions or in only a particular region. This type of drill-down operation can even be extended until the data analyst identifies the store that is performing below the norm. The March sales scenario is possible because the attribute hierarchy allows the data warehouse and BI systems to have a defined path that identifies how data is to be rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE13.9 LOCATIONATTRIBUTEHIERARCHY The attribute hierarchy allows the end user to perform drill-down and roll-up searches. Region State City Store attribute hierarchy A top-down data organization that is used for two main purposes: aggregation and drill- down/roll-up data analysis. Copyright 2017 Cengage Lea Editorial review has deemed that decomposed and aggregated for drill-down and roll-up operations. It is not necessary for all attributes to be part of an attribute hierarchy; some attributes exist merely to provide narrative descriptions of the dimensions. However, keep in mind that the attributes from different dimensions can be grouped to form a hierarchy. For example, after you drill down from city to store, you might want to drill down using the product dimension so the manager can identify slow-selling products in the store. The product dimension can be based on the product group (dairy, meat, and so on) or the product brand (Brand A, Brand B, and so on). Figure 13.10 illustrates a scenario in which the data analyst studies sales facts using the product, time, and location dimensions. In this example, the product dimension is set to “All products,” meaning that the data analyst will see all products on the y-axis. The time dimension (x-axis) is set to “Quarter,” meaning that the data is aggregated by quar- ters—for example, total sales of products A, B, and C in Q1, Q2, Q3, and Q4. Finally, the location dimension is initially set to “Region,” thus ensuring that each cell contains the total regional sales for a given product in a given quarter. The simple scenario illustrated in Figure 13.10 provides the data analyst with three different information paths. On the product dimension (the y-axis), the data analyst can request to see all products, products grouped by type, or just one product. On the time dimension (the x-axis), the data analyst can request time–variant data at different levels of aggregation: year, quarter, month, or week. Each sales value initially shows the total sales, by region, of each product. When a GUI is used, clicking on the region cell enables Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 615 FIGURE13.10 ATTRIBUTEHIERARCHIESINMULTIDIMENSIONALANALYSIS Time dimension Product dimension Product A Product B Product C ........ ........ ........ Total of quarters Location hierarchy Region State City Store Year Quarter All products By product type One product Month Week Q1 Q2 Q3 Q4 Total of product 616 Part 4 Advanced Database Concepts the data analyst to drill down to see sales by states within the region. Clicking again on one of the state values yields the sales for each city in the state, and so forth. As the preceding examples illustrate, attribute hierarchies determine how the data in the data warehouse is extracted and presented. The attribute hierarchy information is stored in the DBMS’s data dictionary and is used by the BI tool to access the data ware- house properly. Once such access is ensured, query tools must be closely integrated with the data warehouse’s metadata, and they must support powerful analytical capabilities. 13-5e Star Schema Representation Facts and dimensions are normally represented by physical tables in the data warehouse database. The fact table is related to each dimension table in a many-to-one (M:1) relation- ship. In other words, many fact rows are related to each dimension row. Using the sales example, you can conclude that each product appears many times in the SALES fact table. Fact and dimension tables are related by foreign keys and are subject to the familiar primary key and foreign key constraints. The primary key on the “1” side, the dimension table, is stored as part of the primary key on the “many” side, the fact table. Because the fact table is related to many dimension tables, the primary key of the fact table is a com- posite primary key. Figure 13.11 illustrates the relationships among the sales fact table and the product, location, and time dimension tables. To show you how easily the star schema can be expanded, a customer dimension has been added to the mix. Adding the customer dimension merely required including the CUST_ID in the SALES fact table and adding the CUSTOMER table to the database. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE13.11 STARSCHEMAFORSALES LOCATION LOC_ID 1 LOC_DESCRIPTION REGION_ID LOC_STATE LOC_CITY 25 records CUSTOMER CUST_ID CUST_LNAME CUST_FNAME CUST_INITIAL CUST_DOB 125 records 1 TIME TIME_ID TIME_YEAR TIME_QUARTER TIME_MONTH TIME_DAY TIME_CLOCKTIME 365 records PRODUCT 1 M M SALES M TIME_ID LOC_ID CUST_ID PROD_ID M SALES_QUANTITY SALES_PRICE SALES_TOTAL 3,000,000 records Daily sales aggregates by store, customer, and product 1 PROD_ID PROD_DESCRIPTION PROD_TYPE_ID PROD_BRAND PROD_COLOR PROD_SIZE PROD_PACKAGE PROD_PRICE 3,000 records The composite primary key for the SALES fact table is composed of TIME_ID, LOC_ ID, CUST_ID, and PROD_ID. Each record in the SALES fact table is uniquely iden- tified by the combination of values for each of the fact table’s foreign keys. By default, the fact table’s primary key is always formed by combining the foreign keys pointing to the dimension tables to which they are related. In this case, each sales record represents each product sold to a specific customer, at a specific time, and in a specific location. In this schema, the TIME dimension table represents daily periods, so the SALES fact table represents daily sales aggregates by product and by customer. Because fact tables contain the actual values used in the decision support process, those values are repeated many times in the fact tables. Therefore, the fact tables are always the largest tables in the star schema. Because the dimension tables contain only nonrepetitive information, such as all unique salespersons and all unique products, the dimension tables are always smaller than the fact tables. In a typical star schema, each dimension record is related to thousands of fact records. For example, “widget” appears only once in the product dimension, but it has thousands of corresponding records in the SALES fact table. This characteristic of the star schema facilitates data retrieval because the data analyst usually looks at the facts through the dimension’s attributes. Therefore, a data warehouse DBMS that is optimized for decision support first searches the smaller dimension tables before accessing the larger fact tables. Data warehouses usually have many fact tables. Each fact table is designed to answer specific decision support questions. For example, suppose that you develop a new inter- est in orders while maintaining your original interest in sales. In that scenario, you should maintain an ORDERS fact table and a SALES fact table in the same data ware- house. If orders are considered to be an organization’s key interest, the ORDERS fact table should be the center of a star schema that might have vendor, product, and time dimensions. In that case, an interest in vendors yields a new vendor dimension, repre- sented by a new VENDOR table in the database. The product dimension is represented by the same product table used in the initial sales star schema. However, given the interest in orders as well as sales, the time dimension now requires special attention. If the orders department uses the same time periods as the sales department, time can be represented by the same time table. If different time periods are used, you must create another table, perhaps named ORDER_TIME, to represent the time periods used by the orders department. In Figure 13.12, the ORDERS star schema shares the product, vendor, and time dimensions. Multiple fact tables can also be created for performance and semantic reasons. The following section explains several performance-enhancing techniques that can be used within the star schema. 13-5f Performance-ImprovingTechniques for the Star Schema Creating a database that provides fast and accurate answers to data analysis queries is the prime objective of data warehouse design. Therefore, performance enhancement might target query speed through the facilitation of SQL code and through better semantic representation of business dimensions. The following four techniques are often used to optimize data warehouse design: • • • • Normalizing dimensional tables Maintaining multiple fact tables to represent different aggregation levels Denormalizing fact tables Partitioning and replicating tables Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 617 618 Part 4 Advanced Database Concepts FIGURE13.12 ORDERSSTARSCHEMA PRODUCT PROD_ID PROD_DESCRIPTION PROD_TYPE_ID PROD_BRAND PROD_COLOR PROD_SIZE PROD_PACKAGE PROD_PRICE 3,000 records 1 1 ORDER M TIME_ID PROD_ID M VEND_ID ORDER_QUANTITY ORDER_PRICE ORDER_AMOUNT 85,000 records Daily sales aggregates by product and vendor M 1 TIME TIME_ID TIME_YEAR TIME_QUARTER TIME_MONTH TIME_DAY TIME_CLOCKTIME 365 records VENDOR VEND_ID VEND_NAME VEND_AREACODE VEND_PHONE VEND_EMAIL 50 records snowflake schema A type of star schema in which dimension tables can have their own dimension tables. The snowflake schema is usually the result of normalizing dimension tables. Note Normalizing Dimensional Tables Dimensional tables are normalized to achieve semantic simplicity and facilitate end-user navigation through the dimensions. For example, if the location dimension table contains transitive dependencies among region, state, and city, you can revise those relationships to the 3NF (third normal form), as shown in Figure 13.13. (If necessary, review the normalization techniques in Chapter 6, Normalization of Database Tables.) The star schema shown in Figure 13.13 is known as a snowflake schema, which is a type of star schema in which the dimension tables can have their own dimension tables. The snowflake schema is usually the result of normal- izing dimension tables. By normalizing the dimension tables, you simplify the data-filtering operations related to the dimensions. In this example, the region, state, city, and location contain very few records compared to the SALES fact table. Only the location table is directly related to the SALES fact table. Although using the dimension tables shown in Figure 13.13 provides structural simplicity, there is a price to pay for that simplicity. For example, if you want to aggregate the data by region, you must use a four-table join, thus increasing the complexity of the SQL state- ments. The star schema in Figure 13.11 uses a LOCATION dimension table that greatly facil- itates data retrieval by eliminating multiple join operations. This is yet another example of the trade-offs that designers must consider. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 619 FIGURE13.13 NORMALIZEDDIMENSIONTABLES REGION REGION_ID REGION_NAME STATE STATE_ID STATE_NAME REGION_ID CITY CITY_ID CITY_NAME STATE_ID 1 1 1 SALES M TIME_ID LOC_ID CUST_ID PROD_ID SALES_QUANTITY SALES_PRICE SALES_TOTAL LOCATION LOC_ID LOC_DESCRIPTION M CITY_ID 1 M M Maintaining Multiple Fact Tables that Represent Different Aggregation Levels You can also speed up query operations by creating and maintaining multiple fact tables related to each level of aggregation (region, state, and city) in the location dimension. These aggregate tables are precomputed at the data-loading phase rather than at run time. The purpose of this technique is to save processor cycles at run time, thereby speeding up data analysis. An end-user query tool optimized for decision analysis then properly accesses the summarized fact tables instead of computing the values by accessing a fact table at a lower level of detail. This technique is illustrated in Figure 13.14, which adds aggregate fact tables for region, state, and city to the initial sales example. The data warehouse designer must identify which levels of aggregation to precompute and store in the database. These multiple aggregate fact tables are updated during each load cycle in batch mode. Also, because the objective is to minimize access according to the expected frequency of use and to minimize the processing time required to calculate a given aggregation level at run time, the data warehouse designer must select which aggregation fact tables to create. Denormalizing Fact Tables Denormalizing fact tables improves data access perfor- mance and saves data storage space. The latter objective, however, is becoming less of an issue. Data storage costs decrease almost daily, and DBMS limitations on database and table size, record size, and the maximum number of records in a single table have far more negative effects than raw storage space costs. Denormalization improves performance by using a single record to store data that normally takes many records. For example, to compute the total sales for all products in all regions, you might have to access the region sales aggregates and summarize all of the records in this table. If you have 300,000 product sales, you could be summarizing at least 300,000 rows. Although this might not be a taxing operation for a DBMS, a comparison Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 620 Part 4 Advanced Database Concepts FIGURE13.14 MULTIPLEFACTTABLES SALES_REGION TIME_ID M 1 REGION_ID CUST_ID PROD_ID SLSREG_QUANTITY 1 SLSREG_PRICE SLSREG_AMOUNT REGION 1 REGION_ID REGION_NAME M SALES_CITY TIME_ID CITY_ID CUST_ID PROD_ID SLSCIT_QUANTITY SLSCIT_PRICE SLSCIT_AMOUNT SALES_LOCATION TIME_ID SALES_STATE TIME_ID STATE_ID CUST_ID PROD_ID SLSSTA_QUANTITY SLSSTA_PRICE SLSSTA_AMOUNT STATE STATE_ID STATE_NAME REGION_ID CITY 1 M 1 M 1 CITY_ID CITY_NAME M CUST_ID M LOC_ID M LOC_ID SLSLOC_AMOUNT LOC_DESCRIPTION CITY_ID STATE_ID LOCATION 1 PROD_ID SLSLOC_QUANTITY SLSLOC_PRICE partitioning The process of splitting a table into subsets of rows or columns. replication The process of creating and managing duplicate versions of a database. Replication is used to place copies in different locations and to improve access time and fault tolerance. of 10 years’ worth of previous sales begins to bog down the system. In such cases, it is useful to have special aggregate tables that are denormalized. For example, a YEAR_ TOTALS table might contain the following fields: YEAR_ID, MONTH_1, MONTH_2 ... MONTH_12, and each year’s total. Such tables can easily be used to serve as a basis for year-to-year comparisons at the top month level, the quarter level, or the year level. Here again, design criteria such as frequency of use and performance requirements are evaluated against the possible overload placed on the DBMS to manage the denormal- ized relations. PartitioningandReplicatingTables Becausetablepartitioningandreplicationwere covered in detail in Chapter 12, Distributed Database Management Systems, those tech- niques are discussed here only as they specifically relate to the data warehouse. Table partitioning and replication are particularly important when a BI system is imple- mented in dispersed geographic areas. Partitioning splits a table into subsets of rows or columns and places the subsets close to the client computer to improve data access time. Replication makes a copy of a table or partition and places it in a different location, also to improve access time. No matter which performance-enhancement scheme is used, time is the most com- mon dimension used in business data analysis. Therefore, it is very common to have one fact table for each level of aggregation defined within the time dimension. In the sales example, you might have five aggregate sales fact tables: daily, weekly, monthly, Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. quarterly, and yearly. These fact tables must have an implicit or explicit periodicity defined. Periodicity, which is usually expressed as current year only, previous years, or all years, provides information about the time span of the data stored in the table. At the end of each year, daily sales for the current year are moved to another table that contains previous years’ daily sales only. This table actually contains all sales records from the beginning of operations, with the exception of the current year. The data in the current year and previous years’ tables thus represents the complete sales history of the company. The previous years’ sales table can be replicated at several locations to avoid having to remotely access the historic sales data, which can cause a slow response time. The possible size of this table is enough to intimidate all but the bravest of query optimizers. Here is one case in which denormalization would be of value! In this section you learned how the star schema design technique allows you to model data optimized for business decision making. A BI system uses all the previously men- tioned components to provide decision support to all organizational users. In the next section you will learn about a widely used BI style known as online analytical processing. 13-6 Online Analytical Processing Online analytical processing (OLAP) is a BI style whose systems share three main characteristics: • Multidimensional data analysis techniques • Advanced database support • Easy-to-use end-user interfaces This section examines each characteristic. 13-6a Multidimensional Data Analysis Techniques The most distinctive characteristic of modern OLAP tools is their capacity for multidi- mensional analysis, in which data is processed and viewed as part of a multidimensional structure. This type of data analysis is particularly attractive to business decision makers because they tend to view business data as being related to other business data. To better understand this view, you can examine how a business data analyst might investigate sales figures. In this case, the analyst is probably interested in the sales figures as they relate to other business variables such as customers and time. In other words, cus- tomers and time are viewed as different dimensions of sales. Figure 13.15 illustrates how the operational (one-dimensional) view differs from the multidimensional view of sales. Note in Figure 13.15 that the operational (tabular) view of sales data is not well suited to decision support because the relationship between INVOICE and LINE does not pro- vide a business perspective of the sales data. On the other hand, the end user’s view of sales data from a business perspective is more closely represented by the multidimen- sional view of sales than by the tabular view of separate tables. Note also that the multidi- mensional view allows end users to consolidate or aggregate data at different levels: total sales figures by customers and by date. Finally, the multidimensional view of data allows a business data analyst to easily switch business perspectives (dimensions) from sales by customer to sales by division, by region, and so on. Multidimensional data analysis techniques are augmented by the following functions: • Advanced data presentation functions. These functions include 3D graphics, pivot tables, crosstabs, data rotation, and three-dimensional cubes. Such tools are compatible with desktop spreadsheets, statistical packages, and query and report packages. periodicity Information about the time span of data stored in a table, usually expressed as current year only, previous years, or all years. online analytical processing (OLAP) Decision support system (DSS) tools that use multidimensional data analysis techniques. OLAP creates an advanced data analysis environment that supports decision making, business modeling, and operations research. Chapter 13 Business Intelligence and Data Warehouses 621 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 622 Part 4 Advanced Database Concepts FIGURE13.15 OPERATIONALVS.MULTIDIMENSIONALVIEWOFSALES • Advanced data aggregation, consolidation, and classification functions. These allow the data analyst to create multiple data aggregation levels, slice and dice data (see Section 13-5c), and drill down and roll up data across different dimensions and aggre- gation levels. For example, aggregating data by week, month, quarter, and year allows the data analyst to drill down and roll up across time dimensions. • Advanced computational functions. These include business-oriented variables such as market share, period comparisons, sales margins, product margins, and percentage changes; financial and accounting ratios, including profitability, overhead, cost allo- cations, and returns; and statistical and forecasting functions. These functions are provided automatically, so the end user does not need to redefine the components each time they are accessed. • Advanced data-modeling functions. These provide support for what-if scenarios, variable assessment, contributions to outcome, linear programming, and predictive Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. modeling tools. Predictive modeling allows the system to build advanced statisti- cal models to predict future values (business outcomes) with a high percentage of accuracy. 13-6b Advanced Database Support To deliver efficient decision support, OLAP tools must have the following advanced data access features: • Access to many different kinds of DBMSs, flat files, and internal and external data sources • Access to aggregated data warehouse data as well as to the detail data found in operational databases • Advanced data navigation features such as drill-down and roll-up • Rapid and consistent query response times • The ability to map end-user requests, expressed in either business or model terms, to the appropriate data source and then to the proper data access language (usually SQL). The query code must be optimized to match the data source, regardless of whether the source is operational or data warehouse data. • Support for very large databases. As explained earlier, the data warehouse could easily and quickly grow to multiple terabytes in size. To provide a seamless interface, OLAP tools map the data elements from the data warehouse and the operational database to their own data dictionaries. This metadata is used to translate end-user data analysis requests into the proper (optimized) query codes, which are then directed to the appropriate data sources. 13-6c Easy-to-Use End-User Interfaces The end-user analytical interface is one of the most critical OLAP components. When properly implemented, an analytical interface permits the user to navigate the data in a way that simplifies and accelerates decision making or data analysis. Advanced OLAP features become more useful when access to them is kept simple. OLAP tool vendors learned this lesson early and have equipped their sophisticated data extraction and analysis tools with easy-to-use graphical interfaces. Many of the interface features are “borrowed” from previous generations of data analysis tools that are already familiar to end users. Because many analysis and presentation functions are common to desktop spreadsheet packages, most OLAP vendors have closely integrated their systems with spreadsheets such as Microsoft Excel. Using the features available in graphical end-user interfaces, OLAP sim- ply becomes another option within the spreadsheet menu bar, as shown in Figure 13.16. This seamless integration is an advantage for OLAP systems and spreadsheet vendors because end users gain access to advanced data analysis features by using familiar pro- grams and interfaces. Therefore, additional training and development costs are minimized. 13-6d OLAP Architecture The OLAP architecture is designed to meet ease-of-use requirements while keeping the system flexible. An OLAP system has three main architectural components: • Graphical user interface (GUI) • Analytical processing logic • Data-processing logic Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 623 624 Part 4 Advanced Database Concepts FIGURE13.16 INTEGRATIONOFOLAPWITHASPREADSHEETPROGRAM Source: Microsoft LLC (Excel screenshot); Oracle OCBC (Oracle windows) These three components can exist on the same computer or be distributed among several computers. Figure 13.17 illustrates OLAP’s architectural components. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE13.17 OLAPARCHITECTURE External Operational Analytical processing logic Data-processing logic Data Warehouse Excel plug-in data data Alternate direct access of operational and data warehouse data ETL Extraction, Access OLAP OLAP “engine” provides a front end to the data warehouse. Advanced reporting Spreadsheet reports plug-in OLAP GUI Multiple interfaces and application plug-ins reports Dashboards Mobile Bl transformation, and loading © Antun Hirsman/Shutterstock.com As Figure 13.17 illustrates, OLAP systems are designed to use both operational and data warehouse data. The figure shows the OLAP system components on a single com- puter, but this single-user scenario is only one of many. In fact, one problem with the installation shown here is that each data analyst must have a powerful computer to store the OLAP system and perform all data processing locally. A more common and practical architecture is one in which the OLAP GUI runs on client workstations while the OLAP data-processing logic (or OLAP “server”) runs on a shared server computer. The OLAP analytical processing logic could be located on a client workstation, the OLAP server, or be split between the two sides. In any case, the OLAP server component acts as an intermediary between the OLAP GUI and the data warehouse. This middle layer accepts and handles the data-processing requests gen- erated by the many end-user OLAP workstations. This flexible architecture allows for many different OLAP configurations. Figure 13.18 illustrates an OLAP server with local miniature data marts. As illustrated in Figure 13.18, the OLAP system could merge the data warehouse and data mart approaches by storing extracts of the data warehouse at end-user workstations. The objective is to increase the speed of data access and data visualization (the graphic representations of data trends and characteristics). The logic behind this approach is the assumption that most end users usually work with fairly small, stable data warehouse subsets. For example, a sales analyst is most likely to work with sales data, whereas a customer representative is likely to work with customer data. Whatever the arrangement of the OLAP components, one thing is certain: multi- dimensional data must be used. But how is multidimensional data best stored and Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 625 FIGURE13.18 OLAPSERVERWITHLOCALMINIDATAMARTS OLAP “server” Analytical processing logic Data processing logic Multiple OLAP clients accessing the OLAP server Sales Dept. OLAP GUI Marketing Dept. OLAP GUI Manufacturing Dept. OLAP GUI Procurement Dept. OLAP GUI Local data marts Operational data Data Warehouse Data extracted from the data warehouse to local data marts, which provides faster processing s m g r o n e C e c t t o i i u M o k u d s n P s o t r d r r e V a n 626 Part 4 Advanced Database Concepts managed? OLAP proponents are sharply divided. Some favor the use of relational data- bases to store multidimensional data; others argue that specialized multidimensional databases are superior. The basic characteristics of each approach are examined next. 13-6e Relational OLAP Relational online analytical processing (ROLAP) provides OLAP functionality by using relational databases and familiar relational query tools to store and analyze multidi- mensional data. This approach builds on existing relational technologies and represents a natural extension to companies that already use relational database management systems within their organizations. ROLAP adds the following extensions to traditional RDBMS technology: • Multidimensional data schema support within the RDBMS • Data access language and query performance optimized for multidimensional data • Support for very large databases (VLDBs) Multidimensional Data Schema Support within the RDBMS Relational technol- ogy uses normalized tables to store data. The reliance on normalization as the design methodology for relational databases is seen as a stumbling block to its use in OLAP systems. Normalization divides business entities into smaller pieces to produce the nor- malized tables. For example, sales data components might be stored in four or five dif- ferent tables. The reason for using normalized tables is to reduce redundancies, thereby eliminating data anomalies, and to facilitate data updates. Unfortunately, for decision support purposes, it is easier to understand data when it is seen with respect to other data. (See the example in Figure 13.15.) Given that view of the data environment, this text has emphasized that decision support data tends to be non-normalized, duplicated, and preaggregated. Those characteristics seem to preclude the use of standard relational design techniques and RDBMSs as the foundation for multidimensional data. Fortunately for companies heavily invested in relational technology, ROLAP uses a special design technique that enables RDBMS technology to support multidimensional data representations. This special design technique is known as a star schema, which is covered in detail in Section 13-5. The star schema is designed to optimize data query operations rather than data update operations. Naturally, changing the data design foundation means that the tools used to access such data will have to change. End users who are familiar with traditional relational query tools will discover that those tools do not work efficiently with the star schema. However, ROLAP saves the day by adding support for the star schema when familiar query tools are used. ROLAP provides advanced data analysis functions and improves query optimization and data visualization methods. Data Access Language and Query Performance Optimized for Multidimensional Data Another criticism of relational databases is that SQL is not suited for performing advanced data analysis. Most decision support data requests require the use of multi- ple-pass SQL queries or multiple nested SQL statements. To answer this criticism, ROLAP extends SQL so that it can differentiate between access requirements for data warehouse data (based on the star schema) and operational data (normalized tables). A ROLAP system therefore can generate the SQL code required to access the star schema data. Query performance is also improved because the query optimizer is modified to iden- tify the SQL code’s intended query targets. For example, if the query target is the data warehouse, the optimizer passes the requests to the data warehouse. However, if the end user performs drill-down queries against operational data, the query optimizer identifies rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. relational online analytical processing (ROLAP) Analytical processing functions that use relational databases and familiar relational query tools to store and analyze multidimensional data. Copyright 2017 Cengage Lea Editorial review has deemed that that operation and properly optimizes the SQL requests before passing them to the oper- ational DBMS. Another source of improved query performance is the use of advanced indexing tech- niques such as bitmapped indexes within relational databases. As the name suggests, a bitmapped index is based on 0 and 1 bits to represent a given condition. For example, if the REGION attribute in Figure 13.4 has only four outcomes—North, South, East, and West—those outcomes may be represented as shown in Table 13.11. Only the first 10 rows from Figure 13.4 are represented in the table. The “1” represents “bit on,” and the “0” represents “bit off.” For example, to represent a row with a REGION attribute = “East,” only the “East” bit would be on. Note that each row must be represented in the index table. Note that the index in Table 13.11 takes a minimal amount of space. Therefore, bit- mapped indexes are more efficient at handling large amounts of data than the indexes typically found in many relational databases. However, keep in mind that bitmapped indexes are primarily used when the number of possible values for an attribute is fairly small. For example, REGION has only four outcomes in this example. Marital status— married, single, widowed, or divorced—would be another good bitmapped index candi- date, as would gender—M or F. TABLE 13.11 BITMAP REPRESENTATION OF REGION VALUES NORTH SOUTH EAST WEST Chapter 13 Business Intelligence and Data Warehouses 627 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 Support for Very Large Databases Recall that support for VLDBs is a requirement for decision support databases. Therefore, when the relational database is used in a decision support role, it also must be able to store very large amounts of data. Both the storage capability and the process of loading data into the database are crucial. Therefore, the RDBMS must have the proper tools to import, integrate, and populate the data ware- house with data. Decision support data is normally loaded in bulk (batch) mode from the operational data. However, batch operations require that both the source and the destination databases be reserved (locked). The speed of the data-loading operations is important, especially when you realize that most operational systems run 24 hours a day, 7 days a week. Therefore, the window of opportunity for maintenance and batch loading is open only briefly, typically during slack periods. Clearly, ROLAP is a logical choice for companies that already use relational databases for their operational data. Given the size of the relational database market, it is hardly surprising that most current RDBMS vendors have extended their products to support data warehouses and OLAP capabilities. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 628 Part 4 Advanced Database Concepts 13-6f Multidimensional OLAP Multidimensional online analytical processing (MOLAP) extends OLAP functional- ity to multidimensional database management systems (MDBMSs). An MDBMS uses proprietary techniques to store data in matrix-like n-dimensional arrays. MOLAP’s premise is that multidimensional databases are best suited to manage, store, and analyze multidimensional data. Most of the proprietary techniques used in MDBMSs are derived from engineering fields such as computer-aided design/computer-aided manufacturing (CAD/CAM) and geographic information systems (GIS). MOLAP tools store data using multidimensional arrays, row stores, or column stores. (If necessary, review the NoSQL data model in Chapter 2, Data Models.) Conceptually, MDBMS end users visualize the stored data as a three-dimensional cube known as a data cube. The location of each data value in the data cube is a func- tion of the x-, y-, and z-axes in a three-dimensional space. The three axes represent the dimensions of the data value. The data cubes can grow to n number of dimensions, thus becoming hypercubes. Data cubes are created by extracting data from the operational databases or from the data warehouse. One important characteristic of data cubes is that they are static; that is, they are not subject to change and must be created before they can be used. Data cubes cannot be created by ad hoc queries. Instead, you query precreated cubes with defined axes; for example, a cube for sales will have the product, location, and time dimensions, and you can query only those dimensions. Therefore, the data cube creation process is critical and requires in-depth front-end design work. This design work may be well justified because MOLAP databases are known to be much faster than their ROLAP counterparts, especially when dealing with large data sets. To speed data access, data cubes are normally held in memory in the cube cache. (A data cube is only a window to a predefined subset of data in the database. A data cube and a database are not the same thing.) Because MOLAP also benefits from a client/server infrastructure, the cube cache can be located at the MOLAP server, the MOLAP client, or both. Because the data cube is predefined with a set number of dimensions, the addition of a new dimension requires that the entire data cube be re-created, which is time-con- suming. Therefore, when data cubes are created too often, the MDBMS loses some of its speed advantage over the relational database. In addition, the MDBMS uses proprietary data storage techniques that in turn require proprietary data access methods using a multidimensional query language. Multidimensional data analysis is also affected by how the database system handles sparsity. Sparsity measures the density of the data held in the data cube; it is computed by dividing the total number of actual values in the cube by its total number of cells. Because the data cube’s dimensions are predefined, not all cells are populated. In other words, some cells are empty. Returning to the sales example, many products might not be sold during a given time period in a given location. In fact, you will often find that less than 50 percent of the data cube’s cells are populated. In any case, multidimensional databases must handle sparsity effectively to reduce processing overhead and resource requirements. 13-6g Relational versus Multidimensional OLAP Table 13.12 summarizes some pros and cons of ROLAP and MOLAP. Keep in mind that the selection of one or the other often depends on the evaluator’s vantage point. For example, a proper evaluation of OLAP must include price, supported hardware plat- forms, compatibility with the existing DBMS, programming requirements, performance, and availability of administrative tools. The summary in Table 13.12 provides a useful starting point for comparison. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. multidimensional online analytical processing (MOLAP) An extension of online analytical processing to multidimensional database management systems. multidimensional database management system (MDBMS) A database management system that uses proprietary techniques to store data in matrixlike arrays of n dimensions known as cubes. data cube The multidimensional data structure used to store and manipulate data in a multidimensional DBMS. The location of each data value in the data cube is based on its x-, y-, and z-axes. Data cubes are static, meaning they must be created before they are used, so they cannot be created by an ad hoc query. cube cache In multidimensional OLAP, the shared, reserved memory area where data cubes are held. Using the cube cache assists in speeding up data access. sparsity In multidimensional data analysis, a measurement of the data density held in the data cube. Copyright 2017 Cengage Lea Editorial review has deemed that TABLE 13.12 RELATIONAL VS. MULTIDIMENSIONAL OLAP CHARACTERISTIC ROLAP MOLAP Chapter 13 Business Intelligence and Data Warehouses 629 Schema Uses star schema Additional dimensions can be added dynamically Uses data cubes Multidimensional arrays, row stores, column stores Additional dimensions require re-creation of the data cube Database size Medium to large Large Architecture Client/server Standards-based Client/server Open or proprietary, depending on vendor Access Supports ad hoc requests Unlimited dimensions Limited to predefined dimensions Proprietary access languages Speed Good with small data sets; average for medium-sized to large data sets Faster for large data sets with predefined dimensions ROLAP and MOLAP vendors are working to integrate their respective solutions within a unified decision support framework. Many OLAP products can handle tabular and multidimensional data with the same ease. For example, if you use Excel OLAP functionality, as shown earlier in Figure 13.16, you can access relational OLAP data in a SQL server as well as cube (multidimensional) data in the local computer. In the mean- time, relational databases have successfully extended SQL to support many OLAP tools. 13-7 SQL Extensions for OLAP The proliferation of OLAP tools has fostered the development of SQL extensions to sup- port multidimensional data analysis. Most SQL innovations are the result of vendor- centric product enhancements. However, many of the innovations have made their way into standard SQL. This section introduces some of the new SQL extensions that have been created to support OLAP-type data manipulations. The SaleCo snowflake schema shown in Figure 13.19 demonstrates the use of the SQL extensions. Note that this snowflake schema has a central DWSALESFACT fact Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. FIGURE13.19 SALECOSNOWFLAKESCHEMA 630 Part 4 Advanced Database Concepts Online Content The script files used to populate the database and run the SQL com- mands are available at www.cengagebrain.com. Note table and three dimension tables: DWCUSTOMER, DWPRODUCT, and DWTIME. The central fact table represents daily sales by product and customer. However, as you examine the schema shown in Figure 13.19, you will see that the DWCUSTOMER and DWPRODUCT dimension tables have their own dimension tables: DWREGION and DWVENDOR. Keep in mind that a database is at the core of all data warehouses. Therefore, all SQL commands (such as CREATE, INSERT, UPDATE, DELETE, and SELECT) will work in the data warehouse as expected. However, most queries you run in a data warehouse tend to include a lot of data groupings and aggregations over multiple columns. Therefore, this section introduces two extensions to the GROUP BY clause that are particularly useful: ROLLUP and CUBE. In addition, you will learn about using materialized views to store preaggregated rows in the database. This section uses the Oracle RDBMS to demonstrate the use of SQL extensions to support OLAP functionality. If you use a different DBMS, consult the documentation to verify whether the vendor supports similar functionality and what the proper syntax is for your DBMS. Note 13-7a The ROLLUP Extension The ROLLUP extension is used with the GROUP BY clause to generate aggregates by different dimensions. As you know, the GROUP BY clause will generate only one aggre- gate for each new value combination of attributes listed in the GROUP BY clause. The ROLLUP extension goes one step further; it enables you to get a subtotal for each column listed except for the last one, which gets a grand total instead. The syntax of the GROUP BY ROLLUP command sequence is as follows: SELECT column1 [, column2, ...], aggregate_function(expression) FROM table1 [, table2, ...] [WHERE condition] GROUP BY ROLLUP (column1 [, column2, ...]) [HAVING condition] [ORDER BY column1 [, column2, ...]] MS SQL Server and MySQL both support ROLLUP functionality. Other than the GROUP BY clause, the same syntax used for working with aggregate functions in these DBMSs applies. The GROUP BY clause is written: GROUP BY column1 [, column2, ...] WITH ROLLUP In MySQL, if the ROLLUP option is specified, then an ORDER BY clause is not allowed. Access does not support the ROLLUP extension. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. The order of the column list within GROUP BY ROLLUP is very important. The last column in the list will generate a grand total, and all other columns will generate sub- totals. For example, Figure 13.20 shows the use of the ROLLUP extension to generate subtotals by vendor and product. Figure 13.20 shows the subtotals by vendor code and a grand total for all product codes. Contrast that with the normal GROUP BY clause that generates only the subtotals for each vendor and product combination. The ROLLUP extension is particularly useful when you want to obtain multiple nested subtotals for a dimension hierarchy. For exam- ple, within a location hierarchy, you can use ROLLUP to generate subtotals by region, state, city, and store. 13-7b The CUBE Extension The CUBE extension is also used with the GROUP BY clause to generate aggregates by the listed columns, including the last one. The CUBE extension enables you to get a subtotal for each column listed in the expression, in addition to a grand total for the last column listed. The syntax of the GROUP BY CUBE command sequence is as follows: SELECT column1 [, column2, ...], aggregate_function(expression) FROM table1 [, table2, ...] [WHERE condition] GROUP BY CUBE (column1 [, column2, ...]) [HAVING condition] [ORDER BY column1 [, column2, ...]] Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 631 FIGURE13.20 ROLLUPEXTENSION Subtotals by V_CODE Grand total for all P_CODE values 632 Part 4 Note Advanced Database Concepts MS SQL Server supports CUBE functionality, too. Other than the GROUP BY clause, the same syntax used for working with aggregate functions applies. The GROUP BY clause is written similarly to the ROLLUP extension: GROUP BY column1 [, column2, ...] WITH CUBE MySQL and Access do not support the CUBE extension. For example, Figure 13.21 shows the use of the CUBE extension to compute the sales subtotals by month and by product, as well as a grand total. FIGURE13.21 CUBEEXTENSION Subtotals by month Subtotals by product Grand total for all products and months In Figure 13.21, the CUBE extension also generates subtotals for each combination of month and product. The CUBE extension is particularly useful when you want to compute all possible subtotals within groupings based on multiple dimensions. Cross- tabulations are especially good candidates for application of the CUBE extension. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 13-7c MaterializedViews The data warehouse normally contains fact tables that store specific measurements of interest to an organization. Such measurements are organized by different dimen- sions. The vast majority of OLAP business analysis of everyday activity is based on data comparisons that are aggregated at different levels, such as totals by vendor, by product, and by store. Because businesses normally use a predefined set of summaries for benchmark- ing, it is reasonable to predefine such summaries for future use by creating summary fact tables. (See Section 13-5f for a discussion of additional performance-improving techniques.) However, creating multiple summary fact tables that use GROUP BY queries with multiple table joins could become resource-intensive. In addition, data warehouses must be able to maintain up-to-date summarized data at all times. So what happens with the summary fact tables after new sales data has been added to the base fact tables? Under normal circumstances, the summary fact tables are re-created. This operation requires that the SQL code be run again to re-create all summary rows, even when only a few rows need updating. Clearly, this is a time- consuming process. To save query processing time, most database vendors have implemented additional functions to manage aggregate summaries more efficiently. This new functionality resembles the standard SQL views for which the SQL code is predefined in the data- base. However, the added difference is that the views also store the preaggregated rows, something like a summary table. For example, Microsoft SQL Server provides indexed views, while Oracle provides materialized views. This section explains the use of materialized views. A materialized view is a dynamic table that not only contains the SQL query command to generate the rows, it stores the actual rows. The materialized view is cre- ated the first time the query is run, and the summary rows are stored in the table. The materialized view rows are automatically updated when the base tables are updated. That way, the data warehouse administrator will create the view but will not have to worry about updating the view. The use of materialized views is totally transparent to the end user. The OLAP end user can create OLAP queries using the standard fact tables, and the DBMS query optimization feature will automatically use the materialized views if they provide better performance. The basic syntax for the materialized view is: CREATE MATERIALIZED VIEW view_name BUILD {IMMEDIATE | DEFERRED} REFRESH {[FAST | COMPLETE | FORCE]} ON COMMIT [ENABLE QUERY REWRITE] AS select_query; The BUILD clause indicates when the materialized view rows are actually popu- lated. IMMEDIATE indicates that the materialized view rows are populated right after the command is entered. DEFERRED indicates that the materialized view rows will be populated later. Until then, the materialized view is in an unusable state. The DBMS provides a special routine that an administrator runs to populate materialized views. The REFRESH clause lets you indicate when and how to update the materialized view when new rows are added to the base tables. FAST indicates that whenever a change is made in the base tables, the materialized view updates only the affected rows. COMPLETE indicates that a complete update will be made for all rows in the materialized view when you rerun the SELECT query on which the view is based. FORCE indicates materialized view A dynamic table that not only contains the SQL query command to generate rows but stores the actual rows. The materialized view is created the first time the query is run and the summary rows are stored in the table. The materialized view rows are automatically updated when the base tables are updated. Chapter 13 Business Intelligence and Data Warehouses 633 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 634 Part 4 Advanced Database Concepts that the DBMS will first try to do a FAST update; otherwise, it will do a COMPLETE update. The ON COMMIT clause indicates that the updates to the materialized view will take place as part of the commit process of the underlying DML statement—that is, as part of the commitment of the DML transaction that updated the base tables. The ENABLE QUERY REWRITE option allows the DBMS to use the materialized views in query optimization. To create materialized views, you must have specified privileges and you must com- plete specified prerequisite steps. As always, you must consult the DBMS documentation for the latest updates. In the case of Oracle, you must create materialized view logs on the base tables of the materialized view. Figure 13.22 shows the steps required to create the SALES_MONTH_MV materialized view in the Oracle RDBMS. FIGURE13.22 CREATINGAMATERIALIZEDVIEW Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. The materialized view in Figure 13.22 computes the monthly total units sold and the total sales aggregated by product. The SALES_MONTH_MV materialized view is configured to automatically update after each change in the base tables. The last row of SALES_MONTH_MV indicates that during October, three units of product “WR3/ TT3” were sold for a total of $359.85. Figure 13.23 shows the effects of updating the DWDAYSALESFACT base table. Figure 13.23 shows how the materialized view was automatically updated after the insertion of a new row in the DWDAYSALESFACT table. The last row of SALES_ MONTH_MV now shows that in October, four units of product “WR3/TT3” were sold for a total of $466.84. Although all of the examples in this section focus on SQL extensions to support OLAP reporting in an Oracle DBMS, you have seen just a small fraction of the many business intelligence features currently provided by most DBMS vendors. For example, most vendors provide rich graphical user interfaces to manipulate, analyze, and present the data in multiple formats. Figure 13.24 shows two sample screens, one for Oracle and one for Microsoft SQL Server. Chapter 13 Business Intelligence and Data Warehouses 635 FIGURE13.23 REFRESHINGAMATERIALIZEDVIEW Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 636 Part 4 Advanced Database Concepts FIGURE13.24 SAMPLEOLAPAPPLICATIONS Oracle DBMS OLAP Services Microsoft SQL Server Analysis Services Summary • Business intelligence (BI) is a term for a comprehensive, cohesive, and integrated set of applications used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support business decision making. • Decision support systems (DSSs) refer to an arrangement of computerized tools used to assist managerial decision making within a business. DSSs were the original precursor of current-generation BI systems. • Operational data is not well suited for decision support. From the end user’s point of view, decision support data differs from operational data in three main areas: time span, granularity, and dimensionality. • The data warehouse is an integrated, subject-oriented, time-variant, nonvolatile collection of data that provides support for decision making. The data warehouse is usually a read-only database optimized for data analysis and query processing. A data mart is a small, single-subject data warehouse subset that provides decision support to a small group of people. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. • The star schema is a data-modeling technique used to map multidimensional deci- sion support data into a relational database for advanced data analysis. The basic star schema has four components: facts, dimensions, attributes, and attribute hierarchies. Facts are numeric measurements or values that represent a specific business aspect or activity. Dimensions are general qualifying categories that provide additional per- spectives to facts. Conceptually, the multidimensional data model is best represented by a three-dimensional cube. Attributes can be ordered in well-defined hierarchies, which provide a top-down organization that is used for two main purposes: to permit aggregation and provide drill-down and roll-up data analysis. • Online analytical processing (OLAP) refers to an advanced data analysis environment that supports decision making, business modeling, and operations research. • SQL has been enhanced with extensions that support OLAP-type processing and data generation. Chapter 13 Business Intelligence and Data Warehouses 637 1. What is business intelligence? Give some recent examples of BI usage, using the Internet for assistance. What BI benefits have companies found? 2. Describe the BI framework. Illustrate the evolution of BI. 3. What are decision support systems, and what role do they play in the business environment? 4. Explain how the main components of the BI architecture interact to form a system. Describe the evolution of BI information dissemination formats. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Key Terms attribute hierarchy business intelligence (BI) cube cache dashboard data cube data mart data visualization data warehouse decision support system (DSS) dimension tables dimensions drill down extraction, transformation, and loading (ETL) fact table facts governance key performance indicator (KPI) master data management (MDM) materialized view metrics multidimensional database management system (MDBMS) multidimensional online analytical processing (MOLAP) online analytical processing (OLAP) partitioning periodicity portal relational online analytical processing (ROLAP) replication roll up slice and dice snowflake schema sparsity star schema very large database (VLDB) Online Content Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com. Review Questions 638 Part 4 Advanced Database Concepts 5. What are the most relevant differences between operational data and decision sup- port data? 6. What is a data warehouse, and what are its main characteristics? How does it differ from a data mart? 7. Give three examples of likely problems when operational data is integrated into the data warehouse. Use the following scenario to answer Questions 8–14. While working as a database analyst for a national sales organization, you are asked to be part of its data warehouse project team. 8. Prepare a high-level summary of the main requirements for evaluating DBMS prod- ucts for data warehousing. 9. Your data warehousing project group is debating whether to create a prototype of a data warehouse before its implementation. The project group members are espe- cially concerned about the need to acquire some data warehousing skills before implementing the enterprise-wide data warehouse. What would you recommend? Explain your recommendations. 10. Supposethatyouaresellingthedatawarehouseideatoyourusers.Howwouldyou define multidimensional data analysis for them? How would you explain its advan- tages to them? 11. The data warehousing project group has invited you to provide an OLAP overview. The group’s members are particularly concerned about the OLAP client/server archi- tecture requirements and how OLAP will fit the existing environment. Your job is to explain the main OLAP client/server components and architectures. 12. One of your vendors recommends using an MDBMS. How would you explain this recommendation to your project leader? 13. The project group is ready to make a final decision, choosing between ROLAP and MOLAP. What should be the basis for this decision? Why? 14. The data warehouse project is in the design phase. Explain to your fellow designers how you would use a star schema in the design. 15. Briefly discuss the OLAP architectural styles with and without data marts. 16. What is OLAP, and what are its main characteristics? 17. ExplainROLAP,andlistthereasonsyouwouldrecommenditsuseintherelational database environment. 18. Explain the use of facts, dimensions, and attributes in the star schema. 19. Explainmultidimensionalcubes,anddescribehowtheslice-and-dicetechniquefits into this model. 20. Inthestarschemacontext,whatareattributehierarchiesandaggregationlevels,and what is their purpose? 21. Discuss the most common performance improvement techniques used in star schemas. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 1. The university computer lab’s director keeps track of lab usage, as measured by the number of students using the lab. This function is important for budgeting purposes. The computer lab director assigns you the task of developing a data warehouse to keep track of the lab usage statistics. The main requirements for this database are to: • Show the total number of users by different time periods. • Show usage numbers by time period, by major, and by student classification. • Compare usage for different majors and different semesters. Use the Ch13_P1.mdb database, which includes the following tables: • USELOG contains the student lab access data. • STUDENT is a dimension table that contains student data. Given the three preceding requirements, and using the Ch13_P1.mdb data, complete the following problems: Online Content The databases used for the following problems are avail- able at www.cengagebrain. com (see the list of data files at the beginning of the chap- ter). The data for Problem 2 is stored in Microsoft Excel for- mat at www.cengagebrain. com. The spreadsheet file- name is Ch13_P2.xls. Chapter 13 Business Intelligence and Data Warehouses 639 a. Define the main facts to be analyzed. (Hint: These facts become the source for the design of the fact table.) b. Define and describe the appropriate dimensions. (Hint: These dimensions become the source for the design of the dimension tables.) c. Draw the lab usage star schema, using the fact and dimension structures you defined in Problems 1a and 1b. d. Define the attributes for each of the dimensions in Problem 1b. e. Recommend the appropriate attribute hierarchies. f. Implement your data warehouse design, using the star schema you created in Problem 1c and the attributes you defined in Problem 1d. g. Create the reports that will meet the requirements listed in this problem’s introduction. 2. Victoria Ephanor manages a small product distribution company. Because the business is growing fast, she recognizes that it is time to manage the vast infor- mation pool to help guide the accelerating growth. Ephanor, who is familiar with spreadsheet software, currently employs a sales force of four people. She asks you to develop a data warehouse application prototype that will enable her to study sales figures by year, region, salesperson, and product. (This prototype will be used as the basis for a future data warehouse database.) Using the data supplied in the Ch13_P2.xls file, complete the following seven problems: a. Identify the appropriate fact table components. b. Identify the appropriate dimension tables. c. Draw a star schema diagram for this data warehouse. d. Identify the attributes for the dimension tables that will be required to solve this problem. e. Using Microsoft Excel or any other spreadsheet program that can produce pivot tables, generate a pivot table to show the sales by product and by region. The end user must be able to specify the display of sales for any given year. The sample output is shown in the first pivot table in Figure P13.2E. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Problems 640 Part 4 Advanced Database Concepts FIGUREP13.2E USINGAPIVOTTABLE f. Using Problem 2e as your base, add a second pivot table (see Figure P13.2E) to show the sales by salesperson and by region. The end user must be able to specify sales for a given year or for all years, and for a given product or for all products. g. Create a 3D bar graph to show sales by salesperson, by product, and by region. (See the sample output in Figure P13.2G.) FIGUREP13.2G 3DBARGRAPHSHOWINGTHERELATIONSHIPSAMONGAGENT, PRODUCT, AND REGION Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 3. David Suker, the inventory manager for a marketing research company, wants to study the use of supplies within the different company departments. Suker has heard that his friend, Victoria Ephanor, has developed a spreadsheet-based data ware- house model that she uses to analyze sales data (see Problem 2). Suker is interested in developing a data warehouse model like Ephanor’s so he can analyze orders by department and by product. He will use Microsoft Access as the data warehouse DBMS and Microsoft Excel as the analysis tool. a. Develop the order star schema. b. Identify the appropriate dimension attributes. c. Identify the attribute hierarchies required to support the model. d. Develop a crosstab report in Microsoft Access, using a 3D bar graph to show orders by product and by department. (The sample output is shown in Figure P13.3.) Chapter 13 Business Intelligence and Data Warehouses 641 FIGUREP13.3 CROSSTABREPORT:ORDERSBYPRODUCTANDDEPARTMENT Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 642 Part 4 Advanced Database Concepts 4. Content The script files used to populate the Ch13-Sale- Co-DW database are available at www. cengagebrain.com. The script files are available in Oracle, MySQL, and SQL Server formats. MS Access does not have SQL support for the complex grouping required. 5. ROBCOR, whose sample data is contained in the database named Ch13_P4.mdb, provides “on-demand” aviation charters using a mix of different aircraft and aircraft types. Because ROBCOR has grown rapidly, its owner has hired you as its first database manager. The company’s database, developed by an outside consulting team, is already in place to help manage all company operations. Your first criti- cal assignment is to develop a decision support system to analyze the charter data. (Review the company’s operations in Problems 24–31 of Chapter 3, The Relational Database Model.) The charter operations manager wants to be able to analyze charter data such as cost, hours flown, fuel used, and revenue. She also wants to be able to drill down by pilot, type of airplane, and time periods. Given those requirements, complete the following: a. Create a star schema for the charter data. b. Define the dimensions and attributes for the charter operation’s star schema. c. Define the necessary attribute hierarchies. d. Implement the data warehouse design using the design components you devel- oped in Problems 4a–4c. e. Generate the reports to illustrate that your data warehouse meets the specified information requirements. Using the data provided in the Ch13-SaleCo-DW database, solve the following problems. (Hint: In Problems 5–11, use the ROLLUP command.) What is the SQL command to list the total sales by customer and by product, with subtotals by customer and a grand total for all product sales? Figure P13.5 shows the abbreviated results of the query. What is the SQL command to list the total sales by customer, month, and product, with subtotals by customer and by month and a grand total for all product sales? Figure P13.6 shows the abbreviated results of the query. Online FIGUREP13.5 PROBLEM5ABBREVIATEDRESULT Some records omitted in output shown 6. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 7. What is the SQL command to list the total sales by region and customer, with subtotals by region and a grand total for all sales? Figure P13.7 shows the result of the query. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 643 FIGUREP13.6 PROBLEM6ABBREVIATEDRESULT Some records omitted in output shown FIGUREP13.7 PROBLEM7RESULT 644 Part 4 Advanced Database Concepts 8. What is the SQL command to list the total sales by month and product category, with subtotals by month and a grand total for all sales? Figure P13.8 shows the result of the query. FIGUREP13.8 PROBLEM8RESULT 9. What is the SQL command to list the number of product sales (number of rows) and total sales by month, with subtotals by month and a grand total for all sales? Figure P13.9 shows the result of the query. FIGUREP13.9 PROBLEM9RESULT Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 10. WhatistheSQLcommandtolistthenumberofproductsales(numberofrows)and total sales by month and product category, with subtotals by month and product category and a grand total for all sales? Figure P13.10 shows the result of the query. 11. What is the SQL command to list the number of product sales (number of rows) and total sales by month, product category, and product, with subtotals by month and prod- uct category and a grand total for all sales? Figure P13.11 shows the result of the query. Chapter 13 Business Intelligence and Data Warehouses 645 FIGUREP13.10 PROBLEM10RESULT FIGUREP13.11 PROBLEM11RESULT Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 646 Part 4 Advanced Database Concepts Note 12. Using the answer to Problem 10 as your base, what command would you need to generate the same output but with subtotals in all columns? (Hint: Use the CUBE command.) Figure P13.12 shows the result of the query. 13. Create your own data analysis and visualization presentation. The purpose of this project is for you to search for a publicly available data set using the Internet and create your own presentation using what you have learned in this chapter. a. Search for a data set that may interest you and download it. Some examples of public data sets sources are: • http://www.data.gov • http://data.worldbank.org • http://aws.amazon.com/datasets • http://usgovxml.com/ • https://data.medicare.gov/ • http://www.faa.gov/data_research/ b. Use any tool available to you to analyze the data. You can use tools such as MS Excel Pivot Tables, Pivot Charts, or other free tools, such as Google Fusion tables, Tableau free trial, IBM Many Eyes, etc. c. Create a short presentation to explain some of your findings (what the data sources are, where the data comes from, what the data represents, etc.) FIGUREP13.12 PROBLEM12RESULT The visualization in Figure P13.13 was created using a data set downloaded from one of the public sources listed above. A trial version of Tableau was used to create the visualiza- tions. This simple example illustrates the type of quick analysis you can do for this project. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 13 Business Intelligence and Data Warehouses 647 FIGURE P13.13 VISUALIZATION EXAMPLE USING TABLEAU Source: Tableau Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 14 Big Data Analytics and NoSQL In this chapter, you will learn: • What Big Data is and why it is important in modern business • The primary characteristics of Big Data and how these go beyond the traditional “3 Vs” • How the core components of the Hadoop framework, HDFS and MapReduce, operate • What the major components of the Hadoop ecosystem are • The four major approaches of the NoSQL data model and how they differ from the relational model • About data analytics, including data mining and predictive analytics Preview In Chapter 2, Data Models, you were introduced to the emerging NoSQL data model and the Big Data problem that has led to NoSQL’s development. In this chapter, you learn about these issues in much greater detail. You will find that there is more to Big Data and the problem that it represents to modern businesses than just the volume, velocity, and variety (“3 V”) characteristics introduced in Chapter 2. In fact, you will find that these characteristics themselves are more complex than previously discussed. After learning about Big Data issues, you learn about the technologies that have devel- oped, and continue to be developed, to address Big Data. First, you learn about the low- level technologies in the Hadoop framework. Hadoop has become a standard component in organizations’ efforts to address Big Data. Next, you learn about the higher-level approaches of the NoSQL data model to develop nonrelational databases such as key- value databases, document databases, column-oriented databases, and graph databases. Finally, you learn about the important area of data analytics and how statistical tech- niques are being used to help organizations turn the vast stores of data that are being collected into actionable information. Analytics are helping organizations understand not only what has happened in the business, but also to predict what is likely to happen. Note Data Files Available on cengagebrain.com Because it is purely conceptual, this chapter does not reference any data files. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. The relational database model has been dominant for decades, and during that time it has faced challenges such as object-oriented databases and the development of data warehouses. The relational model, and the tools based on it, have evolved to adapt to these challenges and remain dominant in the data management arena. In each case, the challenge arose because technological advances changed business’s perceptions of what is possible and created new opportunities for organizations to create value from increased data leverage. The latest of these challenges is Big Data. Big Data is an ill-defined term that describes a new wave of data storage and manipulation possibilities and requirements. Organizations’ efforts to store, manipulate, and analyze this new wave of data represent one of the most urgent emerging trends in the database field. The challenges of dealing with the wave of Big Data have led to the development of NoSQL databases that reject many of the underlying assumptions of the relational model. Although the term Big Data lacks a consistent definition, there is a set of characteristics generally associated with it. 14-1 Big Data Big Data generally refers to a set of data that displays the characteristics of volume, velocity, and variety (the “3 Vs”) to an extent that makes the data unsuitable for management by a relational database management system. These characteristics can be defined as follows: • Volume—the quantity of data to be stored • Velocity—the speed at which data is entering the system • Variety—the variations in the structure of the data to be stored Notice the lack of specific values associated with these characteristics. This lack of spec- ificity is what leads to the ambiguity in defining Big Data. What was Big Data five years ago might not be considered Big Data now. Similarly, something considered Big Data now might not be considered Big Data five years from now. The key is that the charac- teristics are present to an extent that the current relational database technology struggles with managing the data. Further adding to the problem of defining Big Data is that there is some disagreement among pundits about which of the 3 Vs must be present for a data set to be considered Big Data. Originally, Big Data was conceived as shown in Figure 14.1 as a combination of the 3 Vs. Web data, a combination of text, graphics, video, and audio sources combined Chapter 14 Big Data Analytics and NoSQL 649 FIGURE14.1 ORIGINALVIEWOFBIGDATA Volume Velocity Big Data Variety Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. volume A characteristic of Big Data that describes the quantity of data to be stored. velocity A characteristic of Big Data that describes the speed at which data enters the system and must be processed. variety A characteristic of Big Data that describes the variations in the structure of data to be stored. 650 Part 4 Advanced Database Concepts into complex structures, is often cited as creating the new challenges for data manage- ment that involve all three characteristics. After the dot-com bubble burst in the 1990s, many startup web-based companies failed, but the companies that survived experienced significant growth as web commerce consolidated into a smaller set of businesses. As a result, companies like Google and Amazon experienced significant growth and were among the first to feel the pressure of managing Big Data. The success of social media giant Facebook quickly followed, and these companies became pioneers in creating new technologies to address Big Data problems. Google created the BigTable data store, Amazon created Dynamo, and Facebook created Cassandra to deal with the growing need to store and manage large sets of data that had the characteristics of the 3 Vs. Although social media and web data have been at the forefront of perceptions of Big Data issues, other organizations have Big Data issues, too. More recently, changes in technology have increased the opportunities for businesses to generate and track data so that Big Data has been redefined as involving any, but not necessarily all, of the 3 Vs, as shown in Figure 14.2. Advances in technology have led to a vast array of user-generated data and machine-generated data that can spur growth in specific areas. FIGURE14.2 CURRENTVIEWOFBIGDATA Volume Velocity Big Data Variety For example, Disney World has introduced “Magic Bands” for park visitors to wear on their wrists. Each visitor’s Magic Band is connected to much of the data that Disney stores about that individual. These bands use RFID and near-field communications (NFC) to act as tickets for rides, hotel room keys, and even credit cards within the park. The bands can be tracked so the Disney systems can track individuals as they move through the park, record with which Disney characters (who are also tracked) they interact, pur- chases made, wait time in lines, and more. Visitors can make reservations at a restaurant and order meals through a Disney app on their smartphones, and by tracking the Magic Bands, the restaurant staff knows when the visitor arrives for their reservation, can track at which table they are seated, and deliver their meals within minutes of the guests sitting down. With the many cameras mounted throughout the park, Disney can also capture pictures and short videos of the visitor throughout their stay in the park to produce a personalized movie of their vacation experience, which can then be sold to the visitor as a souvenir. All of this involves the capture of a constant stream of data from each band, pro- cessed in real time. Considering the thousands of visitors in Disney World each day, each with their own Magic Band, the volume, velocity, and variety of the data is enormous. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 14-1a Volume Volume, the quantity of data to be stored, is a key characteristic of Big Data. The storage capacities associated with Big Data are extremely large. Table 14.1 provides definitions for units of data storage capacity. TABLE 14.1 STORAGE CAPACITY UNITS TERM CAPACITY ABBREVIATION Chapter 14 Big Data Analytics and NoSQL 651 Bit 0 or 1 value b Byte 8 bits B Kilobyte 1024* bytes KB Megabyte 1024 KB MB Gigabyte 1024 MB GB Terabyte 1024 GB TB Petabyte 1024 TB PB Exabyte 1024 PB EB Zettabyte 1024 EB ZB Yottabyte 1024 ZB YB * Note that because bits are binary in nature and are the basis on which all other storage values are based, all values for data storage units are defined in terms of powers of 2. For example, the prefix kilo typically means 1000; however, in data storage, a kilobyte = 210 = 1024 bytes. Naturally, as the quantity of data needing to be stored increases, the need for larger storage devices increases as well. When this occurs, systems can either scale up or scale out. Scaling up is keeping the same number of systems, but migrating each system to a larger system: for example, changing from a server with 16 CPU cores and a 1 TB storage system to a server with 64 CPU cores and a 100 TB storage system. Scaling up involves moving to larger and faster systems. However, there are limits to how large and fast a single system can be. Further, the costs of these high-powered systems increase at a dramatic rate. On the other hand, scaling out means that when the workload exceeds the capacity of a server, the workload is spread out across a number of servers. This is also referred to as clustering—creating a cluster of low-cost servers to share a workload. This can help to reduce the overall cost of the computing resources since it is cheaper to buy ten 100 TB storage systems than it is to buy a single 1 PB storage system. Make no mistake, organizations need storage capacities in these extreme sizes. The eBay singu- larity system, which collects clickstream data among other things, is over 40 PB. This is in addition to the eBay enterprise data warehouse, which is over 14 PB and spread over hundreds of thousands of nodes.1 1 Cliff Saran, “Case study: How big data powers the eBay customer journey,” ComputerWeekly.com, TechTarget, 2015, www.computerweekly.com/news/2240219736/Case-Study-How-big-data-powers-the-eBay-customer- journey, August 18, 2015. scaling up A method for dealing with data growth that involves migrating the same structure to more powerful systems. scaling out A method for dealing with data growth that involves distributing data storage structures across a cluster of commodity servers. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 652 Part 4 Advanced Database Concepts Recall from Chapter 3 that one of the greatest advances represented by the relational model was the development of an RDBMS—a sophisticated database management system that could hide the complexity of the underlying data storage and manipulation from the user so that the data always appears to be in tables. To carry out these functions, the DBMS acts as the “brain” of the database system and must maintain control over all of the data within the database. As discussed in Chapter 12, it is possible to distribute a relational database over multiple servers using replication and fragmentation. However, because the DBMS must act as a single point of control for all of the data in the database, distributing the database across multiple systems requires a high degree of communication and coordination across the systems. There are significant limits associated with the ability to distribute the DBMS due to the increased performance costs of communication and coordination as the number of nodes grows. This limits the degree to which a relational database to be scaled out as data volume grows, and it makes RDBMSs ill-suited for clusters. Note 14-1b Velocity Velocity, another key characteristic of Big Data, refers to the rate at which new data enters the system as well as the rate at which the data must be processed. In many ways, the issues of velocity mirror those of volume. For example, consider a web retailer such as Amazon. In the past, a retail store might capture only the data about the final transac- tion of a customer making a purchase. A retailer like Amazon captures not only the final transaction, but every click of the mouse in the searching, browsing, comparing, and purchase process. Instead of capturing one event (the final sale) in a 20-minute shopping experience, it might capture data on 30 events during that 20-minute time frame—a 30× increase in the velocity of the data. Other advances in technology, such as RFID, GPS, and NFC, add new layers of data-gathering opportunities that often generate large amounts of data that must be stored in real-time. For example, RFID tags can be used to track items for inventory and warehouse management. The tags do not require line-of- sight between the tag and the reader, and the reader can read hundreds of tags simulta- neously while the products are still in boxes. This means that instead of a single record for tracking a given quantity of a product being produced, each individual product is tracked, creating an increase of several orders of magnitude in the amount of data being delivered to the system at any one time. In addition to the speed with which data is entering the system, for Big Data to be actionable, that data must be processed at a very rapid pace. The velocity of processing can be broken down into two categories. • Stream processing • Feedback loop processing Stream processing focuses on input processing, and it requires analysis of the data stream as it enters the system. In some situations, large volumes of data can enter the system at such a rapid pace that it is not feasible to try to store all of the data. The data must be processed and filtered as it enters the system to determine which data to keep rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Although some RDBMS products, such as SQL Server and Oracle Real-Application Clusters (RAC), legitimately claim to support clusters, these clusters are limited in scope and gener- ally rely on a single, shared data storage subsystem, such as a storage area network (SAN). stream processing The processing of data inputs in order to make decisions about which data to keep and which data to discard before storage. Copyright 2017 Cengage Lea Editorial review has deemed that and which data to discard. For example, at the CERN Large Hadron Collider, the largest and most powerful particle accelerator in the world, experiments produce about 600 TB per second of raw data. Scientists have created algorithms to decide ahead of time which data will be kept. These algorithms are applied in a two-step process to filter the data down to only about 1 GB per second of data that will actually be stored.2 Feedback loop processing refers to the analysis of the data to produce actionable results. While stream processing could be thought of as focused on inputs, feedback loop processing can be thought of as focused on outputs. The process of capturing the data, processing it into usable information, and then acting on that information is a feedback loop. Figure 14.3 shows a feedback loop for providing recommendations for book pur- chases. Feedback loop processing to provide immediate results requires analyzing large amounts of data within just a few seconds so that the results of the analysis can become a part of the product delivered to the user in real time. Not all feedback loops are used for inclusion of results within immediate data products. Feedback loop processing is also used to help organizations sift through terabytes and petabytes of data to inform decision makers to help them make faster strategic and tactical decisions, and it is a key component in data analytics. FIGURE14.3 FEEDBACKLOOPPROCESSING Data is analyzed to determine other books and products the user may like 14-1c Variety List of recommended items added to the user request Data is captured about the user and about the book requested In a Big Data context, variety refers to the vast array of formats and structures in which the data may be captured. Data can be considered to be structured, unstructured, or semistructured. Structured data is data that has been organized to fit a predefined data model. Unstructured data is data that is not organized to fit into a predefined data model. Semistructured data combines elements of both—some parts of the data fit a pre- defined model while other parts do not. Relational databases rely on structured data. A data model is created by the database designer based on the business rules, as discussed in Chapter 4. As data enters the database, the data is decomposed and routed for storage in the corresponding tables and columns as defined in the data model. Although much of the transactional data that organizations use works well in a structured environment, 2 CERN, “Processing: What to record?” http://home.web.cern.ch/about/computing/processing-what-record, August 20, 2015. algorithm A process or set of operations in a calculation. feedback loop processing Analyzing stored data to produce actionable results. structured data Data that conforms to a predefined data model. unstructured data Data that does not conform to a predefined data model. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 14 Big Data Analytics and NoSQL 653 Information requested by user plus information on recommendations are returned User clicks on a link for a book 654 Part 4 Advanced Database Concepts most of the data in the world is semistructured or unstructured. Unstructured data includes maps, satellite images, emails, texts, tweets, videos, transcripts, and a whole host of other data forms. Over the decades that the relational model has been dominant, relational databases have evolved to address some forms of unstructured data. For exam- ple, most large-scale RDBMSs support a binary large object (BLOB) data type that allows the storage of unstructured objects like audio, video, and graphic data as a single, atomic value. One problem with BLOB data is that the semantic value of the data, the meaning that the object conveys, is inaccessible and uninterpretable by data processing. Big Data requires that the data be captured in whatever format it naturally exists, without any attempt to impose a data model or structure to the data. This is one of the key differences between processing data in a relational database and Big Data process- ing. Relational databases impose a structure on the data when the data is captured and stored. Big Data processing imposes a structure on the data as needed for applications as a part of retrieval and processing. One advantage to providing structure during retrieval and processing is the flexibility of being able to structure the data in different ways for different applications. 14-1d Other Characteristics Characterizing Big Data with the 3 Vs is fairly standard. However, as the industry matures, other characteristics have been put forward as being equally important. Keep- ing with the spirit of the 3 Vs, these additional characteristics are typically presented as additional Vs. Variability refers to the changes in the meaning of the data based on con- text. While variety and variability are similar terms, they mean distinctly different things in Big Data. Variety is about differences in structure. Variability is about differences in meaning. Variability is especially relevant in areas such as sentiment analysis that attempt to understand the meanings of words. Sentiment analysis is a method of text analysis that attempts to determine if a statement conveys a positive, negative, or neutral attitude about a topic. For example, the statements, “I just bought a new smartphone—I love it!” and “The screen on my new smartphone shattered the first time I dropped it—I love it!” In the first statement the presence of the phrase “I love it” might help an algorithm correctly interpret the statement as expressing a positive attitude. However, the second statement uses sarcasm to express a negative attitude so the presence of the phrase “I love it” may cause the analysis to interpret the meaning of the phrase incorrectly. Veracity refers to the trustworthiness of the data. Can decision makers reasonably rely on the accuracy of the data and the information generated from it? This is espe- cially pertinent given the automation of data capture and some of the analysis. Uncer- tainty about the data can arise from several causes, such as having to capture only selected portions of the data due to high velocity. Also, in terms of sentiment analysis, customers’ opinions and preferences can change over time, so comments at one point in time might not be suitable for action at another point in time. Increasingly, value is being touted as an important characteristic for Big Data. Value, also called viability, refers to the degree to which the data can be analyzed to provide meaningful information that can add value to the organization. Just because a set of data can be captured does not mean that it should be captured. Only data that can form the basis for analysis that has the potential to impact organizational behavior should be included in a company’s Big Data efforts. The final characteristic of Big Data is visualization. Visualization is the ability to graphically present the data in such a way as to make it understandable. Volumes of data can leave decision makers awash in facts but with little understanding of what the facts mean. Visualization is a way of presenting the facts so that decision makers can compre- hend the meaning of the information to gain insights. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. variability The characteristic of Big Data for the same data values to vary in meaning over time. sentiment analysis A method of text analysis that attempts to determine if a statement conveys a positive, negative, or neutral attitude. veracity The trustworthiness of a set of data. value The degree to which data can be analyzed to provide meaningful insights. visualization The ability to graphically present data in such a way as to make it understandable to users. Copyright 2017 Cengage Lea Editorial review has deemed that An argument could be made that these additional Vs are not necessarily characteris- tics of Big Data; or, perhaps more accurately, they are not characteristics of only Big Data. Veracity of data is an issue with even the smallest data store, which is why data manage- ment is so important in relational databases. Value of data also applies to traditional, structured data in a relational database. One of the keys to data modeling is that only the data that is of interest to the users should be included in the data model. Data that is not of value should not be recorded in any data store—Big Data or not. Visualization was dis- cussed and illustrated at length in Chapter 13 as an important tool in working with data warehouses, which are often maintained as structured data stores in RDBMS products. The important thing to remember is that these characteristics that play an important part in working with data in the relational model are universal and also apply to Big Data. Big Data represents a new wave in data management challenges, but it does not mean that relational database technology is going away. Structured data that depends on ACID transactions, as discussed in Chapter 10, will always be critical to business operations. Relational databases are still the best way for storing and managing this type of data. What has changed is that now, for the first time in decades, relational databases are not necessarily the best way for storing and managing all of an organization’s data. Since the rise of the relational model, the decision for data managers when faced with new storage requirements was not whether to use a relational database, but rather which relational DBMS to use. Now, the decision of whether to use a relational database at all is a real question. This has led to polyglot persistence—the coexistence of a variety of data stor- age and management technologies within an organization’s infrastructure. Scaling up, as discussed, is often considered a viable option as relational databases grow. However, it has practical limits and cost considerations that make it infeasible for many Big Data installations. Scaling out into clusters based on low-cost, commodity servers is the dominant approach that organizations are currently pursuing for Big Data management. As a result, new technologies not based on the relational model have been developed. 14-2 Hadoop Big Data requires a different approach to distributed data storage that is designed for large-scale clusters. Although other implementation technologies are possible, Hadoop has become the de facto standard for most Big Data storage and processing. Hadoop is not a database. Hadoop is a Java-based framework for distributing and processing very large data sets across clusters of computers. While the Hadoop framework includes many parts, the two most important components are the Hadoop Distributed File System (HDFS) and MapReduce. HDFS is a low-level distributed file processing system, which means that it can be used directly for data storage. MapReduce is a programming model that supports processing large data sets in a highly parallel, distributed manner. While it is possible to use HDFS and MapReduce separately, the two technologies complement each other so that they work better together as a Hadoop system. Hadoop was engineered specifically to distribute and process enormous amounts of data across vast clusters of servers. 14-2a HDFS The Hadoop Distributed File System (HDFS) approach to distributing data is based on several key assumptions: polyglot persistence The coexistence of a variety of data storage and data management technologies within an organization’s infrastructure. Hadoop Distributed File System (HDFS) A highly distributed, fault-tolerant file storage system designed to manage large amounts of data at high speeds. Chapter 14 Big Data Analytics and NoSQL 655 • High volume. The volume of data in Big Data applications is expected to be in tera- bytes, petabytes, or larger. Hadoop assumes that files in the HDFS will be extremely large. Data in the HDFS is organized into physical blocks, just as in other file storage. For example, on a typical personal computer, file storage is organized into blocks that Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 656 Part 4 Advanced Database Concepts are often 512 bytes in size, depending on the hardware and operating system involved. Relational databases often aggregate these into database blocks. By default, Oracle organizes data into 8-KB physical blocks. Hadoop, on the other hand, has a default block size of 64 MB (8,000 times the size of an Oracle block!), and it can be configured to even larger values. As a result, the number of blocks per file is greatly reduced, sim- plifying the metadata overhead of tracking the blocks in each file. • Write-once, read-many. Using a write-once, read-many model simplifies concurrency issues and improves overall data throughput. Using this model, a file is created, writ- ten to the file system, and then closed. Once the file is closed, changes cannot be made to its contents. This improves overall system performance and works well for the types of tasks performed by many Big Data applications. Although existing contents of the file cannot be changed, recent advancements in the HDFS allow for files to have new data appended to the end of the file. This is a key advancement for NoSQL databases because it allows for database logs to be updated. • Streaming access. Unlike transaction processing systems where queries often retrieve small pieces of data from several different tables, Big Data applications typically process entire files. Instead of optimizing the file system to randomly access individual data elements, Hadoop is optimized for batch processing of entire files as a continuous stream of data. • Fault tolerance. Hadoop is designed to be distributed across thousands of low-cost, commodity computers. It is assumed that with thousands of such devices, at any point in time, some will experience hardware errors. Therefore, the HDFS is designed to replicate data across many different devices so that when one device fails, the data is still available from another device. By default, Hadoop uses a replication factor of three, meaning that each block of data is stored on three different devices. Different replication factors can be specified for each file, if desired. Hadoop uses several types of nodes. A node is just a computer that performs one or more types of tasks within the system. Within the HDFS, there are three types of nodes: the client node, the name node, and one or more data nodes, as depicted in Figure 14.4. FIGURE14.4 HADOOPDISTRIBUTEDFILESYSTEM(HDFS) Client Node Name Node Metadata: File1: Blocks 1,3,4: r3 File2: Blocks 2,5,6: r3 Data Node 1 Data Node 2 Data Node 3 Data Node 4 Block 1 Block 2 Block 3 Block 6 Block 1 Block 2 Block 1 Block 3 Block 2 Block 3 Block 4 Block 5 Block 5 Block 4 Block 5 Block 4 Block 6 Block 6 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Data nodes store the actual file data within the HDFS. Recall that files in HDFS are broken into blocks and are replicated to ensure fault tolerance. As a result, each block is duplicated on more than one data node. Figure 14.4 shows the default replication factor of three, so each block appears on three data nodes. The name node contains the metadata for the file system. There is typically only one name node within a HDFS cluster. The metadata is designed to be small, simple, and easily recoverable. Keeping the metadata small allows the name node to hold all of the metadata in memory to reduce disk accesses and improve system performance. This is important because there is only one name node so contention for the name node is min- imized. The metadata is composed primarily of the name of each file, the block numbers that comprise each file, and the desired replication factor for each file. The client node makes requests to the file system, either to read files or to write new files, as needed to support the user application. When a client node needs to create a new file, it communicates with the name node. The name node: • Adds the new file name to the metadata. • Determines a new block number for the file. • Determines a list of which data nodes the block will be stored. • Passes that information back to the client node. The client node contacts the first data node specified by the name node and begins writing the file on that data node. At the same time, the client node sends the data node the list of other data nodes that will be replicating the block. As the data is received from the client node, the data node contacts the next data node in the list and begins sending the data to this node for replication. This second data node then contacts the next data node in the list and the process continues with the data being streamed across all of the data nodes that are storing the block. Once the first block is written, the client node can get another block number and list of data nodes from the name node for the next block. When the entire file has been written, the client node informs the name node that the file is closed. It is important to note that at no time was any of the data file actually transmitted to the name node. This helps to reduce the data flow to the name node to avoid congestion that could slow system performance. Similarly, if a client node needs to read a file, it contacts the name node to request the list of blocks associated with that file and the data nodes that hold them. Given that each block may appear in many data nodes, for each block, the client attempts to retrieve the block from the data node that is closest to it on the network. Using this information, the client node reads the data directly from each of those nodes. Periodically, each data node communicates with the name node. The data nodes send block reports and heartbeats. A block report is sent every 6 hours and informs the name node of which blocks are on that data node. Heartbeats are sent every 3 seconds. A heartbeat is used to let the name node know that the data node is still available. If a data node experiences a fault, due to hardware failure, power outage, etc., then the name node will not receive a heartbeat from that data node. As a result, the name node knows not to include that data node in lists to client nodes for reading or writing files. If the lack of a heartbeat from a data node causes a block to have fewer than the desired number of replicas, the name node can have a “live” data node initiate replicating the block on another data node. Taken together, the components of the HDFS produce a powerful, yet highly special- ized distributed file system that works well for the specialized processing requirements of Big Data applications. Next, we will consider how MapReduce provides data processing to complement data storage of HDFS. block report In the Hadoop Distributed File System (HDFS), a report sent every 6 hours by the data node to the name node informing the name node which blocks are on that data node. heartbeat In the Hadoop Distributed File System (HDFS), a signal sent every 3 seconds from the data node to the name node to notify the name node that the data node is still available. Chapter 14 Big Data Analytics and NoSQL 657 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 658 Part 4 Advanced Database Concepts 14-2b MapReduce MapReduce is the computing framework used to process large data sets across clusters. Conceptually, MapReduce is easy to understand and follows the principle of divide and conquer. MapReduce takes a complex task, breaks it down into a collection of smaller subtasks, performs the subtasks all at the same time, and then combines the result of each subtask to produce a final result for the original task. As the name implies, it is a combination of a map function and a reduce function. A map function takes a collection of data and sorts and filters the data into a set of key-value pairs. The map function is performed by a program called a mapper. A reduce function takes a collection of key-value pairs, all with the same key value, and summarizes them into a single result. The reduce function is performed by a program called a reducer. Recall that Hadoop is a Java-based platform, therefore map and reduce functions are written as detailed, procedure-oriented Java programs. Figure 14.5 provides a simple, conceptual illustration of MapReduce that determines the total number of units of each product that has been sold. The original data in Figure 14.5 is stored as key-value pairs, with the invoice number as the key and the remainder of the invoice data as a value. Remember, the data in Hadoop data storage is not a relational data- base so the data is not separated into tables and there is no form of normalization that ensures that each fact is stored only once. Therefore, there is a great deal of duplication of data in the original data store. Note that even in the very small subset of data that is shown in Figure 14.5, redundant data is kept for customer 10011, Leona Dunne. In the figure, map functions parse each invoice to find data about the products sold on that invoice. The result of the map function is a new list of key-value pairs in which the product code is the key and the line units are the value. The reduce function then takes that list of key-value pairs and combines them by summing the values associated with each key (product code) to produce the summary result. MapReduce An open-source application programming interface (API) that provides fast data analytics services; one of the main Big Data technologies that allows organizations to process massive data stores. map The function in a MapReduce job that sorts and filters data into a set of key-value pairs as a subtask within a larger job. mapper A program that performs a map function. FIGURE14.5 MAPREDUCE As previously stated, the data sets used in Big Data applications are extremely large. Transferring entire files from multiple nodes to a central node for processing would require a tremendous amount of network bandwidth, and place an incredible processing burden on the central node. Therefore, instead of the computational program retrieving the data for processing in a central location, copies of the program are “pushed” to the nodes containing the data to be processed. Each copy of the program produces results that are then aggregated across nodes and sent back to the client. This mirrors the distri- bution of data in the HDFS. Typically, the Hadoop framework will distribute a mapper rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. reduce The function in a MapReduce job that collects and summarizes the results of map functions to produce a single result. reducer A program that performs a reduce function. Copyright 2017 Cengage Lea Editorial review has deemed that for each block on each data node that must be processed. This can lead to a very large number of mappers. For example, if 1 TB of data is to be processed and the HDFS is using 64-MB blocks, that yields over 15,000 mapper programs. The number of reducers is configurable by the user, but best practices suggest about one reducer per data node. Note The implementation of MapReduce complements the structure of the HDFS, which is an important reason why they work so well together. Just as the HDFS structure is com- posed of a name node and several data nodes, MapReduce uses a job tracker (the actual name of the program is JobTracker) and several task trackers (the programs are named TaskTrackers). The job tracker acts as a central control for MapReduce processing and it normally exists on the same server that is acting as the name node. Task tracker pro- grams reside on the data nodes. One important feature of the MapReduce framework is that the user must write the Java code for the map and reduce functions, and must spec- ify the input and output files to be read and written for the job that is being submitted. However, the job tracker will take care of locating the data, determining which nodes to use, dividing the job into tasks for the nodes, and managing failures of the nodes. All of this is done automatically without user intervention. When a user submits a MapReduce job for processing, the general process is as follows: 1. A client node (client application) submits a MapReduce job to the job tracker. 2. The job tracker communicates with the name node to determine which data nodes contain the blocks that should be processed for this job. 3. The job tracker determines which task trackers are available for work. Each task tracker can handle a set number of tasks. Remember, many MapReduce jobs from dif- ferent users can be running on the Hadoop system simultaneously, so a data node may contain data that is being processed by multiple mappers from different jobs all at the same time. Therefore, the task tracker on that node might be busy running mappers for other jobs when this new request arrives. Because the data is replicated on multiple nodes, the job tracker may be able to select from multiple nodes for the same data. 4. The job tracker then contacts the task trackers on each of those nodes to begin mappers and reducers to complete that node’s portion of the task. 5. ThetasktrackercreatesanewJVM(Javavirtualmachine)torunthemapandreduce functions. This way, if a function fails or crashes, the entire task tracker is not halted. 6. The task tracker sends heartbeat messages to the job tracker to let the job tracker know that the task tracker is still working on the job (and about the nodes availability for more jobs). 7. The job tracker monitors the heartbeat messages to determine if a task manager has failed. If so, the job tracker can reassign that portion of the task to another node. 8. When the entire job is finished, the job tracker changes status to indicate that the job is completed. 9. The client node periodically queries the job tracker until the job status is completed. job tracker A central control program used to accept, distribute, monitor, and report on MapReduce processing jobs in a Hadoop environment. task tracker A program in the MapReduce framework responsible to running map and reduce tasks on a node. Chapter 14 Big Data Analytics and NoSQL 659 Best practices suggest that the number of mappers on a given node should be kept to 100 or less. However, there are cases of applications with simple map functions running as many as 300 mappers on a given node with satisfactory performance. Clearly, much depends on the computing resources available at each node. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 660 Part 4 Advanced Database Concepts The Hadoop system uses batch processing. Batch processing is when a program runs from beginning to end, either completing the task or halting with an error, without any interaction with the user. Batch processing is often used when the computing task requires an extended period of time or a large portion of the system’s processing capacity. Businesses often use batch processing to run year-end financial reports in the evenings when systems are often idle, and universities might use batch processing for student fee payment processing. Batch processing is not bad, but it has limitations. As a result, a number of complementary programs have been developed to improve the integration of Hadoop within the larger IT infrastructure. The next section discusses some of these programs. 14-2c Hadoop Ecosystem Hadoop is widely used by organizations tapping into the potential of analyzing extremely large data sets. Unfortunately, because Hadoop is a very low-level tool requiring consid- erable effort to create, manage, and use, it presents quite a few obstacles. As a result, a host of related applications have grown up around Hadoop to attempt to make it easier to use and more accessible to users who are not skilled at complex Java programming. Figure 14.6 shows examples of some of these types of applications. Most organizations that use Hadoop also use a set of other related products that interact and complement each other to produce an entire ecosystem of applications and tools. Like any ecosystem, the interconnected pieces are constantly evolving and their relationships are changing, so it is a rather fluid situation. The following are some of the more popular components in a Hadoop ecosystem and how they relate to each other. FIGURE14.6 ASAMPLEOFTHEHADOOPECOSYSTEM MapReduce simplification applications Pig Hive Flume Sqoop Data ingestion applications Core Hadoop components HBase Impala Direct query applications MapReduce Hadoop Distributed File System (HDFS) MapReduceSimplificationApplications CreatingMapReducejobsrequiressignif- icant programming skills. As the mapper and reducer programs become more complex, the skill requirements increase and the time to produce the programs becomes signifi- cant. These skills are beyond the capabilities of most data users. Therefore, applications to simplify the process of creating MapReduce jobs have been developed. Two of the most popular are Hive and Pig. Hive is a data warehousing system that sits on top of HDFS. It is not a relational database, but it supports its own SQL-like language, called HiveQL, that mimics SQL commands to run ad hoc queries. HiveQL commands are processed by the Hive query engine into sets of MapReduce jobs. As a result, the underlying processing tends to be batch-oriented, producing jobs that are very scalable over extremely large sets of data. However, the batch nature of the jobs makes Hive a poor choice for jobs that only require a small subset of data to be returned very quickly. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. batch processing A data processing method that runs data processing tasks from beginning to end without any user interaction. Copyright 2017 Cengage Lea Editorial review has deemed that Pig is a tool for compiling a high-level scripting language, named Pig Latin, into MapReduce jobs for executing in Hadoop. In concept it is similar to Hive in that it provides a means of producing MapReduce jobs without the burden of low-level Java programming. The primary difference is that Pig Latin is a scripting language, which means it is procedural, while HiveQL, like SQL, is declarative. Declarative languages allow the user to specify what they want, not how to get it. This is very useful for query processing. Procedural languages require the user to specify how the data is to be manip- ulated. This is very useful for performing data transformations. As a result, Pig is often used for producing data pipeline tasks that transform data in a series of steps. This is often seen in ETL processes as described in Chapter 13. Data Ingestion Applications One challenge faced by organizations that are taking advantage of Hadoop’s massive data storage and data processing capabilities is the issue of actually getting data from their existing systems into the Hadoop cluster. To simplify this task, applications have been developed to “ingest” or gather this data into Hadoop. Flume is a component for ingesting data into Hadoop. It is designed primarily for harvesting large sets of data from server log files, like clickstream data from web server logs. It can be configured to import the data on a regular schedule or based on specified events. In addition to simply bringing the data into Hadoop, Flume contains a simple query processing component so the possibility exists of performing some transforma- tions on the data as it is being harvested. Typically, Flume would move the data into the HDFS, but it can also be configured to input the data directly into another component of the Hadoop ecosystem named HBase. Sqoop is a more recent addition to the Hadoop ecosystem. It is a tool for convert- ing data back and forth between a relational database and the HDFS. The name Sqoop (pronounced, “scoop,” as in a scoop of ice cream) is an amalgam of “SQL-to-Hadoop.” In concept, Sqoop is similar to Flume in that it provides a way of bringing data into the HDFS. However, while Flume works primarily with log files, Sqoop works with relational databases such as Oracle, MySQL, and SQL Server. Further, while Flume operates in one direction only, Sqoop can transfer data in both directions—into and out of HDFS. When transferring data from a relational database into HDFS, the data is imported one table at a time with the process reading the table row-by-row. This is done in a highly parallelized manner using MapReduce, so the contents of the table will usually be distributed into several files with the rows stored in a delimited format. Once the data has been imported into HDFS, it can be processed by MapReduce jobs or using Hive. The resulting data can then be exported from HDFS back to the relational database, most often a traditional data warehouse. Direct Query Applications Direct query applications attempt to provide faster query access than is possible through MapReduce. These applications interact with HDFS directly, instead of going through the MapReduce processing layer. HBase is a column-oriented NoSQL database designed to sit on top of the HDFS. One of HBase’s primary characteristics is that it is highly distributed and designed to scale out easily. It does not support SQL or SQL-like languages, relying instead on lower-level languages such as Java for interaction. The system does not rely on MapReduce jobs, so it avoids the delays caused by batch processing, making it more suitable for fast processing involving smaller subsets of the data. HBase is very good at quickly processing sparse data sets. HBase is one of the more popular components of the Hadoop ecosystem, and is used by Facebook for its messaging system. Column-oriented databases will be discussed in more detail in the next section. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Chapter 14 Big Data Analytics and NoSQL 661 662 Part 4 Advanced Database Concepts Impala was the first SQL-on-Hadoop application. It was produced by Cloudera as a query engine that supports SQL queries that pull data directly from HDFS. Prior to Impala, if an organization needed to make data from Hadoop available to analysts through an SQL interface, data would be extracted from HDFS and imported into a relational database. With Impala, analysts can write SQL queries directly against the data while it is still in HDFS. Impala makes heavy use of in-memory caching on data nodes. It is generally considered an appropriate tool for processing large amounts of data into a relatively small result set. Note 14-3 NoSQL NoSQL is the unfortunate name given to a broad array of nonrelational database technol- ogies that have developed to address the challenges represented by Big Data. The name is unfortunate in that it does not describe what the NoSQL technologies are, but rather what they are not. In fact, the name also does a poor job of explaining what the technologies are not! The name was chosen as a Twitter hashtag to simplify coordinating a meeting of developers to discuss ideas about the nonrelational database technologies that were being developed by organizations like Google, Amazon, and Facebook to deal with the problems they were encountering as their data sets reached enormous sizes. The term “NoSQL” was never meant to imply that products in this category should never include support for SQL. In fact, many such products support query languages that mimic SQL in important ways. Although no one has yet produced a NoSQL system that implements standard SQL, given the large base of SQL users, the appeal of creating such a product is obvious. More recently, some industry observers have tried to interject that “NoSQL” could stand for “Not Only SQL.” In fact, if the requirement to be considered a NoSQL product were sim- ply that languages beyond SQL are supported, then all of the traditional RDBMS products such as Oracle, SQL Server, MySQL, and MS Access would all qualify. Regardless, you are better off focusing on understanding the array of technologies to which the term refers than worrying about the name itself. There are literally hundreds of products that can be considered as being under the broadly defined term NoSQL. Most of these fit roughly into one of four categories: key- value data stores, document databases, column-oriented databases, and graph data- bases. Table 14.2 shows some popular NoSQL databases of each type. Although not all NoSQL databases have been produced as open-source software, most have been. As a result, NoSQL databases are generally perceived as a part of the open-source movement. Accordingly, they also tend to be associated with the Linux operating system. It makes sense from a cost standpoint that, if an organization is going to create a cluster con- taining tens of thousands of nodes, the organization does not want to purchase licenses for Windows or Mac OS for all of those nodes. The preference is to use a platform, like Linux, that is freely available and highly customizable. Therefore, most of the NoSQL products run only in a Linux or Unix environment. The following sections discuss each of the major NoSQL approaches. rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Other than Impala, each of the components of the Hadoop ecosystem described in this section are all open-source, top-level projects of the Apache Software Foundation. More information on each of these projects and many others is available at www.apache.org. NoSQL A new generation of database management systems that is not based on the traditional relational database model. Copyright 2017 Cengage Lea Editorial review has deemed that TABLE 14.2 NOSQL DATABASES NOSQL CATEGORY EXAMPLE DATABASES Chapter 14 Big Data Analytics and NoSQL 663 Key-value database Dynamo Riak Redis Voldemort Document databases MongoDB CouchDB OrientDB RavenDB Column-oriented databases HBase Cassandra Hypertable Graph databases Neo4J ArangoDB GraphBase 14-3a Key-Value Databases Key-value (KV) databases are conceptually the simplest of the NoSQL data models. A KV database is a NoSQL database that stores data as a collection of key-value pairs. The key acts as an identifier for the value. The value can be anything such as text, an XML document, or an image. The database does not attempt to understand the contents of the value component or its meaning—the database simply stores whatever value is provided for the key. It is the job of the applications that use the data to understand the meaning of the data in the value component. There are no foreign keys; in fact, relationships can- not be tracked among keys at all. This greatly simplifies the work that the DBMS must perform, making KV databases extremely fast and scalable for basic processing. Key-value pairs are typically organized into “buckets.” A bucket can roughly be thought of as the KV database equivalent of a table. A bucket is a logical grouping of keys. Key values must be unique within a bucket, but they can be duplicated across buckets. All data operations are based on the bucket plus the key. In other words, it is not possible to query the data based on anything in the value component of the key-value pair. All queries are performed by specifying the bucket and key. Operations on KV databases are rather simple—only get, store, and delete operations are used. Get or fetch is used to retrieve the value component of the pair. Store is used to place a value in a key. If the bucket + key combination does not exist, then it is added as a new key-value pair. If the bucket + key combination does exist, then the existing value component is replaced with the new value. Delete is used to remove a key-value pair. Figure 14.7 shows a customer bucket with three key-value pairs. Since the KV model does not allow queries based on data in the value component, it is not possible to query for a key-value pair based on customer last name, for example. In fact, the KV DBMS does not even know that there is such a thing as a customer last name because it does not understand the content of the value component. An application could issue a get command to have the KV DBMS return the key-value pair for bucket customer and key 10011, but it would be up to the application to know how to parse the value component to find the customer’s last name, first name, and other characteristics. (One important note about Figure 14.7: Be aware that although key-value pairs appear in tabular form in the figure, the tabular format is just a convenience to help visually distinguish the components. Actual key-value pairs are not stored in a table-like structure.) key-value (KV) database A NoSQL database model that stores data as a collection of key- value pairs in which the value component is unintelligible to the DBMS. bucket In a key-value database, a logical collection of related key-value pairs. Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 664 Part 4 Advanced Database Concepts FIGURE14.7 KEY-VALUEDATABASESTORAGE Bucket = Customer Key Value 10010 “LName Ramas FName Alfred Initial A Areacode 615 Phone 844-2573 Balance 0” 10011 “LName Dunne FName Leona Initial K Areacode 713 Phone 894-1238 Balance 0” 10014 “LName Orlando FName Myron Areacode 615 Phone 222-1672 Balance 0” 14-3b Document Databases Document databases are conceptually similar to key-value databases, and they can almost be considered a subtype of KV databases. A document database is a NoSQL data- base that stores data in tagged documents in key-value pairs. Unlike a KV database where the value component can contain any type of data, a document database always stores a document in the value component. The document can be in any encoded format, such as XML, JSON (JavaScript Object Notation), or BSON (Binary JSON). Another import- ant difference is that while KV databases do not attempt to understand the content of the value component, document databases do. Tags are named portions of a document. For example, a document may have tags to identify which text in the document represents the title, author, and body of the document. Within the body of the document, there may be additional tags to indicate chapters and sections. Despite the use of tags in documents, document databases are considered schema-less, that is, they do not impose a predefined structure on the data that is stored. For a document database, being schema-less means that although all documents have tags, not all documents are required to have the same tags, so each document can have its own structure. The tags in a document database are extremely important because they are the basis for most of the additional capabilities that document databases have over KV databases. Tags inside the document are accessi- ble to the DBMS, which makes sophisticated querying possible. Just as KV databases group key-value pairs into logical groups called buckets, document databases group documents into logical groups called collections. While a document may be retrieved by specifying the collection and key, it is also possible to query based on the contents of tags. For example, Figure 14.8 represents the same data from Figure 14.7, but in a tagged format for a document database. Because the DBMS is aware of the tags within the documents, it is possible to write queries that retrieve all of FIGURE14.8 DOCUMENTDATABASETAGGEDFORMAT Collection = Customer Key Document 10010 {LName: “Ramas”, FName: “Alfred”, Initial: “A”, Areacode: “615”, Phone: “844-2573”, Balance: “0”} 10011 {LName: “Dunne”, FName: “Leona”, Initial: “K”, Areacode: “713”, Phone: “894-1238”, Balance: “0”} 10014 {LName: “Orlando”, FName: “Myron”, Areacode: “615”, Phone: “222-1672”, Balance: “0”} rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. document database A NoSQL database model that stores data in key-value pairs in which the value component is composed of a tag- encoded document. JSON (JavaScript Object Notation) A human-readable text format for data interchange that defines attributes and values in a document. BSON (Binary JSON) A computer-readable format for data interchange that expands the JSON format to include additional data types including binary objects. Copyright 2017 Cengage Lea Editorial review has deemed that the documents where the Balance tag has the value 0. Document databases even support some aggregate functions such as summing or averaging balances in queries. Document databases tend to operate on an implied assumption that a document is rel- atively self-contained, not a fragment of the data about a given topic. Relational databases decompose complex data in the business environment into a set of related tables. For example data about orders may be decomposed into customer, invoice, line, and product tables. A document database would expect all of the data related to an order to be in a single order document. Therefore, each order document in an Orders collection would contain data on the customer, the order itself, and the products purchased in that order all as a single self-contained document. Document databases do not store relationships as perceived in the relational model and generally have no support for join operations. 14-3c Column-Oriented Databases The term column-oriented database can refer to two different sets of technologies that are often confused with each other. In one sense, column-oriented database or columnar database can refer to traditional, relational database technologies that use column-centric storage instead of row-centric storage. Relational databases present data in logical tables; however, the data is actually stored in data blocks containing rows of data. All of the data for a given row is stored together in sequence with many rows in the same data block. If a table has many rows of data, the rows will be spread across many data blocks. Figure 14.9 illustrates a relational table with 10 rows of data that is physically stored across five data blocks. Row-centric storage minimizes the number of disk reads necessary to retrieve a row of data. Retrieving one row of data requires accessing just Chapter 14 Big Data Analytics and NoSQL 665 FIGURE14.9 COMPARISONOFROW-CENTRICANDCOLUMN-CENTRICSTORAGE CUSTOMER relational table Cus_Code Cus_LName Cus_FName Cus_City Cus_State 10010 Ramas Alfred Nashville TN 10011 Dunne Leona Miami FL 10012 Smith Kathy Boston MA 10013 Olowski Paul Nashville TN 10014 Orlando Myron 10015 O’Brian Amy Miami FL 10016 Brown James 10017 Williams George Mobile AL 10018 Farriss Anne Opp AL 10019 Smith Olette Nashville TN Row-centric storage Column-centric storage Block 1 10010,Ramas,Alfred,Nashville,TN 10011,Dunne,Leona,Miami,FL Block 2 10012,Smith,Kathy,Boston,MA 10013,Olowski,Paul,Nashville,TN Block 4 10016,Brown,James,NULL,NULL 10017,Williams,George,Mobile,AL Block 5 10018,Farriss,Anne,OPP,AL 10019,Smith,Olette,Nashville,TN Block 1 10010,10011,10012,10013,10014 10015,10016,10017,10018,10019 Block 2 Ramas,Dunne,Smith,Olowski,Orlando O’Brian,Brown,Williams,Farriss,Smith Block 4 Nashville,Miami,Boston,Nashville,NULL Miami,NULL,Mobile,Opp,Nashville Block 5 TN,FL,MA,TN,NULL, FL,NULL,AL,AL,TN Block 3 10014,Orlando,Myron,NULL,NULL 10015,O’Brian,Amy,Miami,FL Block 3 Alfred,Leona,Kathy,Paul,Myron Amy,James,George,Anne,Olette Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. column-centric storage A physical data storage technique in which data is stored in blocks, which hold data from a single column across many rows. row-centric storage A physical data storage technique in which data is stored in blocks, which hold data from all columns of a given set of rows. 666 Part 4 Advanced Database Concepts one data block, as shown in Figure 14.9. Remember, in transactional systems, normal- ization is used to decompose complex data into related tables to reduce redundancy and to improve the speed of rapid manipulation of small sets of data. These manipulations tend to be row-oriented, so row-oriented storage works very well. However, in queries that retrieve a small set of columns across a large set of rows, a large number of disk accesses is required. For example, a query that wants to retrieve only the city and state of every customer will have to access every data block that contains a customer row to retrieve that data. In Figure 14.9, that would mean accessing five data blocks to get the city and state of every customer. A column-oriented or columnar database stores the data in blocks by column instead of by row. A single customer’s data will be spread across several blocks, but all of the data from a single column will be in just a few blocks. In Figure 14.9, all of the city data for customers will be stored together, just as all of the state data will be stored together. In that case, retrieving the city and state for every customer might require accessing only two data blocks. This type of column-centric storage works very well for databases that are primarily used to run queries over few columns but many rows, as is done in many reporting systems and data warehouses. Though Figure 14.9 shows only a few rows and data blocks, it is easy to imagine that the gains would be significant if the table size grew to millions or billions of rows across hundreds of thou- sands of data blocks. At the same time, column-centric storage would be very inefficient for processing transactions since insert, update, and delete activities would be very disk intensive. It is worth noting that column-centric storage can be achieved within rela- tional database technology, meaning that it still requires structured data and has the advantage of supporting SQL for queries. The other use of the term column-oriented database, also called column family data- base, is to describe a type of NoSQL database that takes the concept of column-centric storage beyond the confines of the relational model. As NoSQL databases, these prod- ucts do not require the data to conform to predefined structures nor do they support SQL for queries. This database model originated with Google’s BigTable product. Other column-oriented database products include HBase, described earlier, and Cassandra. Cassandra began as a project at Facebook, but Facebook released it to the open-source community, which has continued to develop Cassandra into one of the most popular column-oriented databases. A column family database is a NoSQL database that orga- nizes data in key-value pairs with keys mapped to a set of columns in the value compo- nent. While column family databases use many of the same terms as relational databases, the terms don’t mean quite the same things. Fortunately, the column family databases are conceptually simple and are conceptually close enough to the relational model that your understanding of the relational model can help you understand the column family model. A column is a key-value pair that is similar to a cell of data in a relational database. The key is the name of the column, and the value component is the data that is stored in that column. Therefore, “cus_lname: Ramas” is a column; cus_lname is the name of the column, and Ramas is the data value in the column. Similarly, “cus_city: Nashville” is another column, with cus_city as the column name and Nashville as the data value. Note rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Even though column family databases do not (yet) support standard SQL, Cassandra devel- opers have created a Cassandra query language (CQL). It is similar to SQL in many respects and is one of the more compelling reasons for adopting Cassandra. column family database A NoSQL database model that organizes data into key-value pairs, in which the value component is composed of a set of columns that vary by row. Copyright 2017 Cengage Lea Editorial review has deemed that As more columns are added, it becomes clear that some columns form natural groups, such as cus_fname, cus_lname, and cus_initial which would logically group together to form a customer’s name. Similarly, cus_street, cus_city, cus_state, and cus_zip would logically group together to form a customer’s address. These groupings are used to cre- ate super columns. A super column is a group of columns that are logically related. Recall the discussion in Chapter 4 about simple and composite attributes in the entity relationship model. In many cases, super columns can be thought of as the composite attribute and the columns that compose the super column as the simple attributes. Just as all simple attributes do not have to belong to a composite attribute, not all columns have to belong to a super column. Although this analogy is helpful in many contexts, it is not perfect. It is possible to group columns into a super column that logically belongs together for application processing reasons but does not conform to the relational idea of a composite attribute. Row keys are created to identify objects in the environment. All of the columns or super columns that describe these objects are grouped together to create a column family; therefore, a column family is conceptually similar to a table in the relational model. While a column family is similar in concept to a relational table, Figure 14.10 shows that it is structurally very different. Notice in Figure 14.10 that each row key in the column family can have different columns. Note Chapter 14 Big Data Analytics and NoSQL 667 A column family can be composed of columns or super columns, but it cannot contain both. FIGURE14.10 COLUMNFAMILYDATABASE Column Family Name Key Key Columns Key Columns CUSTOMERS Rowkey 1 Columns City Fname Lname State Rowkey 2 Nashville Alfred Ramas TN Balance 345.86 Fname Kathy Lname Smith Rowkey 3 Company Lname Local Markets, Inc. Dunne Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. super column In a column family database, a column that is composed of a group of other related columns. column family In a column family database, a collection of columns or super columns related to a collection of rows. 668 Part 4 Advanced Database Concepts 14-3d Graph Databases A graph database is a NoSQL database based on graph theory to store data about relationship-rich environments. Graph theory is a mathematical and computer science field that models relationships, or edges, between objects called nodes. Modeling and storing data about relationships is the focus of graph databases. Graph theory is a well-established field of study going back hundreds of years. As a result, creating a database model based on graph theory immediately provides a rich source for algorithms and applications that have helped graph databases gain in sophistication very quickly. Since it also happens that much of the data explosion over the last decade has involved data that is relationship-rich, graph databases have been poised to experience significant interest in the business environment. Interest in graph databases originated in the area of social networks. Social networks include a wide range of applications beyond the typical Facebook, Twitter, and Instagram that immediately come to mind. Dating websites, knowledge management, logistics and routing, master data management, and identity and access management are all areas that rely heavily on tracking complex relationships among objects. Of course, relational data- bases support relationships too. One of the great advances of the relational model was that relationships are easy to maintain. A relationship between a customer and an agent is as easy to implement in the relational model as adding a foreign key to create a com- mon attribute, and the customer and agent rows are related by having the same value in the common attributes. If the customer changes to a different agent, then simply chang- ing the value in the foreign key will change the relationship between the rows to maintain the integrity of the data. The relational model does all of these things very well. However, what if we want a “like” option so customers can “like” agents on our website? This would require a structural change to the database to add a new foreign key to support this sec- ond relationship. Next, what if the company wants to allow customers on its website to “friend” each other so a customer can see which agents their friends like, or the friends of their friends? In social networking data, there can be dozens of different relationships among individuals that need to be tracked, and often the relationships are tracked many layers deep (e.g., friends, friends of friends, friends of friends of friends, etc.). This results in a situation where the relationships become just as important as the data itself. This is the area where graph databases shine. The primary components of graph databases are nodes, edges, and properties, as shown in Figure 14.11. A node corresponds to the idea of a relational entity instance. The node is a specific instance of something we want to keep data about. Each node (circle) in Figure 14.10 represents a single agent. Properties are like attributes; they are the data that we need to store about the node. All agent nodes might have properties like first name and last name, but all nodes are not required to have the same properties. An edge is a relationship between nodes. Edges (shown as arrows in Figure 14.10) can be in one direction, or they can be bidirectional. For example, in Figure 14.11, the friends relationships are bidirectional, but the likes relationships are not. Note that edges can also have properties. In Figure 14.11 the date on which customer Alfred Ramas liked agent Alex Alby is recorded in the graph database. A query in a graph database is called a traversal. Instead of querying the database, the correct terminology would be traversing the graph. Graph databases excel at traversals that focus on relationships between nodes, such as shortest path and degree of connectedness. Graph database share some characteristics with other NoSQL databases in that graph databases do not force data to fit predefined structures, do not support SQL, and are opti- mized to provide velocity of processing, at least for relationship-intensive data. However, other key characteristics do not apply to graph databases. Graph databases do not scale rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. graph database A NoSQL database model based on graph theory that stores data on relationship-rich data as a collection of nodes and edges. node In a graph database, the representation of a single entity instance. edge In a graph database, the representation of a relationship between nodes. properties In a graph database, the attributes or characteristics of a node or edge that are of interest to the users. traversal A query in a graph database. Copyright 2017 Cengage Lea Editorial review has deemed that out very well to clusters. The other NoSQL database models achieve clustering efficiency by making each piece of data relatively independent. That allows a key-value pair to be stored on one node in the cluster without the DBMS needing to associate it with another key-value pair that may be on a different node on the cluster. The greater the number of nodes involved in a data operation, the greater the need for coordination and centralized control of resources. Separating independent pieces of data, often called shards, across nodes in the cluster is what allows NoSQL databases to scale out so effectively. Graph databases specialize in highly related data, not independent pieces of data. As a result, graph databases tend to perform best in centralized or lightly clustered environments, similar to relational databases. 14-3e NewSQL Databases Relational databases are the mainstay of organizational data, and NoSQL databases do not attempt to replace them for supporting line-of-business transactions. These trans- actions that support the day-to-day operations of business rely on ACID-compliant transactions and concurrency control, as discussed in Chapter 10. NoSQL databases (except graph databases that focus on specific relationship-rich domains) are concerned with the distribution of user-generated and machine-generated data over massive clus- ters. NewSQL databases try to bridge the gap between RDBMS and NoSQL. NewSQL databases attempt to provide ACID-compliant transactions over a highly distributed infrastructure. NewSQL databases are the latest technologies to appear in the data man- agement arena to address Big Data problems. As a new category of data management products, NewSQL databases have not yet developed a track record of success and have been adopted by relatively few organizations. NewSQL products, such as ClusterixDB and NuoDB, are designed from scratch as hybrid products that incorporate features of relational databases and NoSQL databases. NewSQL A database model that attempts to provide ACID-compliant transactions across a highly distributed infrastructure. Chapter 14 Big Data Analytics and NoSQL 669 FIGURE14.11 GRAPHDATABASEREPRESENTATION ID: 1 Type: agent Fname: Alex Lname: Alby Phone: 228-1249 ID: 102 Label: likes Date: 8/15/2012 ID: 100 Label: assists ID: 2 Type: agent Fname: Leah Lname: Hahn ID: 106 Label: likes Date: 9/15/2015 ID: 6 Type: customer Fname: Kathy Lname: Smith ID: 5 Type: customer Fname: Leona Lname: Dunne ID: 103 Label: friends ID: 7 Type: customer Fname: Paul Lname: Olowski Phone: 894-2180 ID: 4 Type: customer Fname: Alfred Lname: Ramas Amt: 100 Renew: 04/05/2017 ID: 3 Type: agent Fname: John Lname: Okon Phone: 123-5589 Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. ID: 101 Label: likes Date: 9/15/2015 ID: 105 Label: friends ID: 120 Label: assists ID: 111 Label: assists ID: 109 Label: likes Date: 1/07/2016 ID: 107 Label: likes Date: 3/20/2016 ID: 104 Label: likes Date: 10/11/2014 ID: 108 Label: friends 670 Part 4 Advanced Database Concepts data analytics A subset of business intelligence functionality that encompasses a wide range of mathematical, statistical, and modeling techniques with the purpose of extracting knowledge from data. explanatory analytics Data analysis that provides ways to discover relationships, trends, and patterns among data. predictive analytics Data analytics that use advanced statistical and modeling techniques to predict future business outcomes with great accuracy. Like RDBMS, NewSQL databases support: • SQL as the primary interface • ACID-compliant transactions Similar to NoSQL, NewSQL databases also support: • Highly distributed clusters • Key-value or column-oriented data stores As expected, no technology can perfectly provide the advantages of both RDBMS and NoSQL, so NewSQL has disadvantages (the CAP theorem still applies!). Principally, the disadvantages that have been discovered center around NewSQL’s heavy use of in-memory storage. Critics point to the fact that this can jeopardize the “durability” component of ACID. Further, the ability to handle vast data sets can be impacted by the reliance on in-memory structures because there are practical limits to the amount of data that can be held in mem- ory. Although in theory NewSQL databases should be able to scale out significantly, in prac- tice little has been done to scale beyond a few dozen data nodes. While this is a marked improvement over traditional RDBMS distribution, it is far from the hundreds of nodes used by NoSQL databases. Capturing data, in and of itself, is not the goal of data management. As discussed earlier, the data must add value to the organization. The data must help the organization to meet the needs of customers and provide value to shareholders. Data analysis is the process of turning the data into information that adds insights that enable data-based decisions. The next section will describe the complexity of that process. 14-4 Data Analytics Data analytics is a subset of business intelligence (BI) functionality that encompasses a wide range of mathematical, statistical, and modeling techniques with the purpose of extracting knowledge from data. Data analytics is used at all levels within the BI frame- work, including queries and reporting, monitoring and alerting, and data visualization. Hence, data analytics is a “shared” service that is crucial to what BI adds to an organiza- tion. Data analytics represents what business managers really want from BI: the ability to extract actionable business insight from current events and foresee future problems or opportunities. Data analytics discovers characteristics, relationships, dependencies, or trends in the organization’s data, and then explains the discoveries and predicts future events based on the discoveries. In practice, data analytics is better understood as a continuous spec- trum of knowledge acquisition that goes from discovery to explanation to prediction. The outcomes of data analytics then become part of the information framework on which decisions are built. Data analytics tools can be grouped into two separate (but closely related and often overlapping) areas: • Explanatory analytics focuses on discovering and explaining data characteristics and relationships based on existing data. Explanatory analytics uses statistical tools to formulate hypotheses, test them, and answer the how and why of such relationships— for example, how do past sales relate to previous customer promotions? • Predictive analytics focuses on predicting future data outcomes with a high degree of accuracy. Predictive analytics uses sophisticated statistical tools to help the end user create advanced models that answer questions about future data occurrences—for example, what would next month’s sales be based on a given customer promotion? Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. You can think of explanatory analytics as explaining the past and present, while predictive analytics forecasts the future. However, you need to understand that both sciences work together; predictive analytics uses explanatory analytics as a stepping stone to create predictive models. Data analytics has evolved over the years from simple statistical analysis of business data to dimensional analysis with OLAP tools, and then from data mining that discovers data patterns, relationships, and trends to its current status of predictive analytics. The next sections illustrate the basic characteristics of data mining and predictive analytics. 14-4a Data Mining Data mining refers to analyzing massive amounts of data to uncover hidden trends, pat- terns, and relationships; to form computer models to simulate and explain the findings; and then to use such models to support business decision making. In other words, data mining focuses on the discovery and explanation stages of knowledge acquisition. To put data mining in perspective, look at the pyramid in Figure 14.12, which rep- resents how knowledge is extracted from data. Data forms the pyramid base and rep- resents what most organizations collect in their operational databases. The second level contains information that represents the purified and processed data. Information forms the basis for decision making and business understanding. Knowledge is found at the pyramid’s apex and represents highly distilled information that provides concise, action- able business insight. Current-generation data-mining tools contain many design and application variations to fit specific business requirements. Depending on the problem domain, data-mining tools focus on market niches such as banking, insurance, marketing, retailing, finance, and health care. Within a given niche, data-mining tools can use certain algorithms that are implemented in different ways and applied over different data. Despite the lack of precise standards, data mining consists of four general phases: data mining A process that employs automated tools to analyze data in a data warehouse and other sources and to proactively identify possible relationships and anomalies. Chapter 14 Big Data Analytics and NoSQL 671 FIGURE14.12 EXTRACTINGKNOWLEDGEFROMDATA Processing High Low Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 672 Part 4 Advanced Database Concepts • Data preparation • Data analysis and classification • Knowledge acquisition • Prognosis In the data preparation phase, the main data sets to be used by the data-mining oper- ation are identified and cleansed of any data impurities. Because the data in the data warehouse is already integrated and filtered, the data warehouse usually is the target set for data-mining operations. The data analysis and classification phase studies the data to identify common data characteristics or patterns. During this phase, the data-mining tool applies specific algo- rithms to find: • Data groupings, classifications, clusters, or sequences • Data dependencies, links, or relationships • Data patterns, trends, and deviations The knowledge acquisition phase uses the results of the data analysis and classifica- tion phase. During the knowledge acquisition phase, the data-mining tool (with possible intervention by the end user) selects the appropriate modeling or knowledge acquisi- tion algorithms. The most common algorithms used in data mining are based on neural networks, decision trees, rules induction, genetic algorithms, classification and regres- sion trees, memory-based reasoning, and nearest neighbor. A data-mining tool may use many of these algorithms in any combination to generate a computer model that reflects the behavior of the target data set. Although many data-mining tools focus on the knowledge–discovery phase, others continue to the prognosis phase. In that phase, the data-mining findings are used to pre- dict future behavior and forecast business outcomes. Examples of data-mining findings can be: • Sixty-five percent of customers who did not use a particular credit card in the last six months are 88 percent likely to cancel that account. • Eighty-two percent of customers who bought a 42-inch or larger LCD TV are 90 per- cent likely to buy an entertainment center within the next four weeks. • If age < 30, income <= 25,000, credit rating < 3, and credit amount > 25,000, then the minimum loan term is 10 years.
The complete set of findings can be represented in a decision tree, a neural network, a forecasting model, or a visual presentation interface that is used to project future events or results. For example, the prognosis phase might project the likely outcome of a new product rollout or a new marketing promotion. Figure 14.13 illustrates the different phases of the data-mining process.
Because of the nature of the data-mining process, some findings might fall out- side the boundaries of what business managers expect. For example, a data-mining tool might find a close relationship between a customer’s favorite brand of soda and the brand of tires on the customer’s car. Clearly, that relationship might not be held in high regard among sales managers. (In regression analysis, those relationships are commonly described by the label “idiot correlation.”) Fortunately, data mining usu- ally yields more meaningful results. In fact, data mining has proven helpful in finding practical relationships among data that help define customer buying patterns, improve product development and acceptance, reduce health care fraud, analyze stock markets, and so on.
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Chapter 14 Big Data Analytics and NoSQL 673
FIGURE14.13 DATA-MININGPHASES
Data preparation phase
Data analysis and classification phase
Knowledge acquisition phase
Prognosis phase
• Identify data set • Clean data set
• Integrate data set
• Classification analysis
• Clustering and sequence analysis • Link analysis
• Trend and deviation analysis
• Select and apply algorithms • Neural networks
• Inductive logic
• Decision trees
• Clustering
• Regression tree
• Nearest neighbor • Visualization, etc.
• Modeling • Forecasting • Prediction
Data mining can be run in two modes:
• Guided. The end user guides the data-mining tool step by step to explore and explain known patterns or relationships. In this mode, the end user decides what techniques to apply to the data.
• Automated. In this mode, the end user sets up the data-mining tool to run automat- ically and uncover hidden patterns, trends, and relationships. The data-mining tool applies multiple techniques to find significant relationships.
As you learned in this section, data-mining methodologies focus on discovering and extracting information that describes and explains the data. For example, an explanatory model could create a customer profile that describes a given customer group. However, data mining can also be used as the basis to create advanced predictive data models. For example, a predictive model could be used to predict future customer behavior, such as a customer response to a target marketing campaign. The next section explains the use of predictive analytics in more detail.
14-4b Predictive Analytics
Although the term predictive analytics is used by many BI vendors to indicate many different levels of functionality, the promise of predictive analytics is very attractive for businesses looking for ways to improve their bottom line. Therefore, predictive analytics is receiving a lot of marketing buzz; vendors and businesses are dedicating extensive resources to this BI area. Predictive analytics refers to the use of advanced mathematical, statistical, and modeling tools to predict future business outcomes with high degrees of accuracy.
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

674 Part 4
Advanced Database Concepts
What is the difference between data mining and predictive analytics? As you learned earlier, data mining also has predictive capabilities. In fact, data mining and predictive analytics use similar and overlapping sets of tools, but with a slightly different focus. Data mining focuses on answering the “how” and “what” of past data, while predictive analyt- ics focuses on creating actionable models to predict future behaviors and events. In some ways, you can think of predictive analytics as the next logical step after data mining; once you understand your data, you can use the data to predict future behaviors. In fact, most BI vendors are dropping the term data mining and replacing it with the more alluring term predictive analytics.
The origins of predictive analytics can be traced back to the banking and credit card industries. The need to profile customers and predict customer buying patterns in these industries was a critical driving force for the evolution of many modeling methodologies used in BI data analytics today. For example, based on your demographic information and purchasing history, a credit card company can use data-mining models to determine what credit limit to offer, what offers you are more likely to accept, and when to send those offers.
Predictive analytics received a big stimulus with the advent of social media. Compa- nies turned to data mining and predictive analytics as a way to harvest the mountains of data stored on social media sites. Google was one of the first companies that offered tar- geted ads as a way to increase and personalize search experiences. Similar initiatives were used by all types of organizations to increase customer loyalty and drive up sales. Note the example of the airline and credit card industries and their frequent flyer and affinity card programs. Today, many organizations use predictive analytics to profile customers in an attempt to get and keep the right ones, which in turn will increase loyalty and sales.
Predictive analytics employs mathematical and statistical algorithms, neural networks, artificial intelligence, and other advanced modeling tools to create actionable predictive models based on available data. The algorithms used to build the predictive model are specific to certain types of problems and work with certain types of data. Therefore, it is important that the end user, who typically is trained in statistics and understands busi- ness, applies the proper algorithms to the problem in hand. However, thanks to constant technology advances, modern BI tools automatically apply multiple algorithms to find the optimum model.
Most predictive analytics models are used in areas such as customer relationships, customer service, customer retention, fraud detection, targeted marketing, and opti- mized pricing. Predictive analytics can add value to an organization in many different ways. For example, it can help optimize existing processes, identify hidden problems, and anticipate future problems or opportunities. However, predictive analytics is not the “secret sauce” to fix all business problems. Managers should carefully monitor and eval- uate the value of predictive analytics models to determine their return on investment.
In Chapter 13, you learned about data warehouses and star schemas to model and store decision support data. In this chapter, you have added to that by exploring the vast stores of data that organizations are collecting in unstructured formats and the technologies that make that data available to users. Data analytics is used to extract knowledge from all of these sources of data—NoSQL databases, Hadoop data stores, and data warehouses—to provide decision support to all organizational users.
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• Big Data is characterized by data of such volume, velocity, and/or variety that the rela- tional model struggles to adapt to it. Volume refers to the quantity of data that must be stored. Velocity refers to both the speed at which data is entering storage as well as the speed with which it must be processed. Variety refers to the lack of uniformity in the structure of the data being stored. As a result of Big Data, organizations are having to employ a variety of data storage solutions that include technologies in addition to relational databases, a situation referred to as polyglot persistence.
• Volume, velocity, and variety are collectively referred to as the 3 Vs of Big Data. How- ever, these are not the only characteristics of Big Data to which data administrators must be sensitive. Additional Vs that have been suggested by the data management industry include variability, veracity, value, and visualization. Variability is the varia- tion in the meaning of data that can occur over time. Veracity is the trustworthiness of the data. Value is concerned with whether or not the data is useful. Finally, visu- alization is the requirement that the data must be able to be presented in a manner that makes it comprehendible to decision makers. Most of these additional Vs are not unique to Big Data. They are also concerns for data in relational databases as well.
• The Hadoop framework has quickly emerged as a standard for the physical storage of Big Data. The primary components of the framework include the Hadoop Dis- tributed File System (HDFS) and MapReduce. HDFS is a coordinated technology for reliably distributing data over a very large cluster of commodity servers. MapReduce is a complementary process for distributing data processing across distributed data. One of the key concepts for MapReduce is to move the computations to the data instead of moving the data to the computations. MapReduce works by combining the functions of map, which distributes subtasks to the cluster servers that hold data to be processed, and reduce, which combines the map results into a single result set. The Hadoop framework also supports an entire ecosystem of additional tools and technol- ogies, such as Hive, Pig, and Flume that work together to produce a complex system of Big Data processing.
• NoSQL is a broad term to refer to any of several nonrelational database approaches to data management. Most NoSQL databases fall into one of four categories: key-value databases, document databases, column-oriented databases, or graph databases. Due to the wide variability of products under the NoSQL umbrella, these categories are not necessarily all-encompassing, and many products can fit into multiple categories.
• Key-value databases store data in key-value pairs. In a key-value pair, the value of the key must be known to the DBMS, but the data in the value component can be of any type, and the DBMS makes no attempt to understand the meaning of the data in it. These types of databases are very fast when the data is completely independent, and the application programs can be relied on to understand the meaning of the data.
• Document databases also store data in key-value pairs, but the data in the value com- ponent is an encoded document. The document must be encoded using tags, such as in XML or JSON. The DBMS is aware of the tags in the documents, which makes querying on tags possible. Document databases expect documents to be self-contained and relatively independent of each other.
• Column-oriented databases, also called column family databases, organize data into key-value pairs in which the value component is composed of a series of columns, which are themselves key-value pairs. Columns can be grouped into super columns, similar to a composite attribute in the relational model being composed of simple
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Chapter 14 Big Data Analytics and NoSQL 675
Summary

Key Terms
algorithm
batch processing
block report
BSON (Binary JSON) bucket
column family
column family database column-centric storage data analytics
data mining
document database edge
explanatory analytics feedback loop processing graph database
Hadoop Distributed File System (HDFS)
heartbeat
job tracker
JSON (JavaScript Object Notation)
key-value (KV) database map
MapReduce
mapper
NewSQL
node
NoSQL
polyglot persistence predictive analytics properties
reduce
reducer row-centric storage
scaling out
scaling up sentiment analysis stream processing structured data super column
task tracker traversal unstructured data value
variability variety velocity veracity visualization volume
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attributes. All objects of a similar type are identified as rows, given a row key, and placed within a column family. Rows within a column family are not required to have the same structure, that is, they are not required to have the same columns.
• Graph databases are based on graph theory and represent data through nodes, edges, and properties. A node is similar to an instance of an entity in the relational model. Edges are the relationships between nodes. Both nodes and edges can have properties, which are attributes that describe the corresponding node or edge. Graph databases excel at tracking data that is highly interrelated, such as social media data. Due to the many relationships among the nodes, it is difficult to distribute a graph database across a cluster in a highly-distributed manner.
• NewSQL databases attempt to integrate features of both RDBMS (providing ACID-compliant transactions) and NoSQL databases (using a highly distributed infrastructure).
• Data analytics is a subset of BI functionality that provides advanced data analysis tools to extract knowledge from business data. Data analytics can be divided into explanatory and predictive analytics. Explanatory analytics focuses on discovering and explaining data characteristics and relationships. Predictive analytics focuses on creating models to predict future outcomes or events based on the existing data.
• Data mining automates the analysis of operational data to find previously unknown data characteristics, relationships, dependencies, and trends. The data-mining pro- cess has four phases: data preparation, data analysis and classification, knowledge acquisition, and prognosis.
• Predictive analytics uses the information generated in the data-mining phase to create advanced predictive models with high degrees of accuracy.
Online
Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
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Chapter 14 Big Data Analytics and NoSQL 677
Review Questions
1. What is Big Data? Give a brief definition.
2. What are the traditional 3 Vs of Big Data? Briefly, define each.
3. Explain why companies like Google and Amazon were among the first to address the Big Data problem.
4. Explain the difference between scaling up and scaling out.
5. What is stream processing, and why is it sometimes necessary?
6. How is stream processing different from feedback loop processing?
7. Explain why veracity, value, and visualization can also be said to apply to relational databases as well as Big Data.
8. What is polyglot persistence, and why is it considered a new approach?
9. What are the key assumptions made by the Hadoop Distributed File System approach?
10. What is the difference between a name node and a data node in HDFS?
11. Explain the basic steps in MapReduce processing.
12. Briefly explain how HDFS and MapReduce are complementary to each other.
13. What are the four basic categories of NoSQL databases?
14. How are the value components of a key-value database and a document database different?
15. Brieflyexplainthedifferencebetweenrow-centricandcolumn-centricdatastorage.
16. What is the difference between a column and a super column in a column family database?
17. Explain why graph databases tend to struggle with scaling out.
18. What is data analytics? Briefly define explanatory and predictive analytics.
19. Describeandcontrastthefocusofdataminingandpredictiveanalytics.Givesome examples.
20. Howdoesdataminingwork?Discussthedifferentphasesinthedataminingprocess.
21. Describe the characteristics of predictive analytics. What is the impact of Big Data in predictive analytics?
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PART 5
Databases and the Internet
15 Database Connectivity and Web Technologies
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Chapter 15
Database Connectivity and Web Technologies
In this chapter, you will learn:
• About database connectivity fundamentals
• About various database connectivity technologies: ODBC, OLE, ADO.NET, JDBC
• How web-to-database middleware is used to integrate databases with the Internet
• What services are provided by web application servers
• What Extensible Markup Language (XML) is and why it is important for web database development • About cloud computing and how it enables the database-as-a-service model
Preview
Databases are the central repository for critical data generated by business applications, including newer channels such as the web and mobile devices. For businesses to remain competitive, such data must be readily available, anywhere and anytime, to all business users and in all types of formats: a desktop spreadsheet, a Visual Basic application, a web front end, and using newer technologies such as smartphones and tablets. In this chapter, you will learn about various architectures used to connect applications to databases.
The Internet has changed how organizations of all types operate. Buying goods and services via the Internet has become commonplace. This chapter examines the funda- mentals of web database technologies used to open databases to the Internet. In today’s environment, interconnectivity occurs not only between an application and the database but between applications exchanging messages and data. Extensible Markup Language (XML) provides a standard way of exchanging unstructured and structured data between applications.
Companies that want to integrate database and web technologies within their applica- tions portfolio can now choose from a range of Internet-based services. Therefore, you will learn how organizations can benefit from cloud computing by leveraging the data- base-as-a-service model within their IT environments. These cloud-based services offer a quick and cost-efficient way to provide new business services.
Data Files and Available Formats
CH15_Orderdb P P P P
Data Files Available on cengagebrain.com
MS Access
Oracle
MS SQL
My SQL
MS Access
Oracle
MS SQL
My SQL
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15-1 Database Connectivity
Database connectivity refers to the mechanisms through which application programs connect and communicate with data repositories. Databases store data in persistent stor- age structures so it can be retrieved at a later time for processing. As you already learned, the database management system (DBMS) functions as an intermediary between the data (stored in the database) and the end-user’s applications. Before learning about the various data connectivity options, it is important to review some important fundamen- tals you have learned in this book:
• DBMSs provide means to interact with the data in their databases. This could be in the form of administrative tools and data manipulation tools. DBMSs also provide a proprietary way for external application programs to connect to the database by the means of an application programing interface. See Chapter 1, Database Systems.
• Modern DBMSs have the option to store data locally or distributed in multiple loca- tions. Locally stored data resides in the same processing host as the DBMS. A dis- tributed database stores data in multiple geographically distributed nodes with data management capability. See Chapter 12, Distributed Database Management Systems.
• The database connectivity software we discuss in this chapter supports Structured Query Language (SQL) as the standard data manipulation language. However, depending on the type of database model, some database connectivity interfaces may support other proprietary data manipulation languages.
• Database connectivity software works in a client/server architecture, by which pro- cessing tasks are split among multiple software layers. In this model, the multiple layers exchange control messages and data. See Chapter 12 and Appendix F, Client/ Server Systems, for more information on this topic.
To better understand database connectivity software, we use client/server concepts in which an application is broken down in interconnected functional layers. In the case of database connectivity software, you could break down its basic functionality into three broad layers:
1. A data layer where the data resides. You could think of this layer as the actual data repository interface. This layer resides closest to the database itself and normally is provided by the DBMS vendor.
2. A middle layer that manages multiple connectivity and data transformation issues. This layer is in charge of dealing with data logic issues, data transformations, ways to “talk” to the database below it, and so on. This would also include translating multiple data manipulation languages to the native language supported by the specific data repository.
3. A top layer that interfaces with the actual external application. This mostly comes in the form of an application programming interface that publishes specific protocols for the external programs to interact with the data.
From the previous discussion, you can understand why the database connectivity
software is also known as database middleware—because it provides an interface between the application program and the database or data repository. The data reposi- tory, also known as the data source, represents the data management application, such as Oracle, SQL Server, IBM DB2, or NoSQL that will be used to store the data generated by the application program. Ideally, a data source or data repository could be located any- where and hold any type of data. Furthermore, the same database connectivity middle- ware could support multiple data sources at the same time. For example, the data source could be a relational database, a NoSQL database, a spreadsheet, a MS Access database,
database middleware Database connectivity software through which application programs connect and communicate with data repositories.
Chapter 15 Database Connectivity and Web Technologies 681
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682 Part 5
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or a text data file. This multi-data-source type capability is based on the support of well-established data access standards.
The need for standard database connectivity interfaces cannot be overstated. Just as SQL has become the de facto data manipulation language, a standard database connec- tivity interface is necessary for enabling applications to connect to data repositories. Although there are many ways to achieve database connectivity, this section covers only the following interfaces:
• Native SQL connectivity (vendor provided)
• Microsoft’s Open Database Connectivity (ODBC), Data Access Objects (DAO), and
Remote Data Objects (RDO)
• Microsoft’s Object Linking and Embedding for Database (OLE-DB)
• Microsoft’s ActiveX Data Objects (ADO.NET)
• Oracle’s Java Database Connectivity (JDBC)
The data connectivity interfaces illustrated here are dominant players in the market, and more importantly, they enjoy the support of most database vendors. In fact, ODBC, OLE-DB, and ADO.NET form the backbone of Microsoft’s Universal Data Access (UDA) architecture, a collection of technologies used to access any type of data source and manage the data through a common interface. As you will see, Microsoft’s database connectivity interfaces have evolved over time: each interface builds on top of the other, thus providing enhanced functionality, features, flexibility, and support.
15-1a Native SQL Connectivity
Most DBMS vendors provide their own methods for connecting to their databases. Native SQL connectivity refers to the connection interface that is provided by the database ven- dor and is unique to that vendor. The best example of this type of native interface is the Oracle RDBMS. To connect a client application to an Oracle database, you must install and configure Oracle’s SQL*Net interface on the client computer. Figure 15.1 shows the configuration of the Oracle SQL*Net interface on the client computer.
Native database connectivity interfaces are optimized for “their” DBMS, and those interfaces support access to most or all of the database features. However, maintaining
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
FIGURE15.1 ORACLENATIVECONNECTIVITY
Universal Data Access (UDA)
Within the Microsoft application framework, a collection of technologies used to access any type of data source and to manage the data through a common interface.
Copyright 2017 Cengage Lea Editorial review has deemed that

multiple native interfaces for different databases can become a burden for the program- mer. Therefore, the need for universal database connectivity arises. Usually, the native database connectivity interface provided by the vendor is not the only way to connect to a database; most current DBMS products support other database connectivity standards, the most common being ODBC.
15-1b ODBC, DAO, and RDO
Developed in the early 1990s, Open Database Connectivity (ODBC) is Microsoft’s imple- mentation of a superset of the SQL Access Group Call Level Interface (CLI) standard for database access. ODBC is probably the most widely supported database connectivity inter- face. ODBC allows any Windows application to access relational data sources, using SQL via a standard application programming interface (API). The Webopedia online dictionary (www.webopedia.com) defines an API as “a set of routines, protocols, and tools for building software applications.” A good API makes it easy to develop a program by providing all of the building blocks; the programmer puts the blocks together. Most operating environ- ments, such as Windows, provide an API so that programmers can write applications con- sistent with the operating environment. Although APIs are designed for programmers, they are ultimately good for users because they guarantee that all programs using a common API will have similar interfaces. That makes it easy for users to learn new programs.
ODBC was the first widely adopted database middleware standard, and it enjoyed rapid adoption in Windows applications. As programming languages evolved, ODBC did not provide significant functionality beyond the ability to execute SQL to manipu- late relational-style data. Therefore, programmers needed a better way to access data. To answer that need, Microsoft developed two other data access interfaces:
• Data Access Objects (DAO) is an object-oriented API used to access desktop data- bases, such as MS Access and FileMaker Pro. DAO provides an optimized interface that exposes programmers to the functionality of the Jet data engine, on which MS Access is based. The DAO interface can also be used to access other relational-style data sources.
• Remote Data Objects (RDO) is a higher-level, object-oriented application interface used to access remote database servers. RDO uses the lower-level DAO and ODBC for direct access to databases. RDO is optimized to deal with server-based databases such as MS SQL Server, Oracle, and DB2.
Figure 15.2 illustrates how Windows applications can use ODBC, DAO, and RDO to access local and remote relational data sources.
The DAO and RDO object interfaces provide more functionality than ODBC. DAO and RDO make use of the underlying ODBC data services. ODBC, DAO, and RDO are implemented as shared code that is dynamically linked to the Windows operating environment through dynamic-link libraries (DLLs), which are stored as files with a .dll extension. Running as a DLL, the code speeds up load and run times.
The basic ODBC architecture has three main components:
• A high-level ODBC API through which application programs access ODBC functionality
• A driver manager that is in charge of managing all database connections
• An ODBC driver that communicates directly to the DBMS
Defining a data source is the first step in using ODBC. To define a data source, you must create a data source name (DSN) for it. To create a DSN, you need to provide the following:
Open Database Connectivity (ODBC) Microsoft database middleware that provides a database access API to Windows applications.
Call Level Interface (CLI)
A standard developed by the SQL Access Group for database access.
application programming interface (API) Software through which programmers interact with middleware. An API allows the use of generic SQL code, thereby allowing client processes to be database server- independent.
Data Access Objects (DAO)
An object-oriented application programming interface used to access MS Access, FileMaker
Pro, and other Jet-based databases.
Remote Data Objects (RDO)
A higher-level, object- oriented application interface used to access remote database servers. RDO uses the lower-level DAO and ODBC for direct access to databases.
dynamic-link library (DLL)
Shared code module that is treated as part of the operating system or server process so it can be dynamically invoked at run time.
data source name (DSN)
A name that identifies and defines an ODBC data source.
Chapter 15 Database Connectivity and Web Technologies 683
•
An ODBC driver. You must identify the driver to use to connect to the data source. The ODBC driver is normally provided by the database vendor, although Microsoft provides several drivers that connect to most common databases. For example, if you
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684 Part 5
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FIGURE15.2 USINGODBC,DAO,ANDRDOTOACCESSDATABASES
MS Word
MS Access
MS Excel
RDO
DAO Jet Engine
Client Applications
ODBC API ODBC Driver Manager ODBC Database Driver
Remote Data Objects
Data Access Objects
Jet Engine supports MS Access databases and other SQL-aware data sources.
Database vendors provide ODBC database drivers so Windows applications can access their
respective databases.
Oracle Driver
Oracle
MS SQL Driver
MS SQL
ODBC Driver
Access
are using an Oracle DBMS, you would select the Oracle ODBC driver provided by Oracle. Or, you could instead select the Microsoft-provided ODBC driver for Oracle.
• A name. This is a unique name by which the data source will be known to ODBC, and therefore to applications. ODBC offers two types of data sources: user and system. User data sources are available only to the user. System data sources are available to all users, including operating system services.
• ODBC driver parameters. Most ODBC drivers require specific parameters to establish a connection to the database. For example, if you are using an MS Access database, you must point to the location of the MS Access file and then provide a username and password if necessary. If you are using a DBMS server, you must provide the server name, the database name, the username, and the password needed to connect to the database. Figure 15.3 shows the ODBC screens required to create a system ODBC data source for an Oracle DBMS. Note that some ODBC drivers use the native driver provided by the DBMS vendor.
Once the ODBC data source is defined, application programmers can write to the ODBC API by issuing specific commands and providing the required parameters. The ODBC Driver Manager will properly route the calls to the appropriate data source.
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Chapter 15 Database Connectivity and Web Technologies 685
FIGURE15.3 CONFIGURINGANORACLEODBCDATASOURCE
Defining an ODBC
system data source name (DSN) to connect to an Oracle DBMS, using Oracle ODBC Driver
Oracle ODBC Driver uses the native Oracle SQL connectivity.
If no user ID is provided, ODBC will prompt for the user ID and password at run time.
The ODBC API standard defines three levels of compliance: Core, Level-1, and Level-2, which provide increasing levels of functionality. For example, Level-1 might provide sup- port for most SQL DDL and DML statements, including subqueries and aggregate func- tions, but not for procedural SQL or cursors. The database vendors can choose which level to support. However, to interact with ODBC, the database vendor must implement all of the features in the specified ODBC API support level.
Figure 15.4 shows how you could use MS Excel to retrieve data from an Oracle RDBMS using ODBC. Because much of the functionality provided by these interfaces is oriented toward accessing relational data sources, the use of the interfaces was limited with other data source types. With the advent of object-oriented programming languages, it has become more important to provide access to other nonrelational data sources.
15-1c OLE-DB
Although ODBC, DAO, and RDO are used, they do not provide support for nonrela- tional data. To answer that need and to simplify data connectivity, Microsoft developed Object Linking and Embedding for Database (OLE-DB). Based on Microsoft’s Com- ponent Object Model (COM), OLE-DB is database middleware that adds object-oriented functionality for access to relational and nonrelational data. OLE-DB was the first part of Microsoft’s strategy to provide a unified object-oriented framework for the develop- ment of next-generation applications.
OLE-DB is composed of a series of COM objects that provide low-level database connectivity for applications. Because OLE-DB is based on COM, the objects contain data and methods, also known as the interface. The OLE-DB model is better understood when you divide its functionality into two types of objects:
Object Linking and Embedding for Database (OLE-DB) Based on Microsoft’s Component Object Model (COM), OLE-DB
is database middleware that adds object- oriented functionality for accessing relational and nonrelational data.
•
Consumers are objects (applications or processes) that request and use data. Consum- ers request data by invoking the methods exposed by the data provider objects (public interface) and passing the required parameters.
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686 Part 5
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FIGURE15.4 MSEXCELUSESODBCTOCONNECTTOANORACLEDATABASE
CLIENT APPLICATION
1
34
6
8
ODBC Interface
2
5
DATABASE SERVER COMPUTER
RDBMS SERVER
DATABASE
7
9
ODBC API
ODBC DRIVER MGR
ODBC DRIVER
1. From Excel, click the Data Tab, under Get External Data, select the From Other Sources and From Microsoft Query options to retrieve data from an Oracle RDBMS.
2. Select the Gradora ODBC data source.
3. Enter the authentication parameters. ODBC uses the connection
parameters to connect to the data source. Click OK. The first time,
all tables to which the user has access are listed.
4. To limit to only tables owned by the user, click on Options and
choose the user name from the Owner drop down list.
5. Select the table and columns to use in the query.
6. Select filtering options to restrict the rows returned.
7. Select sorting options to order the rows.
8. Select Return Data to Microsoft Office Excel.
9. Select how you want to view the data and where you want it
placed in your Excel workbook.
10. Excel uses the ODBC API to pass the SQL request down to the
database. Oracle executes the request and generates a result set. Excel issues calls to the ODBC API to retrieve the result set and populate the spreadsheet.
10
• Providers are objects that manage the connection with a data source and provide data to the consumers. Providers are divided into two categories: data providers and ser- vice providers.
– Data providers provide data to other processes. Database vendors create data pro- vider objects that expose the functionality of the underlying data source (relational, object-oriented, text, and so on).
– Service providers provide additional functionality to consumers. The service provider is located between the data provider and the consumer. The service provider requests data from the data provider, transforms the data, and then provides the transformed data to the data consumer. In other words, the service provider acts like a data con- sumer of the data provider and as a data provider for the data consumer (end-user application). For example, a service provider could offer cursor management services, transaction management services, query processing services, and indexing services.
As a common practice, many vendors provide OLE-DB objects to augment their ODBC support, effectively creating a shared object layer on top of their existing database connectivity (ODBC or native) through which applications can interact. The OLE-DB objects expose functionality about the database; for example, there are objects that deal with relational data, hierarchical data, and flat-file text data. Additionally, the objects
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implement specific tasks, such as establishing a connection, executing a query, invoking a stored procedure, defining a transaction, or invoking an OLAP function. By using OLE-DB objects, the database vendor can choose what functionality to implement in a modular way, instead of being forced to include all of the functionality all of the time. Table 15.1 shows a sample of the object-oriented classes used by OLE-DB and some of the methods (interfaces) exposed by the objects.
Chapter 15 Database Connectivity and Web Technologies 687
TABLE 15.1
SAMPLE OLE-DB CLASSES AND INTERFACES
OBJECT CLASS
USAGE
SAMPLE INTERFACES
Session
Used to create an OLE-DB session between a data consumer application and a data provider
IGetDataSource
Command
Used to process commands to manipulate
a data provider’s data; generally, the command object will create RowSet objects to hold the data returned by a data provider
ICommandPrepare
RowSet
Used to hold the result set returned by a relational-style database or a database that supports SQL; represents a collection of rows in a tabular format
IRowsetInfo IRowsetFind IRowsetScroll
OLE-DB provides additional capabilities for the applications accessing the data. However, it does not provide support for scripting languages, especially the ones used for web develop- ment, such as Active Server Pages (ASP) and ActiveX. (A script is written in a programming language that is not compiled but is interpreted and executed at run time.) To provide that support, Microsoft developed a new object framework called ActiveX Data Objects (ADO), which provides a high-level, application-oriented interface to interact with OLE-DB, DAO, and RDO. ADO provides a unified interface to access data from any programming language that uses the underlying OLE-DB objects. Figure 15.5 illustrates the ADO/OLE-DB architec- ture and how it interacts with ODBC and native connectivity options.
ADO introduced a simpler object model that was composed of only a few interacting objects to provide the data manipulation services required by the applications. Sample objects in ADO are shown in Table 15.2.
Although the ADO model is a tremendous improvement over the OLE-DB model, Microsoft is actively encouraging programmers to use its newer data access framework, ADO.NET.
15-1d ADO.NET
Based on ADO, ADO.NET is the data access component of Microsoft’s .NET applica- tion development framework. The Microsoft .NET framework is a component-based platform for developing distributed, heterogeneous, interoperable applications aimed at manipulating any type of data using any combination of network, operating system, and programming language. Comprehensive coverage of the .NET framework is beyond the scope of this book. Therefore, this section only introduces the basic data access compo- nent of the .NET architecture, ADO.NET.
It is important to understand that the .NET framework extends and enhances the func- tionality provided by the ADO/OLE-DB duo. ADO.NET introduced two new features that are critical for the development of distributed applications: DataSets and XML support.
script
A programming language that is
not compiled, but
is interpreted and executed at run time.
ActiveX Data Objects (ADO)
A Microsoft object framework that provides a high-level, application- oriented interface to OLE-DB, DAO, and RDO. ADO provides a unified interface to access data from any programming language that uses the underlying OLE-DB objects.
ADO.NET
The data access component of Microsoft’s .NET application development framework.
Microsoft .NET framework
A component-
based platform for
the development
of distributed, heterogeneous, interoperable applications aimed
at manipulating any type of data over any network regardless of operating system and programming language.
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688 Part 5
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FIGURE15.5 OLE-DBARCHITECTURE
Client Applications
Access Excel Visual C++
ActiveX Data Objects (ADO)
OLE-DB Service Providers
E-Mail Indexing Cursor Processing Processing Processing
OLE-DB Consumers
OLE-DB Provider for Oracle
SQL*NET
DATABASE
OLE-DB Provider for Exchange
EMAIL
OLE-DB Provider for SQL Server
SQL Server
OLE-DB Provider for ODBC
ODBC
DATABASE
OLE-DB Data Providers
Query Processing
TABLE 15.2
SAMPLE ADO OBJECTS
OBJECT CLASS
USAGE
Connection
Used to set up and establish a connection with a data source. ADO will connect to any OLE-DB data source. The data source can be of any type.
Command
Used to execute commands against a specific connection (data source)
Recordset
Contains the data generated by the execution of a command. It will also contain any new data to be written to the data source. The Recordset can be disconnected from the data source.
Fields
Contains a collection of field descriptions for each column in the Recordset
To understand the importance of this new model, you should know that a DataSet is a disconnected, memory-resident representation of the database. That is, the DataSet contains tables, columns, rows, relationships, and constraints. Once the data is read from a data provider, it is placed in a memory-resident DataSet, which is then disconnected from the data provider. The data consumer application interacts with the data in the DataSet object to make inserts, updates, and deletes in the DataSet. Once the processing is done, the DataSet data is synchronized with the data source and the changes are made permanent.
arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
DataSet
In ADO.NET, a disconnected, memory- resident representation of the database. The DataSet contains tables, columns, rows, relationships, and constraints.
Copyright 2017 Cengage Le Editorial review has deemed that

The DataSet is internally stored in XML format, and the data in the DataSet can be made persistent as XML documents. This is critical in today’s distributed environments. You can think of the DataSet as an XML-based, in-memory database that represents the persistent data stored in the data source. (You will learn about XML later in this chapter.)
Figure 15.6 illustrates the main components of the ADO.NET object model.
The ADO.NET framework consolidates all data access functionality under one integrated object model. In this object model, several objects interact with one another to perform specific data manipulations. These objects can be grouped as data providers and consumers.
Data provider objects are provided by the database vendors. However, ADO.NET comes with two standard data providers: one for OLE-DB data sources and one for SQL Server. That way, ADO.NET can work with any previously supported database, includ- ing an ODBC database with an OLE-DB data provider. At the same time, ADO.NET includes a highly optimized data provider for SQL Server.
Whatever the data provider is, it must support a set of specific objects to manipulate the data in the data source. Some of those objects are shown in Figure 15.6. A brief description of the objects follows.
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Chapter 15 Database Connectivity and Web Technologies 689
FIGURE15.6 ADO.NETFRAMEWORK
Data Consumers
Client Applications
Excel Internet
ADO.NET DataSet (XML)
DataTableCollection DataTable
DataColumnCollection DataRowCollection ConstraintCollection
DataRelationCollection
Data Providers DataAdapter
DataReader Command Connection
OLE-DB
DATABASE
Access

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• Connection. The Connection object defines the data source used, the name of the server, the database, and so on. This object enables the client application to open and close a connection to a database.
• Command. The Command object represents a database command to be executed within a specified database connection. This object contains the actual SQL code or a stored procedure call to be run by the database. When a SELECT statement is exe- cuted, the Command object returns a set of rows and columns.
• DataReader. The DataReader object is a specialized object that creates a read-only session with the database to retrieve data sequentially (forward only) and very quickly.
• DataAdapter. The DataAdapter object is in charge of managing a DataSet object, and it is the most specialized object in the ADO.NET framework. The DataAdapter object contains the following objects that aid in managing the data in the DataSet: Select- Command, InsertCommand, UpdateCommand, and DeleteCommand. The Data- Adapter object uses these objects to populate and synchronize the data in the DataSet with the permanent data source data.
• DataSet. The DataSet object is the in-memory representation of the data in the data- base. This object contains two main objects. The DataTableCollection object contains a collection of DataTable objects that make up the “in-memory” database, and the DataRelationCollection object contains a collection of objects that describe the data relationships and ways to associate one row in a table to the related row in another table.
• DataTable. The DataTable object represents the data in tabular format. This object has one very important property: PrimaryKey, which allows the enforcement of entity integrity. In turn, the DataTable object is composed of three main objects:
– DataColumnCollection contains one or more column descriptions. Each column description has properties such as column name, data type, nulls allowed, maxi- mum value, and minimum value.
– DataRowCollection contains zero rows, one row, or more than one row with data as described in the DataColumnCollection.
– ConstraintCollection contains the definition of the constraints for the table. Two types of constraints are supported: ForeignKeyConstraint and UniqueConstraint.
As you can see, a DataSet is a simple database with tables, rows, and constraints. Even more importantly, the DataSet does not require a permanent connection to the data source. The DataAdapter uses the SelectCommand object to populate the DataSet from a data source. However, once the DataSet is populated, it is completely independent of the data source, which is why it is called disconnected.
Additionally, DataTable objects in a DataSet can come from different data sources. This means that you could have an EMPLOYEE table in an Oracle database and a SALES table in a SQL Server database. You could then create a DataSet that relates both tables as though they were in the same database. In short, the DataSet object paves the way for truly heterogeneous, distributed database support within applications.
The ADO.NET framework is optimized to work in disconnected environments. In a disconnected environment, applications exchange messages in request/reply format. The most common example of a disconnected system is the Internet. Mod- ern applications rely on the Internet as the network platform and on the web browser as the graphical user interface. In later sections, you will learn about how Internet databases work.
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Chapter 15 Database Connectivity and Web Technologies 691 15-1e Java Database Connectivity (JDBC)
Java is an object-oriented programming language developed by Sun Microsystems (acquired by Oracle in 2010) that runs on top of web browser software. Java is one of the most-common programming languages for web development. Sun Microsystems cre- ated Java as a “write once, run anywhere” environment, which means that a programmer can write a Java application once and then run it in multiple environments without any modification. The cross-platform capabilities of Java are based on its portable architec- ture. Java code is normally stored in preprocessed “chunks” known as applets that run in a virtual machine environment in the host operating system. This environment has well-defined boundaries, and all interactivity with the host operating system is closely monitored. Java run-time environments are available for most operating systems, from computers to handheld mobile devices to TV set-top boxes. Another advantage of using Java is its “on-demand” architecture. When a Java application loads, it can dynamically download all its modules or required components via the Internet.
When Java applications need to access data outside the Java runtime environment, they use predefined application programming interfaces. Java Database Connectivity (JDBC) is an application programming interface that allows a Java program to interact with a wide range of data sources, including relational databases, tabular data sources, spreadsheets, and text files. JDBC allows a Java program to establish a connection with a data source, prepare and send the SQL code to the database server, and process the result set.
One main advantage of JDBC is that it allows a company to leverage its existing invest- ment in technology and personnel training. JDBC allows programmers to use their SQL skills to manipulate the data in the company’s databases. As a matter of fact, JDBC allows direct access to a database server or access via database middleware. Furthermore, JDBC provides a way to connect to databases through an ODBC driver. Figure 15.7 illustrates the basic JDBC architecture and the various database access styles.
Java
An object-oriented programming language developed by Sun Microsystems that
runs on top of the
web browser software. Java applications are compiled and stored on the web server. Java’s main advantage is its ability to let application developers create their applications once and then run them in many environments.
Java Database Connectivity (JDBC) An application programming interface that allows a Java program to interact with a wide range of data sources, including relational databases, tabular data sources, spreadsheets, and text files.
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FIGURE15.7 JDBCARCHITECTURE
Java DB Driver
Java Client Application
JDBC API
JDBC Driver Manager
Java DB Driver
Database Middleware
JDBC-ODBC Bridge Driver
ODBC
DATABASE
DATABASE DATABASE
DATABASE

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As you see in Figure 15.7, the database access architecture in JDBC is very similar to the ODBC/OLE/ADO.NET architecture. All database access middleware shares similar components and functionality. One advantage of JDBC over other middleware is that it requires no configuration on the client side. The JDBC driver is automatically downloaded and installed as part of the Java applet download. Because Java is a web-based technol- ogy, applications can connect to a database directly using a simple URL. Once the URL is invoked, the Java architecture comes into play, the necessary applets are downloaded to the client (including the JDBC database driver and all configuration information), and then the applets are executed securely in the client’s runtime environment. This framework is used successfully in many vertical database markets, in particular in the rapidly growing data analytics market, where open source players like Hadoop and MapReduce provide end-users with advanced application programming interfaces to high-performance data analytics functions using large-scale clusters of interconnected data stores.
Every day, more and more companies are investing resources to develop and expand their web presence and are finding ways to do more business on the Internet. Such busi- ness generates increasing amounts of data to be stored in databases. Java and the .NET framework are part of the trend toward increasing reliance on the Internet as a critical business resource. In fact, the Internet has become a major development platform for most businesses. In the next section, you will learn more about Internet databases and how they are used.
15-2 Database Internet Connectivity
Millions of people all over the world access the Internet and connect to databases via web browsers or data services. For example, they can use a smartphone app to get weather forecasts, stock prices, driving directions, concert tickets, or music downloads. Internet database connectivity opens the door to new, innovative services that do the following:
• Permit rapid responses to competitive pressures by bringing new services and prod- ucts to market quickly.
• Increase customer satisfaction through the creation of innovative data services such as mapping data combined with GPS (Global Positioning System) information to pro- vide location-aware services. These applications present end users with information or services located near the users’ current location.
• Allow anywhere, anytime data access using mobile smart devices via the Internet.
• Yield fast and effective information dissemination through universal access from
across the street or across the globe.
Given these advantages, many organizations rely on their IT departments to create universal data access architectures based on Internet standards. Table 15.3 shows a sam- ple of Internet technology characteristics and the benefits they provide.
As you will learn in the following sections, database application development— particularly the creation and management of user interfaces and database connectivity—is profoundly affected by the web. However, having a web-based database interface does not negate the design and implementation issues that were addressed in the previous chapters. In the final analysis, whether you make a purchase by going online or by stand- ing in line, the system-level transaction details are essentially the same, and they require the same basic database structures and relationships. If any immediate lesson is to be learned, it is this: The effects of bad database design, implementation, and management are magnified in an environment in which transactions might be measured in hundreds of thousands per day rather than hundreds.
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The simplicity of the web’s interface and its cross-platform functionality are at the core of its success as a data access platform. In fact, the web has helped create a new infor- mation dissemination standard. The following sections examine how web-to-database middleware enables end users to interact with databases over the web.
15-2a Web-to-DatabaseMiddleware:Server-SideExtensions
In general, the web server is the main hub through which all Internet services are accessed. For example, when an end user uses a web browser to dynamically query a database, the client browser requests a webpage from the web server. When the web server receives the page request, it looks for the page on the hard disk; when it finds the page, the server sends it back to the client.
Dynamic webpages are at the heart of current websites. In this database query sce- nario, the web server generates the webpage contents before it sends the page to the client web browser. The only problem with the preceding query scenario is that the web server must include the database query result on the page before it sends that page back to the client. Unfortunately, neither the web browser nor the web server knows how to connect to and read data from the database. Therefore, to support this type of request, the web server’s capability must be extended so it can understand and process database requests. This job is known as a server-side extension.
A server-side extension is a program that interacts directly with the web server to handle specific types of requests. In the preceding database query example, the server- side extension program retrieves the data from databases and passes the retrieved data to the web server, which in turn sends the data to the client’s browser for display. The server-side extension makes it possible to retrieve and present the query results, but more importantly, it provides its services to the web server in a way that is totally transpar- ent to the client browser. In short, the server-side extension adds significant functionality to the web server, and therefore to the Internet.
Online Content
Client/server systems are covered in detail in Appendix F, Client/ Server Systems, at www. cengagebrain.com.
server-side extension
A program that interacts directly with the server process to handle specific types of requests. Server- side extensions add significant functionality to web servers and intranets.
Chapter 15 Database Connectivity and Web Technologies 693
TABLE 15.3
CHARACTERISTICS AND BENEFITS OF INTERNET TECHNOLOGIES
INTERNET CHARACTERISTIC
BENEFIT
Hardware and software independence
Savings in equipment and software acquisition Ability to run on most existing equipment Platform independence and portability
No need for multiple platform development
Common and simple user interface
Reduced training time and cost
Reduced end-user support cost
No need for multiple platform development
Location independence
Global access through Internet infrastructure and mobile smart devices Creation of new location-aware services
Reduced requirements (and costs!) for dedicated connections
Rapid development at manageable costs
Availability of multiple development tools
Plug-and-play development tools (open standards)
More interactive development
Reduced development times
Relatively inexpensive tools
Free client access tools (web browsers)
Low entry costs; frequent availability of free web servers Reduced costs of maintaining private networks
Distributed processing and scalability using multiple servers
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A database server-side extension program is also known as web-to-database middleware. Figure 15.8 shows the interaction between the browser, the web server, and the web-to-database middleware.
FIGURE 15.8 WEB-TO-DATABASE MIDDLEWARE
CLIENT COMPUTER
HTML PAGE
8
WEB SERVER
Web server sends the HTML formatted page to the client
7
RDBMS Computer
and passes the script page to the web-to-database
middleware
WEB-TO-DATABASE 4 MIDDLEWARE
1
HTTP page request
SCRIPT PAGE
HTML PAGE
Web-to-database middleware passes the query results in HTML format back to the
web server
2
Web server receives request
TCP/IP NETWORK
3
SERVER Web server determines the COMPUTER page contains script language
The result of the database query is displayed in HTML format
6
Web-to-database middleware connects
to the database and passes query using database connectivity layer
JDBC ADO.NET ADO OLE-DB ODBC
5 RDBMS
SERVER
DATABASE
Database server passes the query results back to the
web-to-database middleware
Trace the web-to-database middleware actions in Figure 15.8:
1. The client browser sends a page request to the web server.
2. The web server receives and passes the request to the web-to-database middleware for processing.
3. Generally, the requested page contains some type of scripting language to enable the database interaction. The web server passes the script to the web-to-database middleware.
4. Theweb-to-databasemiddlewarereads,validates,andexecutesthescript.Inthiscase, it connects to the database and passes the query using the database connectivity layer.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
web-to-database middleware
A database server-side extension that retrieves data from databases and passes them to the web server, which in turn sends the data to the client’s browser for display.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 15 Database Connectivity and Web Technologies 695 5. Thedatabaseserverexecutesthequeryandpassestheresultbacktotheweb-to-database
middleware.
6. The web-to-database middleware compiles the result set, dynamically generates an HTML-formatted page that includes the data retrieved from the database, and sends it to the web server.
7. The web server returns the just-created HTML page, which now includes the query result, to the client browser.
8. The client browser displays the page on the local computer.
The interaction between the web server and the web-to-database middleware
is crucial to the development of a successful Internet database implementation. Therefore, the middleware must integrate closely via a well-defined web server interface.
15-2b Web Server Interfaces
Extending web server functionality implies that the web server and the web-to- database middleware will properly communicate with each other. (Database profession- als often use the word interoperate to indicate that each party can respond to the com- munications of the other.) A web server interface defines a standard way to exchange messages with external programs. Currently, there are two well-defined web server interfaces:
• Common Gateway Interface (CGI)
• Application programming interface (API)
The Common Gateway Interface (CGI) uses script files that perform specific func- tions based on the client’s parameters that are passed to the web server. The script file is a small program containing commands written in a programming language—usually Perl, C#, or Visual Basic. The script file’s contents can be used to connect to the database and to retrieve data from it, using the parameters passed by the web server. Next, the script converts the retrieved data to HTML format and passes the data to the web server, which sends the HTML-formatted page to the client.
The main disadvantage of using CGI scripts is that the script file is an external pro- gram that executes separately for each user request and therefore causes a resource bot- tleneck. Performance also could be degraded by using an interpreted language or by writing the script inefficiently.
An application programming interface (API) is a newer web server interface stan- dard that is more efficient and faster than a CGI script. APIs are more efficient because they are implemented as shared code or as dynamic-link libraries (DLLs). That means the API is treated as part of the web server program that is dynamically invoked when needed.
APIs are faster than CGI scripts because the code resides in memory, so there is no need to run an external program for each request. Instead, the same API serves all requests. Another advantage is that an API can use a shared connection to the database instead of creating a new one every time, as is the case with CGI scripts.
Although APIs are more efficient in handling requests, they have some disadvantages. Because the APIs share the same memory space as the web server, an API error can bring down the web server. Another disadvantage is that APIs are specific to the web server and to the operating system.
The web interface architecture is illustrated in Figure 15.9.
Common Gateway Interface (CGI)
A web server interface standard that uses script files to perform specific functions based on a client’s parameters.
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FIGURE 15.9 WEB SERVER CGI AND API INTERFACES
CLIENT COMPUTER
SERVER COMPUTER
WEB SERVER
CGI
API
Database Connectivity Middleware
External program
TCP/IP network
RDBMS COMPUTER
JDBC ADO.NET ADO OLE-DB ODBC
RDBMS SERVER
DATABASE
(DLL call)
Regardless of the type of web server interface used, the web-to-database middle- ware program must be able to connect with the database. That connection can be accomplished in one of two ways:
• Use the native SQL access middleware provided by the vendor. For example, you can use SQL*Net if you are using Oracle.
• Use the services of general database connectivity standards such as ODBC, OLE-DB, ADO, ADO.NET, or JDBC.
15-2c The Web Browser
The web browser is software such as Microsoft Internet Explorer, Google Chrome, Apple Safari, or Mozilla Firefox that lets end users navigate the web from their client computer. Each time the end user clicks a hyperlink, the browser generates an HTTP GET page request that is sent to the designated web server using the TCP/IP Internet protocol.
The web browser’s job is to interpret the HTML code that it receives from the web server and to present the various page components in a standard formatted way. Unfortunately,
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the browser’s interpretation and presentation capabilities are not sufficient to develop web-based applications. The web is a stateless system—at any given time, a web server does not know the status of any of the clients communicating with it. That is, there is no open communication line between the server and each client accessing it, which of course is impractical in a worldwide web! Instead, client and server computers interact in very short “conversations” that follow the request-reply model. For example, the browser is concerned only with the current page, so there is no way for the second page to know what was done in the first page. The only time the client and server computers communi- cate is when the client requests a page—when the user clicks a link—and the server sends the requested page to the client. Once the client receives the page and its components, the client/server communication is ended. Therefore, although you may be browsing a page and think that the communication is open, you are actually just browsing the HTML document stored in the local cache (temporary directory) of your browser. The server does not have any idea what the end user is doing with the document, what data is entered in a form, what option is selected, and so on. On the web, if you want to act on a client’s selection, you need to jump to a new page (go back to the web server), thus losing track of what was done before.
The web browser, through its use of HTML, does not have computational abilities beyond formatting output text and accepting form field inputs. Even when the browser accepts form field data, there is no way to perform immediate data entry validation. Therefore, to perform such crucial processing in the client, the web defers to other web programming languages such as Java, JavaScript, and VBScript. The browser resembles a dumb terminal that displays only data and can perform only rudimentary processing such as accepting form data inputs. To improve the capabilities of the web browser, you must use plug-ins and other client-side extensions. On the server side, web application servers provide the necessary processing power.
15-2d Client-Side Extensions
Client-side extensions add functionality to the web browser. Although client-side
extensions are available in various forms, the most common are:
• Plug-ins
• Java and JavaScript
• ActiveX and VBScript
A plug-in is an external application that is automatically invoked by the browser when needed. The plug-in is associated with a data object—generally using the file exten- sion—to allow the web server to properly handle data that is not originally supported. For example, if one of the page components is a PDF document, the web server will receive the data, recognize it as a Portable Document Format object, and launch Adobe Reader to present the document on the client computer.
JavaScript is a scripting language (one that enables the execution of a series of com- mands or macros) that allows web authors to design interactive sites. JavaScript code is embedded in the webpage and executed after a specific event, such as a mouse click on an object or a page being loaded from the server into memory.
ActiveX is Microsoft’s alternative to Java. ActiveX is a specification for writing pro- grams that run inside the Microsoft client browser, Internet Explorer. Because ActiveX is oriented toward Windows applications, it has low portability. ActiveX extends the web browser by adding controls to webpages, including drop-down lists, a slider, a calendar, and a calculator. Those controls are downloaded from the web server when needed so you can manipulate data inside the browser. ActiveX controls can be created in several
stateless system
A system in which a web server does not know the status of the clients communicating with it. The web does not reserve memory
to maintain an open communications state between the client and the server.
client-side extension
Extension that adds functionality to a web browser. The most common extensions are plug-ins, Java, JavaScript, ActiveX, and VBScript.
plug-in
On the web, a client-side, external application that is automatically invoked by the browser when needed to manage specific types of data.
JavaScript
A scripting language that allows web authors to design interactive websites. JavaScript code is embedded in webpages, and then downloaded with the page and activated when a specific event takes place, such as
a mouse click on an object.
ActiveX
Microsoft’s alternative to Java. A specification for writing programs that will run inside the Microsoft client browser. Oriented mainly to Windows applications,
it is not portable. It adds controls such as drop- down windows and calendars to webpages.
Chapter 15 Database Connectivity and Web Technologies 697
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programming languages; C++ and Visual Basic are most commonly used. Microsoft’s .NET framework allows for wider interoperability of ActiveX-based applications (such as ADO.NET) across multiple operating environments.
VBScript is another Microsoft product that is used to extend browser functionality. VBScript is derived from Microsoft Visual Basic. Like JavaScript, VBScript code is embedded inside an HTML page and is activated by triggering events such as clicking a link.
From the developer’s point of view, using routines that permit data validation on the client side is an absolute necessity. For example, when data is entered in a web form and no data validation is done on the client side, the entire data set must be sent to the web server. That scenario requires the server to perform all data validation, thus wasting valuable CPU processing cycles. Therefore, client-side data input validation is one of the most basic requirements for web applications. Most of the data validation routines are done in Java, JavaScript, ActiveX, or VBScript.
15-2e Web Application Servers
A web application server is a middleware application that expands the functionality of web servers by linking them to a wide range of services, such as databases, directory systems, and search engines. The web application server also provides a consistent run- time environment for web applications. Web application servers can be used to perform the following:
• Connect to and query a database from a webpage.
• Present database data in a webpage using various formats.
• Create dynamic web search pages.
• Create webpages to insert, update, and delete database data.
• Enforce referential integrity in the application program logic.
• Use simple and nested queries and programming logic to represent business rules.
Web application servers provide features such as:
• An integrated development environment with session management and support for persistent application variables
• Security and authentication of users through user IDs and passwords
• Computational languages to represent and store business logic in the application
server
• Automatic generation of HTML pages integrated with Java, JavaScript, VBScript, ASP, and so on
• Performance and fault-tolerant features
• Database access with transaction management capabilities
• Access to multiple services, such as file transfers (FTP), database connectivity, email, and directory services
Examples of web application servers include ColdFusion/JRun by Adobe, WebSphere Application Server by IBM, WebLogic Server by Oracle, Fusion by NetObjects, Visual Studio .NET by Microsoft, and WebObjects by Apple. All web application servers offer the ability to connect web servers to multiple data sources and other services. They vary in their range of available features, robustness, scalability, compatibility with other web and database tools, and extent of the development environment.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
VBScript
A Microsoft client-side extension that extends a browser’s functionality; VBScript is derived from Visual Basic.
web application server
A middleware application that expands the functionality of
web servers by linking them to a wide range of services, such as databases, directory systems, and search engines.
Copyright 2017 Cengage Lea Editorial review has deemed that

15-2f Web Database Development
Online Content
To see and try a partic- ular web-to-database interface in action, con- sult Appendix J, Web Database Development with ColdFusion, at www.cengagebrain.com. This appendix steps you through the process of creating and using a simple web-to-database interface, and provides more detailed informa- tion on developing web databases with Adobe ColdFusion middleware.
Chapter 15 Database Connectivity and Web Technologies 699
Web database development deals with the process of interfacing databases with the web browser—in short, how to create webpages that access data in a database. As you learned earlier in this chapter, multiple web environments can be used to develop web database applications. This section presents two simple code exam- ples (ColdFusion and PHP). Because this is a database book, the examples focus only on the commands used to interface with the database rather than the specifics of HTML code.
A Microsoft Access database named Ch15_Orderdb is used to illustrate the web-to-database interface examples. The Ch15_Orderdb database, whose relational dia- gram is shown in Figure 15.10, was designed to track the purchase orders placed by users in a multidepartment company.
The following examples explain how to use ColdFusion and PHP to create a simple webpage to list the VENDOR rows. The scripts used in these examples perform two basic tasks:
1. QuerythedatabaseusingstandardSQLtoretrieveadatasetthatcontainsallrecords in the VENDOR table. The examples will use an ODBC data source named Rob- Cor. The ODBC data source was defined using the operating system tools shown in Section 15-1b.
2. FormattherecordsgeneratedinStep1inHTMLsotheyareincludedinthewebpage that is returned to the client browser.
Figure 15.11 shows the ColdFusion code to query the VENDOR table.
FIGURE 15.10 THE ORDERDB RELATIONAL DIAGRAM FOR WEB DATABASE DEVELOPMENT EXAMPLES
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FIGURE15.11 COLDFUSIONCODETOQUERYTHEVENDORTABLE
In the code in Figure 15.11, note that its ColdFusion tags are CFQUERY (to query a database) and CFOUTPUT (to display the data returned by the query). Take a closer look at these two CFML tags:
• tag (lines 4–6). This tag sets the stage for the database connection and the execution of the enclosed SQL statement. The CFQUERY tag uses the following parameters:
– NAME = “queryname”. This name uniquely identifies the record set returned by the database query.
– DATASOURCE = “datasourcename”. This parameter uses the previously defined ODBC data source name.
– The SQL statement (line 5) is the SQL code used to retrieve the data rows from the VENDOR table.
• tag (lines 15–17 and 18–35). This tag is used to display the results from a CFQUERY or to call other ColdFusion variables or functions. Its parameters are as follows:
– QUERY = “queryname”. This is an optional parameter (see line 18). The tag works like a loop that is executed as many times as the number of rows in the named query set. You can include any valid HTML tags or text within the opening and closing CFOUTPUT tags.
– ColdFusion uses pound signs (#) to reference query fields in the resulting query set or to call other ColdFusion variables. For example, #venlist.RecordCount# (line 16) displays the number of rows returned by the “venlist” query result set.
– Lines 19−34 are repeated as a loop, one for each record returned in the named query. Figure 15.12 shows the PHP code to query the VENDOR table.
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by
• • •
•
•
•
In the figure, note that PHP uses multiple tags to query and display the data returned the query. Take a closer look at the PHP functions:
The odbc_connect function (line 11) opens a connection to the ODBC data source. A handle to this database is set in the $dbc variable.
The odbc_exec function (line 13) executes the SQL query stored in the $sql variable against the $dbc database connection. The query’s result set is stored in the $rs variable.
The while function (line 15) loops through the result set ($rs) and uses the ODBC_ FETCH_ROW function to get one row at a time from the result set. Notice that PHP variables start with the dollar sign ($).
The odbc_result function (lines 17−30) gets a column value from a row in the result set and stores it in a variable. This function extracts the different values for each field to be displayed and stores them in variables.
The echo function (lines 32−47) outputs text to the webpage using the variables defined in the previous lines. You can also combine text (HTML code) and PHP vari- ables (lines 33–46) using the “.” delimiter.
The odbc_close function closes the database connection.
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Chapter 15 Database Connectivity and Web Technologies 701
FIGURE15.12 PHPCODETOQUERYTHEVENDORTABLE

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The previous examples are just two of the many ways you can interface webpages and databases to web applications. These examples only scratch the surface of the multiple features that web application servers provide.
Current-generation systems involve more than just the development of web-enabled database applications. They also require applications that can communicate with each other and with other systems not based on the web. Clearly, systems must be able to exchange data in a standard-based format. That is the role of XML.
15-3 Extensible Markup Language (XML)
Companies use the Internet to generate business transactions and integrate data to increase efficiency and reduce costs. These transactions are known as electronic com- merce (e-commerce); it enables all types of organizations to sell products and services to a global market. E-commerce transactions—the sale of products or services—can take place between businesses (business-to-business, or B2B) or between a business and a consumer (business-to-consumer, or B2C).
Most e-commerce transactions take place between businesses. Because B2B e-commerce integrates business processes among companies, it requires the trans- fer of business information among different business entities. However, the way in which businesses represent, identify, and use data tends to differ substantially from company to company. As a simple example, some companies use the term product code, while others use item ID.
Until recently, a purchase order traveling over the web was expected to be in the form of an HTML document. The HTML webpage displayed on the web browser would include formatting as well as the order details. HTML tags describe how something looks on the webpage, such as typefaces and heading styles, and they often come in pairs to start and end formatting features. For example, the following tags in angle brackets would display FOR SALE in bold Arial font:
FOR SALE
If an application needs to get the order data from the webpage, there is no easy way to extract details such as the order number, date, customer number, product code, quantity, or price from an HTML document. The HTML document can only describe how to dis- play the order in a web browser; it does not permit the manipulation of the order’s data elements. To solve that problem, a new markup language known as Extensible Markup Language was developed.
Extensible Markup Language (XML) is a meta-language used to represent and manipulate data elements. XML is designed to facilitate the exchange of structured doc- uments, such as orders and invoices, over the Internet. The World Wide Web Consor- tium (W3C) published the first XML 1.0 standard definition in 1998, setting the stage for giving XML the real-world appeal of being a true vendor-independent platform. It is not surprising that XML has rapidly become the data exchange standard for e-commerce applications.
The XML meta-language allows the definition of new tags, such as , to describe the data elements used in an XML document. This ability to extend the language explains the X in XML; the language is said to be extensible. XML is derived from the Standard Generalized Markup Language (SGML), an international stan- dard for the publication and distribution of highly complex technical documents. For example, documents used by the aviation industry and the military services are too complex and unwieldy for the web. Just like HTML, which was also derived from
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Online
Content
To learn more about e-commerce, consult Appendix I, Databases in Electronic Commerce, at www.cengagebrain.com.
tag
In markup languages such as HTML and XML, a command inserted in a document to specify how the document should be formatted. Tags are used in server- side markup languages and interpreted by
a web browser for presenting data.
Extensible Markup Language (XML)
A meta-language used to represent
and manipulate data elements. Unlike other markup languages,
XML permits the manipulation of a document’s data elements. XML facilitates the exchange of structured documents such as orders and invoices over the Internet.
Copyright 2017 Cengage Le Editorial review has deemed that

Chapter 15 Database Connectivity and Web Technologies 703 SGML, an XML document is a text file. However, it has a few important additional
characteristics:
• XML allows the definition of new tags to describe data elements.
• XML is case sensitive: is not the same as .
• XML must be well formed; that is, tags must be properly formatted. Most openings also have a corresponding closing. For example, a product’s identification would require the format 2345-AA.
• XML must be properly nested. For example, properly nested XML might look like this: 2345-AA.
• You can use the <‐‐ and ‐‐> symbols to enter comments in the XML document.
• The XML and xml prefixes are reserved for XML only.
XML is not a new version or replacement for HTML. XML is concerned with the description and representation of the data, rather than the way the data is displayed. XML provides the semantics that facilitate the sharing, exchange, and manipulation of structured documents over organizational boundaries. XML and HTML perform complementary functions rather than overlapping functions. Extensible Hypertext Markup Language (XHTML) is the next generation of HTML based on the XML framework. The XHTML specification expands the HTML standard to include XML features. Although it is more powerful than HTML, XHTML requires strict adherence to syntax requirements.
To illustrate the use of XML for data exchange purposes, consider a B2B example in which Company A uses XML to exchange product data with Company B over the Inter- net. Figure 15.13 shows the contents of the productlist.xml document.
The preceding example illustrates several important XML features:
• The first line represents the XML document declaration, and it is mandatory.
• Every XML document has a root element. In the example, the second line declares the ProductList root element.
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FIGURE15.13 CONTENTSOFTHEPRODUCTLIST.XMLDOCUMENT

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• The root element contains child elements or subelements. In the example, line 3 declares Product as a child element of ProductList.
• Each element can contain subelements. For example, each Product element is com- posed of several child elements, represented by P_CODE, P_DESCRIPT, P_INDATE, P_QOH, P_MIN, and P_PRICE.
Once Company B receives productlist.xml, it can process the document, assuming that it understands the tags created by Company A. The meaning of the XML in Fig- ure 15.13 is fairly self-evident, but there is no easy way to validate the data or to check whether the data is complete. For example, you could encounter a P_INDATE value of “25/14/2016,” but is that value correct? What happens if Company B expects a Vendor element as well? How can companies share data descriptions about their business data elements? The next section shows how document type definitions and XML schemas are used to address such concerns.
15-3a DocumentTypeDefinitions(DTD)andXMLSchemas
Companies that use B2B transactions must have a way to understand and validate each other’s tags. One way to accomplish that task is through the use of document type defi- nitions. A document type definition (DTD) is a file with a .dtd extension that describes XML elements—in effect, a DTD file provides the composition of the database’s logical model and defines the syntax rules or valid elements for each type of XML document. (The DTD component is similar to having a public data dictionary for business data.) Companies that intend to engage in e-commerce transactions must develop and share DTDs. Figure 15.14 shows the productlist.dtd document for the productlist.xml docu- ment shown earlier in Figure 15.13.
FIGURE15.14 CONTENTSOFTHEPRODUCTLIST.DTDDOCUMENT
In Figure 15.14, the productlist.dtd file provides definitions of the elements in the productlist.xml document. In particular, note the following:
• The first line declares the ProductList root element.
• The ProductList root element has one child, the Product element. The second line
describes the Product element.
• The plus symbol (+) indicates that Product occurs one or more times within ProductList.
• An asterisk (*) would mean that the child element occurs zero or more times.
• The question mark (?) after P_INDATE and P_MIN indicates that they are optional
child elements.
• The third through eighth lines show that the Product element has six child elements.
• The #PCDATA keyword represents the actual text data.
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document type definition (DTD)
A file with a .dtd extension that describes XML elements; in effect, a DTD file describes a document’s composition and defines the syntax rules or valid tags for each type of XML document.
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To be able to use a DTD file to define elements within an XML document, the DTD must be referenced within that XML document. Figure 15.15 shows the productlistv2.xml document that includes the reference to productlist.dtd in the second line.
In Figure 15.15, note that P_INDATE and P_MIN do not appear in all Product defini- tions because they were declared to be optional elements. The DTD can be referenced by many XML documents of the same type. For example, if Company A routinely exchanges product data with Company B, it will need to create the DTD only once. All subsequent XML documents will refer to the DTD, and Company B will be able to verify the data being received.
To further demonstrate the use of XML and DTD for e-commerce data exchanges, consider the case of two companies exchanging order data. Figure 15.16 shows the DTD and XML documents for that scenario.
Although the use of DTDs is a great improvement for data sharing over the web, a DTD only provides descriptive information for understanding how the elements— root, parent, child, mandatory, or optional—relate to one another. A DTD provides lim- ited additional semantic value, such as data type support or data validation rules. That information is very important for database administrators who are in charge of large e-commerce databases. To solve the DTD problem, the W3C published an XML schema standard that better describes XML data.
The XML schema is an advanced data definition language that is used to describe the structure of XML data documents. This structure includes elements, data types, relationship types, ranges, and default values. One of the main advan- tages of an XML schema is that it more closely maps to database terminology and features. For example, an XML schema can define common database types such as date, integer, or decimal; minimum and maximum values; a list of valid values; and required elements. Using the XML schema, a company would be able to validate data for values that may be out of range, have incorrect dates, contain invalid val- ues, and so on. For example, a university application must be able to specify that a GPA value is between 0 and 4.0, and it must be able to detect an invalid birth date such as “14/13/2016.” (There is no 14th month.) Many vendors are adopting this new standard and are supplying tools to translate DTD documents into XML schema definition documents. It is widely expected that XML schemas will replace DTD as the method to describe XML data.
XML schema
An advanced data definition language used to describe the elements, data types, relationship types, ranges, and default values of XML data documents. One of the main advantages of an XML schema is that
it more closely maps to database terminology and features.
Chapter 15 Database Connectivity and Web Technologies 705
FIGURE15.15 CONTENTSOFTHEPRODUCTLISTV2.XMLDOCUMENT
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FIGURE15.16 DTDANDXMLDOCUMENTSFORORDERDATA
OrderData.dtd
“+” sign indicates one or more ORD_PRODS elements
OrderData.xml
Two ORD_PRODS elements in XML document
Unlike a DTD document, which uses a unique syntax, an XML schema definition (XSD) file uses a syntax that resembles an XML document. Figure 15.17 shows the XSD document for the OrderData XML document.
The code shown in Figure 15.17 is a simplified version of the XML schema document. As you can see, the XML schema syntax is similar to the XML document syntax. How- ever, the XML schema introduces additional semantic information for the OrderData XML document, such as string, date, and decimal data types; required elements; and minimum and maximum cardinalities for the data elements.
15-3b XML Presentation
One of the main benefits of XML is that it separates data structure from its presentation and processing. By separating the two, you can present the same data in different ways— which is similar to having views in SQL. The Extensible Style Language (XSL) specifi- cation provides the mechanism to display XML data. XSL is used to define the rules by which XML data is formatted and displayed. The XSL specification is divided into two parts: Extensible Style Language Transformations (XSLT) and XSL style sheets.
• Extensible Style Language Transformations (XSLT) describes the general mechanism that is used to extract and process data from one XML document and enable its transforma- tion within another document. Using XSLT, you can extract data from an XML docu- ment and convert it into a text file, an HTML webpage, or a webpage that is formatted
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XML schema definition (XSD)
A file that contains the description of an XML document.
Copyright 2017 Cengage Lea Editorial review has deemed that

for a mobile device. What the user sees in those cases is actually a view (or HTML rep- resentation) of the XML data. XSLT can also be used to extract certain elements from an XML document, such as product codes and product prices, to create a product catalog. XSLT can even be used to transform one XML document into another.
• XSL style sheets define the presentation rules applied to XML elements—somewhat like presentation templates. The XSL style sheet describes the formatting options to apply to XML elements when they are displayed on a browser, smartphone, tablet screen, and so on.
Figure 15.18 illustrates the framework used by the various components to translate XML documents into viewable webpages, an XML document, or some other document.
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Chapter 15 Database Connectivity and Web Technologies 707
FIGURE 15.17 THE XML SCHEMA DOCUMENT FOR THE ORDER DATA
FIGURE15.18 FRAMEWORKFORXMLTRANSFORMATIONS
XML document
XSL transformations
•Extract •Convert
New
XML document
XSLT can be used to transform one XML document into another XML document.
XSL style sheets
Apply formatting rules to XML elements
HTML
HTML
The process can render different webpages for different purposes,
such as one page for a web browser and another for a mobile device.

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To display the XML document with Windows Internet Explorer (IE), enter the URL of the XML document in the browser’s address bar. Figure 15.19 is based on the product- list.xml document created earlier. As you examine Figure 15.19, note that IE shows the XML data in a color-coded, collapsible, tree-like structure. (Actually, this is the IE default style sheet that is used to render XML documents.)
15-3c XML Applications
Now that you have some idea what XML is, how can you use it? What kinds of appli- cations lend themselves particularly well to XML? This section lists some of the uses of XML. Keep in mind that the future use of XML is limited only by the imagination and creativity of developers, designers, and programmers.
• B2B exchanges. XML enables the exchange of B2B data, providing the standard for all organizations that need to exchange data with partners, competitors, the government, or customers. In particular, XML is positioned to replace EDI as the standard for automation of the supply chain because it is less expensive and more flexible.
• Legacy systems integration. XML provides the “glue” to integrate legacy system data with modern e-commerce web systems. Web and XML technologies could be used to inject some new life into old but trusted legacy applications. Another example is the use of XML to import transaction data from multiple databases to a data warehouse database.
• Webpage development. XML provides several features that make it a good fit for cer- tain web development scenarios. For example, web portals with large amounts of per- sonalized data can use XML to pull data from multiple external sources (such as news, weather, and stock sites) and apply different presentation rules to format pages on desktop computers as well as mobile devices.
• Database support. A DBMS that supports XML exchanges can integrate with external systems such as the web, mobile data, and legacy systems, thus enabling the creation
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FIGURE15.19 DISPLAYINGXMLDOCUMENTS

of new types of systems. These databases can import or export data in XML format or generate XML documents from SQL queries while still storing the data using their native data model format. An example is the use of the FOR XML clause in the SQL SELECT statement in SQL Server. Alternatively, a DBMS can also support an XML data type to store XML data in its native format—enabling support to store tree-like hierarchical structures inside a relational structure.
• Database metadictionaries. XML is also used to create metadictionaries, or vocabular- ies, for entire industries. Examples of metadictionaries include HR-XML for the human resources industry, the metadata encoding and transmission standard (METS) from the Library of Congress, the clinical accounting information (CLAIM) data exchange standard for patient data exchange in electronic medical record systems, and the extensible business reporting language (XBRL) standard for exchanging business and financial information.
• XML databases.1 Most databases on the market support XML to manage data in some shape or form. The approaches range from simple middleware XML software to object databases with XML interfaces to full XML database engines and servers. XML data- bases provide for the storage of data in complex relationships. For example, an XML database would be well suited to store the contents of a book. The book’s structure would dictate its database structure: a book typically consists of chapters, sections, paragraphs, figures, charts, footnotes, endnotes, and so on. Examples of databases with XML data type support are Oracle, IBM DB2, and MS SQL Server. Fully XML databases examples are Tamino from Software AG (www.softwareag.com) and the open source dbXML from http://sourceforge.net/projects/dbxml-core.
• XML services. Many companies are already working to develop a new breed of services based on XML and web technologies. These services break down the interoperability barriers among systems and companies alike. XML provides the infrastructure that helps heterogeneous systems to work together across the desk, the street, and the world. Services would use XML and other Internet technologies to publish their interfaces. Other services that want to interact with existing ser- vices would locate them and learn their vocabulary (service request and replies) to establish a “conversation.”
One area in which Internet, web, virtualization, and XML technologies work together in innovative ways to leverage IT services is cloud computing.
15-4 Cloud Computing Services
You have almost certainly heard about the “cloud” from the thousands of publica- tions and TV ads that have used the term over the years, although it has represented different concepts. In the late 1980s, the term cloud was used by telecommunication companies to describe their data networks. In the late 1990s, during the peak of Internet growth, the term depicted the Internet itself. Then, in 2006, Google and Amazon began using the term cloud computing to describe a new set of innovative web-based services. Google, Yahoo, eBay, and Amazon were early adopters of this new computing paradigm.
But what exactly is cloud computing? According to the National Institute of Standards and Technology (NIST),2 cloud computing is “a computing model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computer resources
1 For a comprehensive analysis of XML database products, see “XML Database Products” by Ronald Bourret at www.rpbourret.com.
2Recommendations of the National Institute of Standards and Technology, Peter Mell and Timothy Grance, Special Publication 800–145 (Draft), January 2011.
cloud computing
A computing model that provides ubiquitous, on-demand access
to a shared pool of configurable resources that can be rapidly provisioned.
Chapter 15 Database Connectivity and Web Technologies 709
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(e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” The term cloud services is used in this book to refer to the services provided by cloud computing. Cloud services allow any organization to quickly and economically add information tech- nology services such as applications, storage, servers, processing power, databases, and infra- structure to its IT portfolio. Figure 15.20 shows a representation of cloud computing services on the Internet.
Cloud computing allows IT-savvy organizations such as Amazon, Google, and Microsoft to build high-performance, fault-tolerant, flexible, and scalable IT services. These services include applications, storage, servers, processing power, databases, and email, which are deliv- ered via the Internet to individuals and organizations using a pay-as-you-go price model.
For example, imagine that the chief technology officer of a nonprofit organization wants to add email services to the IT portfolio. A few years ago, this proposition would have implied building the email system’s infrastructure from the ground up, includ- ing hardware, software, setup, configuration, operation, and maintenance. However, in today’s cloud computing era, you can use Google Apps for Business or Microsoft Exchange Online and get a scalable, flexible, and more reliable email solution for a frac- tion of the cost. The best part is that you do not have to worry about the daily chores of managing and maintaining the IT infrastructure, such as OS updates, patches, security, fault tolerance, and recovery. What used to take months or years to implement can now be done in a matter of minutes. If you need more space, you just add another storage unit to your storage cloud. If you need more processing power to handle last-minute orders during the busy holiday season, you simply add more processing units to your cloud servers. Even more importantly, you can scale down as easily as you scaled up. Once your need for additional processing or storage subsides, you can go back to your previous
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FIGURE15.20 CLOUDSERVICES
Simple Messaging
Simple Queuing
Elastic Compute
Content Delivery
Simple Storage
Relational DB
NoSQL DB
• Email
• Storage • RDBMS
• Desktop • Server • NoSQL
Cloud Service Providers
cloud services
The services provided by cloud computing. Cloud services allow any organization to quickly and economically add information technology services such as applications, storage, servers, processing power, databases, and infrastructure.
Copyright 2017 Cengage Le Editorial review has deemed that

levels of usage and pay only for what you use. The beauty of cloud services is that you can scale down automatically, without an administrator’s intervention.
Cloud computing is important for database technologies because it has the potential to become a “game changer.” Cloud computing eliminates financial and technological barriers so organizations can leverage database technologies in their business processes with minimal effort and cost. In fact, cloud services have the potential to turn basic IT services into “commodity” services such as electricity, gas, and water, and to enable a revolution that could change not only the way that companies do business, but the IT business itself. As Nicholas Carr put it so vividly: “Cloud computing is for IT what the invention of the power grid was for electricity.”3
The technologies that make cloud computing work have been around for a few years now; these technologies include the web, messaging, virtualization, remote desktop proto- cols, VPN, and XML. However, cloud computing itself is still in the early years and needs to mature further before it can be widely adopted. Despite this, more and more organizations are tapping into cloud services to secure advanced database services (relational or NoSQL) for their organizations. Currently, you can log in to Amazon Web Services (AWS) or Microsoft Azure and have a relational database ready for use in a matter of minutes. Instead of spending large amounts of cash buying hardware and software, organizations can employ a pay-per-use model for their IT services. Figure 15.21 depicts the cost of provisioning a relational database instance in Microsoft Azure and Amazon RDS services, respectively.
3 Nicholas Carr, The Big Switch: Rewiring the World, from Edison to Google. W.W. Norton & Co., 2009.
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Chapter 15 Database Connectivity and Web Technologies 711
FIGURE15.21 PROVISIONINGRDBMSINTHECLOUD
Provisioning a MySQL RDBMS instance in
Amazon Web Services (AWS)
Provisioning MS SQL Azure RDBMS instance in
Microsoft Azure

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Although Figure 15.21 shows a cloud that requires some degree of customization on the customer’s part, other cloud computing services are more transparent to the user and require less customization. For example, Dropbox is a simple cloud service that lets you synchronize your documents, photos, music, and other files transparently over the Inter- net across many devices. Apple provides a similar service, known as iCloud, to seam- lessly exchange data among all its mobile and nonmobile devices. Both services work transparently behind the scenes with minimal end-user intervention. As you can see, cloud computing implementations vary; the next section explains the basic types.
15-4a Cloud Implementation Types
Cloud computing has different types of implementations based on who the target cus-
tomers are:
• Public cloud. This type of cloud infrastructure is built by a third-party organiza- tion to sell cloud services to the general public. The public cloud is the most com- mon type of cloud implementation; examples include Amazon Web Services (AWS), Google Application Engine, and Microsoft Azure. In this model, cloud consumers share resources with other consumers transparently. The public cloud infrastructure is managed exclusively by the third-party provider.
• Private cloud. This type of internal cloud is built by an organization for the sole pur- pose of servicing its own needs. Private clouds are often used by large, geographically dispersed organizations to add agility and flexibility to internal IT services. The cloud infrastructure could be managed by internal IT staff or an external third party.
• Community cloud. This type of cloud is built by and for a specific group of organi- zations that share a common trade, such as agencies of the federal government, the military, or higher education. The cloud infrastructure could be managed by internal IT staff or an external third party.
Regardless of the implementation an organization uses, most cloud services share a common set of core characteristics. These characteristics are explored in the next section.
15-4b Characteristics of Cloud Services
Cloud computing services share a set of guiding principles. The characteristics listed in this section are shared by prominent public cloud providers such as Amazon, Google, Salesforce, SAP, and Microsoft. The prevalent characteristics are:
• Ubiquitous access via Internet technologies. All cloud services use Internet and web technologies to provision, deliver, and manage the services they provide. The basic requirement is that the device has access to the Internet.
• Shared infrastructure. The cloud service infrastructure is shared by multiple users. Sharing is made possible by web and virtualization technologies. Cloud services effectively provide an organization with a virtual IT infrastructure, which is locally managed by the consumer’s organization as if it were the only user of the infrastructure.
• Lower costs and variable pricing. The initial costs of using cloud services tend to be sig- nificantly lower than building on-premise IT infrastructures. According to some stud- ies,4 the savings could range from 35 percent to 55 percent depending on company
4“The Compelling TCO Case for Cloud Computing in SMB and Mid-Market Enterprises: A 4-year total cost of ownership (TCO) perspective comparing cloud and on-premise business application development,” Sanjeev Aggarwal, Partner; Laurie McCabe, Partner: Hurwitz & Associates, 2009.
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public cloud
A form of computing
in which the cloud infrastructure is built by a third-party organization to sell cloud services to the general public.
private cloud
A form of cloud computing in which an internal cloud is built by an organization to serve its own needs.
community cloud
A type of cloud built by and for a specific group of organizations that share a common trade, such as agencies of the federal government, the military, or higher education.
Copyright 2017 Cengage Lea Editorial review has deemed that

size, although more research is needed in this area. Because the web service’s usage is metered per volume and time utilization, consumers benefit from lower and flexible pricing options. These options range from pay-as-you-go to fixed pricing based on min- imum levels of service.
• Flexible and scalable services. The cloud services are built on an infrastructure that is highly scalable, fault tolerant, and very reliable. The services can scale up and down on demand according to resource demands.
• Dynamic provisioning. The consumer can quickly provision any needed resources, including servers, processing power, storage, and email, by accessing the web man- agement dashboard and then adding and removing services on demand. This process also could be automated via other services.
• Service orientation. Cloud computing focuses on providing consumers with specific, well-defined services that use well-known interfaces. These interfaces hide the com- plexity from the end user, and can be delivered anytime and anywhere.
• Managed operations. Cloud computing minimizes the need for extensive and expen- sive in-house IT staff. The system infrastructure is managed by the cloud provider. The consumer organization’s IT staff is free from routine management and maintenance tasks so they can focus on other tasks within the organization. Managed operations apply to organizations that use public clouds and that outsource cloud management to an external third party.
The preceding list is not exhaustive, but it is a starting point to understand most cloud computing offerings. Although most companies move to cloud services because of cost savings, some companies move to them because they are the best way to gain access to specific IT resources that would otherwise be unavailable. Not all cloud ser- vices are the same; in fact, there are several different types, as explained in the next section.
15-4c Types of Cloud Services
Cloud services come in different shapes and forms; no single type of service works for all consumers. In fact, cloud services often follow an à la carte model; consumers can choose multiple service options according to their individual needs. These services can build on top of each other to provide sophisticated solutions. Based on the types of services pro- vided, cloud services can be classified by the following categories:
• Software as a Service (SaaS). The cloud service provider offers turnkey applications that run in the cloud. Consumers can run the provider’s applications internally in their organi- zations via the web or any mobile device. The consumer can customize certain aspects of the application but cannot make changes to the application itself. The application is actu- ally shared among users from multiple organizations. Examples of SaaS include MS Office 365, Google Docs, Intuit’s TurboTax Online, and SCALA digital signage.
• Platform as a Service (PaaS). The cloud service provider offers the capability to build and deploy consumer-created applications using the provider’s cloud infrastructure. In this scenario, the consumer can build, deploy, and manage applications using the provider’s cloud tools, languages, and interfaces. However, the consumer does not manage the underlying cloud infrastructure. Examples of PaaS include the Micro- soft Azure platform with .NET and the Java development environment, and Google Application Engine with Python or Java.
• Infrastructure as a Service (IaaS). In this case, the cloud service provider offers con- sumers the ability to provision their own resources on demand; these resources include
Software as a Service (SaaS)
A model in which
the cloud service provider offers turnkey applications that run in the cloud.
Platform as a Service (PaaS)
A model in which the cloud service provider can build and deploy consumer-created applications using
the provider’s cloud infrastructure.
Infrastructure as a Service (IaaS)
A model in which the cloud service provider offers consumers the ability to provision
their own resources on demand; these resources include storage, servers, databases, processing units, and even a complete virtualized desktop.
Chapter 15 Database Connectivity and Web Technologies 713
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714 Part 5
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storage, servers, databases, processing units, and even a complete virtualized desktop. The consumer then can add or remove the resources as needed. For example, a con- sumer can use Amazon Web Services (AWS) and provision a server computer that runs Linux and Apache Web server using 16 GB of RAM and 160 GB of storage.
Figure 15.22 illustrates a sample of the different types of cloud services; these services can be accessed from any computing device.
Cloud computing services have evolved in their sophistication and flexibility. The merging of new technologies has enabled the creation of new options such as “desktop as a service,” which effectively creates a virtual computer on the cloud that can be accessed from any device over the Internet. For example, you can use a service such as Desktone (http://www.vmwhorizonair.com/) and get a Windows desktop running over the web for your personal use in a matter of minutes. Moreover, you can access your virtual desktop via the web browser or using any Remote Desktop Protocol (RDP) application.
15-4d Cloud Services: Advantages and Disadvantages
Cloud computing has grown remarkably in the past few years. Companies of all sizes are enjoying the advantages of cloud computing, but its widespread adoption is still lim- ited by several factors. Table 15.4 summarizes the main advantages and disadvantages of cloud computing.
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FIGURE 15.22 TYPES OF CLOUD SERVICES
Desktops
Laptops
Servers
Internet
Software as a Service
• MS Office 365, MS Exchange Online • Google Docs, Google Email
• Salesforce CRM Online
• SAP Business ByDesign
Platform as a Service
• Amazon Web Services, Amazon Relational Data Service, Amazon Simple DB
• MS Azure Platform, MS SQL Service • Google Application Engine
Infrastructure as a Service
• Amazon Web Services Elastic Computing Cloud 2 (EC2) • Amazon Elastic MapReduce Service
• Amazon Simple Storage Service (S3)
• Amazon Elastic Load Balancing Service
Tablets
Smartphones

Chapter 15 Database Connectivity and Web Technologies 715
TABLE 15.4
ADVANTAGES AND DISADVANTAGES OF CLOUD COMPUTING
ADVANTAGE
DISADVANTAGE
Low initial cost of entry. Cloud computing has lower costs of entry when compared with the alternative of building in house.
Issues of security, privacy, and compliance. Trusting sensitive company data to external entities is difficult for most data- cautious organizations.
Scalability/elasticity. It is easy to add and remove resources on demand.
Hidden costs of implementation and operation. It is hard to estimate bandwidth and data migration costs.
Support for mobile computing. Cloud computing providers support multiple types of mobile computing devices.
Data migration is a difficult and lengthy process. Migrating large amounts of data to and from the cloud infrastructure can be difficult and time-consuming.
Ubiquitous access. Consumers can access the cloud resources from anywhere at any time, as long as they have Internet access.
Complex licensing schemes. Organizations that implement cloud services are faced with complex licensing schemes and complicated service-level agreements.
High reliability and performance. Cloud providers build solid infrastructures that otherwise are difficult for the average organization to leverage.
Loss of ownership and control. Companies that use cloud services are no longer in complete control of their data. What is the responsibility of the cloud provider if data are breached? Can the vendor use your data without your consent?
Fast provisioning. Resources can be provisioned on demand in a matter of minutes with minimal effort.
Organization culture. End users tend to be resistant to change. Do the savings justify being dependent on a single provider? Will the cloud provider be around in 10 years?
Managed infrastructure. Most cloud implementations are managed by dedicated internal or external staff. This allows the organization’s IT staff to focus on other areas.
Difficult integration with internal IT system. Configuring the cloud services to integrate transparently with internal authentication and other internal services could be a daunting task.
As the table shows, the top-perceived benefit of cloud computing is the lower cost of entry. At the same time, the chief concern of cloud computing is data security and pri- vacy, particularly in companies that deal with sensitive data and are subject to high levels of regulation and compliance. This concern leads to the perception that cloud services are mainly implemented in small to medium-sized companies where the risk of service loss is minimal. In fact, some companies that are subject to strict data security regula- tions tend to favor private clouds rather than public ones.
One of the biggest growth segments in cloud services is mobile computing. For example, Netflix, the video-on-demand trailblazer, moved significant parts of its IT infrastructure to AWS. Netflix decided to move to the cloud because of the challenges of building IT infrastructure fast enough to keep up with its relentless growth.
Note
Cloud Reality Check: Is the Cloud Enterprise-Ready?
Cloud service outages and security breach incidents are reported every year. Such inci- dents affect all types and sizes of organizations from data breaches in large universities to service interruptions in cloud infrastructure providers. Some are very public, such as the iCloud security breach that allowed hackers to steal thousands of private pictures from well-known celebrities. Other incidents could affect millions of people all over the world, such as recent interruptions in social media services (Instagram, Vines, and Twitter.) These incidents can cause service interruption, data loss, performance degradation, or cost mil- lions of dollars in lost business.
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Regardless of a company’s size, databases remain at the center of all system devel- opment. Cloud computing brings a new dimension to data management that is within reach of any type of organization.
15-4e SQL Data Services
As you have seen in this chapter, data access technologies have evolved from simple ODBC data retrieval to advanced remote data processing using ADO.NET and XML. At the same time, companies are looking for ways to better manage ever-growing amounts of data while controlling costs without sacrificing data management features. Cloud computing provides a relatively stable and reliable platform for developing and deploy- ing business services; cloud vendors have expanded their business to offer SQL data ser- vices. SQL data services (SDS) refers to a cloud computing-based data management service that provides relational data storage, access, and management to companies of all sizes without the typically high costs of in-house hardware, software, infrastructure, and personnel. This type of service provides some unique benefits:
• Hosted data management. SDS typically uses a cluster of database servers that provide a large subset of database functionality over the Internet to database administrators and users. Typically, features such as SQL queries, indexing, stored procedures, trig- gers, reporting, and analytical functions are available to end users. Other features such as data synchronization, data backup and restore, and data importing and exporting are available for administrative purposes.
• Standard protocols. SDS uses standard data communication and relational data access protocols. Typically, these services encapsulate SQL networking protocols, such as SQL-Net for Oracle databases and Tabular Data Services (TDS) for Microsoft SQL Server databases, inside the TCP/IP networking protocol.
• A common programming interface. SDS is transparent to application developers. Programmers continue to use familiar programming interfaces such as ADO.NET and Visual Studio .NET to manipulate the data. Programmers write embedded SQL code in their applications and connect to the database as if the data was stored locally instead of in a remote location on the Internet. One potential disadvantage, however, is that some specialized data types may not be supported by SDS.
SQL data services offer the following advantages when compared with in-house systems:
• Highly reliable and scalable relational database for a fraction of the cost
• High level of failure tolerance because data is normally distributed and replicated among multiple servers
• Dynamic and automatic load balancing
• Automated data backup and disaster recovery included with the service
• Dynamic creation and allocation of database processes and storage
Cloud providers such as Amazon and Microsoft allow you to get your own database server running in a matter of minutes. Even better, you do not have to worry about backups, fault tolerance, scalability, and routine maintenance tasks. The use of SQL data services enables rapid application development for businesses with limited information technology resources, and allows them to rapidly deploy business solutions. A consumer of cloud services is free to use the database to create the best solution for the problem at hand. However, having access to relational database technology via a SQL data service is just the start—you still need to be knowledgeable in database design and SQL to develop high-quality applications.
arning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
SQL data services (SDS)
Data management services that provide relational data storage, access, and management over the Internet.
Copyright 2017 Cengage Le Editorial review has deemed that

• Database connectivity refers to the mechanisms through which application programs connect and communicate with data repositories. Database connectivity software is also known as database middleware.
• Microsoft database connectivity interfaces are dominant players in the market and enjoy the support of most database vendors. ODBC, OLE-DB, and ADO.NET form the backbone of Microsoft’s Universal Data Access (UDA) architecture.
• Native database connectivity refers to the connection interface that is provided by the database vendor and is unique to that vendor. ODBC is probably the most widely supported database connectivity interface. ODBC allows any Windows application to access relational data sources using standard SQL. Data Access Objects (DAO) is an older, object-oriented application interface. Remote Data Objects (RDO) is a higher-level, object-oriented application interface used to access remote database servers. RDO was optimized to deal with server-based databases such as MS SQL Server and Oracle.
• Object Linking and Embedding for Database (OLE-DB) is database middleware developed with the goal of adding object-oriented functionality for access to rela- tional and nonrelational data. ActiveX Data Objects (ADO) provides a high-level, application-oriented interface to interact with OLE-DB, DAO, and RDO. Based on ADO, ADO.NET is the data access component of Microsoft’s .NET application devel- opment framework. Java Database Connectivity (JDBC) is the standard way to inter- face Java applications with data sources.
• Database access through the web is achieved through middleware. To improve the capabilities on the client side of the web browser, you must use plug-ins and other client-side extensions such as Java and JavaScript, or ActiveX and VBScript. On the server side, web application servers are middleware that expand the functionality of web servers by linking them to a wide range of services, such as databases, directory systems, and search engines.
• Extensible Markup Language (XML) facilitates the exchange of B2B and other data over the Internet. XML provides the semantics that facilitate the exchange, sharing, and manipulation of structured documents across organizational boundaries. XML produces the description and the representation of data, thus setting the stage for data manipulation in ways that were not possible before. XML documents can be validated through the use of document type definition (DTD) documents and XML schema definition (XSD) documents.
• Cloud computing is a computing model that provides ubiquitous, on-demand access to a shared pool of configurable resources that can be rapidly provisioned.
• SQL data services (SDS) refers to a cloud computing-based data management ser- vice that provides relational data storage, ubiquitous access, and local management to companies of all sizes. This service enables rapid application development for busi- nesses with limited information technology resources. SDS allows rapid deployment of business solutions using standard protocols and common programming interfaces.
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Chapter 15 Database Connectivity and Web Technologies 717
Summary

718 Part 5 Databases and the Internet
Key Terms
ActiveX
ActiveX Data Objects (ADO)
ADO.NET
application programming interface (API)
Call Level Interface (CLI)
client-side extension
cloud computing
cloud services
Common Gateway Interface (CGI)
community cloud
Data Access Objects (DAO) data source name (DSN) database middleware DataSet
document type definition (DTD)
dynamic-link library (DLL)
Extensible Markup Language (XML)
Infrastructure as a Service (IaaS)
Java
Java Database Connectivity (JDBC)
JavaScript
Microsoft .NET framework
Object Linking and Embedding for Database (OLE-DB)
Open Database Connectivity (ODBC)
Platform as a Service (PaaS) plug-in
private cloud
public cloud
Remote Data Objects (RDO)
script
server-side extension
Software as a Service (SaaS)
SQL data services (SDS)
stateless system
tags
Universal Data Access (UDA)
VBScript
web application server
web-to-database middleware
XML schema
XML schema definition (XSD)
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
Review Questions
1. Give some examples of database connectivity options and what they are used for.
2. What are ODBC, DAO, and RDO? How are they related?
3. What is the difference between DAO and RDO?
4. What are the three basic components of the ODBC architecture?
5. What steps are required to create an ODBC data source name?
6. What is OLE-DB used for, and how does it differ from ODBC?
7. Explain the OLE-DB model based on its two types of objects.
8. How does ADO complement OLE-DB?
9. What is ADO.NET, and what two new features make it important for application development?
10. What is a DataSet, and why is it considered to be disconnected?
11. What are web server interfaces used for? Give some examples.
12. Search the Internet for web application servers. Choose one and prepare a short presentation for your class.
13. What does this statement mean: “The web is a stateless system.” What implications does a stateless system have for database application developers?
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14. What is a web application server, and how does it work from a database perspective?
15. What are scripts, and what is their function? (Think in terms of database application development.)
16. What is XML, and why is it important?
17. What are document type definition (DTD) documents, and what do they do?
18. What are XML schema definition (XSD) documents, and what do they do?
19. What is JDBC, and what is it used for?
20. What is cloud computing, and why is it a “game changer”?
21. Name and contrast the types of cloud computing implementation.
Online Content
The Ch02 databases used in the Problems for this chap- ter are available at www. cengagebrain.com.
Chapter 15 Database Connectivity and Web Technologies 719
22. Name and describe the most prevalent characteristics of cloud computing services.
23. Using the Internet, search for providers of cloud services. Then, classify the types of services they provide (SaaS, PaaS, and IaaS).
24. Summarize the main advantages and disadvantages of cloud computing services.
25. Define SQL data services and list their advantages.
In the following exercises, you will set up database connectivity using MS Excel.
1. Use MS Excel to connect to the Ch02_InsureCo MS Access database using ODBC, and retrieve all of the AGENTs.
2. Use MS Excel to connect to the Ch02_InsureCo MS Access database using ODBC, and retrieve all of the CUSTOMERs.
3. Use MS Excel to connect to the Ch02_InsureCo MS Access database using ODBC, and retrieve the customers whose AGENT_CODE is equal to 503.
4. Create a System DSN ODBC connection called Ch02_SaleCo using the Administra- tive Tools section of the Windows Control Panel.
5. Use MS Excel to list all of the invoice lines for Invoice 103 using the Ch02_SaleCo System DSN.
6. Create a System DSN ODBC connection called Ch02_Tinycollege using the Admin- istrative Tools section of the Windows Control Panel.
7. Use MS Excel to list all classes taught in room KLR200 using the Ch02_TinyCollege System DSN.
To answer Problems 8−11, use Section 15-3a as your guide.
8. Create a sample XML document and DTD for the exchange of customer data.
9. Create a sample XML document and DTD for the exchange of product and pricing data.
10. Create a sample XML document and DTD for the exchange of order data.
11. Create a sample XML document and DTD for the exchange of student transcript data. Use your college transcript as a sample.
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Problems

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PART 6
Database Administration
16 Database Administration and Security
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Chapter 16
Database Administration and Security
In this chapter, you will learn:
• That data is a valuable business asset requiring careful management
• How a database plays a critical role in an organization
• That the introduction of a DBMS has important technological, managerial, and cultural
consequences for an organization
• About the database administrator’s managerial and technical roles
• About data security, database security, and the information security framework
• About several database administration tools and strategies
• How cloud-based data services impact the DBA’s role
• How various technical tasks of database administration are performed with Oracle
Preview
This chapter shows you the basis for a successful database administration strategy. Such a strategy requires that data be treated and managed as a valuable corporate asset.
In this chapter, you will learn about important data management issues by looking at the managerial and technical roles of the database administrator (DBA). This chapter also explores database security issues, such as the confidentiality, integrity, and availability of data. In our information-based society, a key aspect of data management is ensuring that data is protected against intentional or unintentional access by unauthorized personnel. It is also essential to ensure that data is available as needed, even in the face of natural disas- ter or hardware failure, and to maintain the integrity of the data in the database.
The chapter includes a discussion of database administration tools and the corpo- rate-wide data architectural framework. You will also learn how database administration management fits within classical organizational structures. Furthermore, you will learn about several considerations when evaluating cloud-based data services. Even though many new types of databases have emerged, recent studies1 show that relational databases still dominate the market share of the enterprise. Therefore, with the preponderance of relational databases in the market, it is important that you learn about some basic data- base administration tasks in Oracle RDBMS. Similar tasks can be performed in all major databases, such as Microsoft SQL Server, IBM DB2, Oracle MySQL, and so on.
Note
Data Files Available on cengagebrain.com
1 Emison, Joe Masters, “2014 State of Database Tech: Think Retro,” InformationWeek.com, 3/10/2014.
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Because it is purely conceptual, this chapter does not reference any data files

16-1 Data as a Corporate Asset
In Chapter 1, Database Systems, you learned that data is the raw material from which information is produced. Therefore, in today’s information-driven environment, data is a valuable asset that requires careful management.
To assess data’s monetary value, consider what is stored in a company database: data about customers, suppliers, inventory, operations, and so on. How many opportunities are lost if the data is lost? What is the actual cost of data loss? For example, an accounting firm that lost its entire database would incur significant direct and indirect costs. The firm’s problems would be magnified if the data loss occurred during tax season. Data loss puts any company in a difficult position. The company might be unable to handle daily operations effectively, it might lose customers who require quick and efficient service, and it might lose the opportunity to gain new customers.
Data is a valuable resource that can translate into information. If the information is accurate and timely, it can enhance the company’s competitive position and generate wealth. In effect, an organization is subject to a data-information-decision cycle; that is, the data user applies intelligence to data to produce information that is the basis of knowledge used in decision making. This cycle is illustrated in Figure 16.1.
Note in Figure 16.1 that decisions made by high-level managers trigger actions within the organization’s lower levels. Such actions produce additional data to be used for mon- itoring company performance. In turn, the additional data must be recycled within the data-information-decision framework. Thus, data forms the basis for decision making, strategic planning, control, and operations monitoring.
Efficient asset management is critical to the success of an organization. To manage data as a corporate asset, managers must understand the value of information. For some companies, such as credit reporting agencies, their only product is information, and their success is solely a function of information management.
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Chapter 16 Database Administration and Security 723
FIGURE 16.1 THE DATA-INFORMATION-DECISION-MAKING CYCLE
Analysis
User
applies intelligence over
Data
Decision making
used in
Knowledge
that is the basis of
Information
triggers
Actions
which generate more
to produce

724 Part 6
Database Administration
Most organizations continually seek new ways to leverage their data resources to get greater returns. This leverage can take many forms, from data warehouses that support improved customer relationships to tighter integration with customers and suppliers in support of the electronic supply chain. As organizations become more dependent on information, that information’s accuracy becomes more critical. Dirty data, or data that suffers from inaccuracies and inconsistencies, becomes an even greater threat. Data can become dirty for many reasons:
• Lack of enforcement of integrity constraints, such as not null, uniqueness, and refer- ential integrity
• Data-entry errors and typographical errors
• Use of synonyms and homonyms across systems
• Nonstandard use of abbreviations in character data
• Different decompositions of composite attributes into simple attributes across systems
Some causes of dirty data, such as improper implementation of constraints, can be addressed within an individual database. However, addressing other causes is more com- plicated. Some dirty data comes from the movement of data across systems, as in the creation of a data warehouse. Efforts to control dirty data are generally referred to as data quality initiatives.
Data quality is a comprehensive approach to ensuring the accuracy, validity, and timeliness of data. This comprehensive approach is important because data quality involves more than just clening dirty data; it also focuses on preventing future inaccu- racies and building user confidence in the data. Large-scale data quality initiatives tend to be complex and expensive projects, so the alignment of these initiatives with business goals is a must, as is buy-in from top management. While data quality efforts vary greatly from one organization to another, most involve the following:
• A data governance structure that is responsible for data quality
• Measurements of current data quality
• Definition of data quality standards in alignment with business goals
• Implementation of tools and processes to ensure future data quality
A number of tools can assist in data quality initiatives. In particular, data-profiling and master data management software are available from many vendors. Data-profiling software gathers statistics, analyzes existing data sources and metadata to determine data pat- terns, and compares the patterns against standards that the organization has defined. This anal- ysis can help to assess the quality of existing data and identify sources of dirty data. Master data management (MDM) software helps to prevent dirty data by coordinating common data across multiple systems. MDM software provides a “master” copy of entities, such as cus- tomers, that appear in numerous systems throughout the organization.
While these technological approaches provide an important part of data quality, the overall solution to high-quality data within an organization still relies heavily on data administration and management.
16-2 The Need for a Database and its Role in an Organization
Data is used by different people in different departments for various reasons. There- fore, data management must address the concept of shared data. Chapter 1 showed how
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
dirty data
Data that contain inaccuracies and/or inconsistencies.
data quality
A comprehensive approach to ensuring the accuracy, validity, and timeliness of data.
data profiling software
Programs that analyze data and metadata to determine patterns that can help assess data quality.
master data management (MDM) software
Software the provides a “master copy” of entities such as customers, that appear in numerous systems throughout
the organization.
This software helps prevent dirty data by coordinating common data across multiple systems.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 16 Database Administration and Security 725 the need for data sharing made the DBMS almost inevitable. Used properly, the DBMS
facilitates:
• Interpretation and presentation of data in useful formats by transforming raw data into information
• Distribution of data and information to the right people at the right time
• Data preservation and monitoring data usage for adequate periods of time
• Control over data duplication and use, both internally and externally
Regardless of the organization, the database’s predominant role is to support mana- gerial decision making at all levels in the organization while preserving data privacy and security.
An organization’s managerial structure might be divided into three levels: top-level management makes strategic decisions, middle management makes tactical decisions, and operational management makes daily working decisions. Operational decisions are short term; for example, a manager might change the price of a product to clear it from inventory. Tactical decisions involve a longer time frame and affect larger-scale operations—for example, changing the price of a product in response to competitive pressures. Strategic decisions affect the long-term well-being of the company or even its survival—for example, changing the pricing strategy across product lines to capture market share.
The DBMS must give each level of management a useful view of the data and support the required level of decision making. The following activities are typical of each man- agement level.
At the top management level, the database must be able to:
• Provide the information necessary for strategic decision making, strategic planning,
policy formulation, and goals definition.
• Provide access to external and internal data to identify growth opportunities and to chart the direction of such growth. (Direction refers to the nature of the operations: will a company become a service organization, a manufacturing organization, or some combination of the two?)
• Provide a framework for defining and enforcing organizational policies that are trans- lated into business rules at lower levels in the organization.
• Improve the likelihood of a positive return on investment by searching for new ways to reduce costs and boost productivity in the company.
• Provide feedback to monitor whether the company is achieving its goals. At the middle management level, the database must be able to:
• Deliver the data necessary for tactical decisions and planning.
• Monitor and control the allocation and use of company resources and evaluate the
performance of various departments.
• Provide a framework for enforcing and ensuring the security and privacy of the data
in the database. Security means protecting the data against accidental or intentional use by unauthorized users. In the context of database administration, privacy is the extent to which individuals and organizations have the right to determine the details of data usage (who, what, when, where, and how).
At the operational management level, the database must be able to:
• Represent and support company operations as closely as possible. The data model
must be flexible enough to incorporate all current and future data.
security
Activities and
measures to ensure the confidentiality, integrity, and availability of an information system and its main asset, data.
privacy
The rights of individuals and organizations to determine access to data about themselves.
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• Produce query results within specified performance levels. Keep in mind that the performance requirements increase for lower levels of management and operations. Thus, the database must support fast responses to a greater number of transactions at the operational management level.
• Enhance the company’s short-term operations by providing timely information for customer support and for application development and computer operations.
A general objective for any database is to provide a seamless flow of information throughout the company.
The company’s database is also known as the corporate or enterprise database. The enterprise database might be defined as the company’s data representation that pro- vides support for all present and expected future operations. Most of today’s successful organizations depend on the enterprise database to provide support for all of their oper- ations—from design to implementation, from sales to services, and from daily decision making to strategic planning.
16-3 Introduction of a Database: Special Considerations
Having a computerized database management system does not guarantee that the data will be properly used to provide the best solutions required by managers. A DBMS is a tool for managing data; like any tool, it must be used effectively to pro- duce the desired results. In the hands of a carpenter, a hammer can help produce furniture, but in the hands of a child it might do damage. The solution to company problems is not the mere existence of a computer system or its database, but its effective management and use.
The introduction of a DBMS represents a big change and challenge. Throughout the orga- nization, the DBMS is likely to have a profound impact, which might be positive or negative depending on how it is administered. For example, one key consideration is to adapt the DBMS to the organization rather than forcing the organization to adapt to the DBMS. The main issue should be the organization’s needs rather than the DBMS’s technical capabilities. However, the introduction of a DBMS (internally hosted or outsourced to a cloud service) cannot be accom- plished without affecting the organization. The flood of new information has a profound effect on the way the organization functions and therefore on its corporate culture.
The introduction of a DBMS has been described as a process that includes three important aspects:2
• Technological—DBMS software and hardware
• Managerial—Administrative functions
• Cultural—Corporate resistance to change
The technological aspect includes selecting, installing, configuring, and monitoring the DBMS to make sure that it efficiently handles data storage, access, and security. The personnel in charge of installing the DBMS must have the technical skills to provide or secure adequate support for various users of the system: programmers, managers, and end users. Therefore, database administration staffing is a key technological consideration. The selected personnel must have the right mix of technical and managerial skills to provide a smooth transition to the new shared-data environment. In today’s IT world, the technological aspects would apply to both internally hosted DBMS as well as cloud-based data environments.
2 Murray, John P., “The Managerial and Cultural Issues of a DBMS,” 370/390 Database Management 1(8), September 1991, pp. 32–33.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
enterprise database
The overall company data representation, which provides support for present and expected future needs.
Copyright 2017 Cengage Lea Editorial review has deemed that

The managerial aspect of the DBMS introduction should not be taken lightly. A high-quality DBMS does not guarantee a high-quality information system, just as hav- ing the best race car does not guarantee winning a race. Such managerial aspects would also include the management of the services and the relationship with the cloud-based data services provider.
The introduction of a DBMS requires careful planning to create an appropriate orga- nizational structure and accommodate the personnel responsible for administering the system. This structure must also be subject to well-developed monitoring and controls. The administrative personnel must have excellent interpersonal and communications skills combined with broad organizational and business understanding. Top manage- ment must be committed to the new system and must define and support data adminis- tration functions, goals, and roles within the organization.
The cultural impact of the new database system must be assessed carefully. The DBMS is likely to have an effect on people, functions, and interactions. For example, addi- tional personnel might be hired, new roles might be allocated to existing personnel, and employee performance might be evaluated using new standards.
A cultural impact is likely because the database approach creates a more controlled and structured information flow. Department managers who are accustomed to han- dling their own data must surrender ownership and share their data with the rest of the company. Application programmers must learn and follow new design and development standards. Managers might perceive an information overload and require time to adjust to the new environment.
When the new database comes online, people might be reluctant to use its infor- mation and might question its value or accuracy. Many might be disappointed that the information does not fit their preconceived notions and strongly held beliefs. Database administrators must be prepared to open their doors to end users, listen to their con- cerns, act on those concerns when possible, and explain the system’s uses and benefits.
16-4 The Evolution of Database Administration
Data administration has its roots in the old, decentralized world of the file system. The cost of data and managerial duplication in these systems gave rise to centralized data administration known as the electronic data processing (EDP) or data processing (DP) department. The DP department’s task was to pool all computer resources to support all departments at the operational level. DP administrators were given the authority to manage all company file systems as well as resolve data and managerial conflicts created by the duplication and misuse of data.
The advent of the DBMS and its shared view of data produced a new level of data management sophistication and led the DP department to evolve into an information systems (IS) department. The responsibilities of the IS department were broadened to include the following:
• A service function to provide end users with data management support
• A production function to provide end users with solutions for their information needs
through integrated application or management information systems
The function of the IS department was reflected in its internal structure; a typical structure is shown in Figure 16.2.
As demand grew, the IS application development segment was subdivided by the type of system it supported: accounting, inventory, marketing, and so on. However, this devel- opment meant that database administration responsibilities were divided. The applica- tion development segment was in charge of gathering database requirements and logical
information systems (IS) department
A department responsible for all information technology services and production functions in an organization.
Chapter 16 Database Administration and Security 727
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FIGURE 16.2 THE IS DEPARTMENT’S INTERNAL ORGANIZATION
Information systems (IS)
Application development
Database operations
database design, whereas the database operations segment took charge of implementing, monitoring, and controlling DBMS operations.
As the number of database applications grew, data management became increas- ingly complex, thus leading to the development of database administration. The per- son responsible for control of the centralized and shared database became known as the database administrator (DBA).
The size and role of the DBA function varies from company to company, as does its placement within the organizational structure. On the organizational chart, the DBA func- tion might be defined as either a staff or line position. In a staff position, the DBA often takes on a consulting role; the DBA can devise the data administration strategy but does not have the authority to enforce it or resolve possible conflicts.3 In a line position, the DBA has both the responsibility and authority to plan, define, implement, and enforce the policies, standards, and procedures used in data administration. The two possible DBA positions are illustrated in Figure 16.3.
There is no standard for how the DBA function fits in an organization’s structure, partly because the function itself is probably the most dynamic of any in an organiza- tion. In fact, the fast-paced changes in DBMS technology dictate changing organizational styles. For example:
• The development of distributed databases can force an organization to decentralize data administration further. The distributed database requires the system DBA to define and delegate the responsibilities of each local DBA, thus imposing new and more complex coordinating activities on the system DBA.
• The growing use of Internet-accessible data and the growing number of data warehousing applications are likely to expand the DBA’s data-modeling and design activities.
• The increasing sophistication and power of personal-computer-based DBMS packages provide an easy platform for developing user-friendly, cost-effective, and efficient solutions. However, such an environment also invites data dupli- cation, not to mention the problems created by people who lack the technical qualifications to produce good database designs. In short, the new computing environment requires the DBA to develop a new set of technical and managerial skills.
• The increasing use of cloud data services is pushing many database platforms and infrastructures into the cloud. This can free the DBA from many lower-level
3 For a historical perspective on the development of the DBA function, refer to Jay-Louise Weldon’s classic Data Base Administration (New York, Plenum Press, 1981). Although you might think that the book’s publi- cation date renders it obsolete, a surprising number of its topics are relevant to current databases.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
database administrator (DBA) The person responsible for planning, organizing, controlling, and monitoring the centralized and shared corporate database. The DBA is the general manager of the database administration department.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 16 Database Administration and Security 729
FIGURE 16.3 THE PLACEMENT OF THE DBA FUNCTION
Application development
Line Authority Position
Information systems (IS)
Database operations
Staff Consulting Position
Information systems (IS)
Database operations
Database administration
Database administration
Application development
technology-oriented tasks, allowing DBAs to focus on higher-value strategic issues. In such environments, the DBA becomes a data use service provider and advisor for the organization.
• Conversely, the growing use of Big Data in organizations can force the DBA to become more technology-oriented. Ongoing efforts to integrate Hadoop storage systems with both NoSQL and relational databases require DBAs to be familiar with the lower-level storage and access issues that are still dominant in those emerging disciplines.
DBA operations are commonly defined and divided according to the phases of the Database Life Cycle (DBLC). If that approach is used, the DBA function requires person- nel to cover the following activities:
• Database planning, including the definition of standards, procedures, and enforcement
• Database requirements gathering and conceptual design
• Database logical and transaction design
• Database physical design and implementation
• Database testing and debugging
• Database operations and maintenance, including installation, conversion, and
migration
• Database training and support
• Data quality monitoring and management
Figure 16.4 represents a DBA functional organization according to the preceding model.
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FIGURE16.4 ADBAFUNCTIONALORGANIZATION
Planning Design
Conceptual
DBA
Implementation
Operations Training
Testing
Logical
Physical
Keep in mind that a company might have several incompatible DBMSs installed to support different operations. For example, some corporations have a hierarchical DBMS to support daily transactions at the operational level and a relational database to support middle and top management’s ad hoc information needs. A variety of personal computer DBMSs might be installed in different departments. In such an environment, the company might have one DBA assigned for each DBMS. The general coordinator of all DBAs is sometimes known as the systems administrator; that position is illustrated in Figure 16.5.
systems administrator
The person responsible for coordinating and performing day-to-day data-processing activities.
FIGURE16.5 MULTIPLEDATABASEADMINISTRATORSINANORGANIZATION
DBA
DB2 relational
DBA
Oracle relational
Systems administrator
DBA
MongoDB NoSQL
DBA
SQL Server relational
Microcomputer DBMS manager
data administrator (DA)
The person responsible for managing the entire data resource, whether it is computerized or not. The DA has broader authority and responsibility than the database administrator (DBA). Also known as
an information resource manager (IRM).
information resource manager (IRM)
See data administrator (DA).
There is a growing trend toward specialization in data management. For exam- ple, the organizational charts used by some larger corporations make a distinction between a DBA and the data administrator (DA). The DA, also known as the information resource manager (IRM), usually reports directly to top manage- ment and is given a higher degree of responsibility and authority than the DBA, although the two roles can overlap.
The DA is responsible for controlling the overall corporate data resources, both com- puterized and manual. Thus, the DA’s job covers more operations than the DBA’s because the DA controls data outside the scope of the DBMS in addition to computerized data. Depending on an organization’s structure, the DBA might report to the DA, the IRM, the IS manager, or directly to the company’s CEO.
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Chapter 16 Database Administration and Security 731
16-5 The Database Environment’s Human Component
A substantial portion of this book is devoted to relational database design and imple- mentation, and to DBMS features and characteristics. Thus far, the book has focused on very important technical aspects of the database. However, even the most carefully crafted database system cannot operate without human assistance. In this section, you will explore how people perform the data administration activities that make a good database design useful.
Effective data administration requires both technical and managerial skills. For exam- ple, the DA’s job typically has a strong managerial orientation with company-wide scope, along with a technical orientation that has a narrower, DBMS-specific scope. However, the DBA also must have considerable people skills. For example, both the DA and DBA direct and control personnel staffing and training within their respective departments.
Table 16.1 contrasts the general characteristics of both positions by summarizing typical DA and DBA activities. All of these activities are assigned to the DBA if the organization does not employ both a DA and a DBA.
TABLE 16.1
CONTRASTING DA AND DBA ACTIVITIES AND CHARACTERISTICS
DATA ADMINISTRATOR (DA)
DATABASE ADMINISTRATOR (DBA)
Performs strategic planning
Controls and supervises
Sets long-term goals
Executes plans to reach goals
Sets policies and standards
Enforces policies and procedures Enforces programming standards
Job is broad in scope
Job is narrow in scope
Focuses on the long term
Focuses on the short term (daily operations)
Has a managerial orientation
Has a technical orientation
Is DBMS-independent
Is DBMS-specific
Note that the DA provides a global and comprehensive administrative strategy for the organization’s data. In other words, the DA’s plans must consider the entire data spec- trum. Thus, the DA is responsible for the consolidation and consistency of both manual and computerized data.
The DA must also set data administration goals. Those goals are defined by issues such as:
• Data “sharability” and time availability
• Data consistency and integrity
• Data security and privacy
• Data quality standards
• Extent and type of data use
Naturally, the list can be expanded to fit an organization’s specific data needs. Regard- less of how data management is conducted—and despite the fact that great authority is invested in the DA or DBA to define and control the way company data is used—the DA and DBA do not own the data. Instead, their functions are defined to emphasize that data is a shared company asset.
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The preceding discussion should not lead you to believe that there are universally accepted DA and DBA administrative standards. The style, duties, organizational place- ment, and internal structure of both functions vary from company to company. For example, many companies distribute DA duties between the DBA and the manager of information systems. For simplicity and to avoid confusion, the label DBA is used here as a general title that encompasses all appropriate data administration.
The arbitration of interactions between the two most important assets of any organization, people and data, places the DBA in the dynamic environment portrayed in Figure 16.6.
As you examine Figure 16.6, note that the DBA is the focal point for data and user interaction. The DBA defines and enforces the procedures and standards to be used by programmers and end users during their work with the DBMS. The DBA also verifies that programmer and end-user access meets the required quality and security standards.
Database users might be classified by the following criteria:
• Type of decision-making support required (operational, tactical, or strategic)
• Degree of computer knowledge (novice, proficient, or expert)
• Frequency of access (casual, periodic, or frequent)
These classifications are not exclusive and usually overlap. For example, an operational user can be an expert with casual database access, or a top-level manager might be a strategic novice user with periodic database access. On the other hand, a database appli- cation programmer is an operational expert and frequent database user. Thus, each orga- nization employs people whose levels of database expertise span an entire spectrum. The DBA must be able to interact with all of them, understand their different needs, answer questions at all levels of expertise, and communicate effectively.
The DBA activities portrayed in Figure 16.6 suggest the need for a diverse mix of skills. In large companies, such skills are likely to be distributed among several DBAs.
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FIGURE16.6 ASUMMARYOFDBAACTIVITIES
defines and enforces
DBA
verifies
used by
Procedures and standards
Programmer
Managers and clerks
manages and monitors
DBA interface
writes
use
Application DBMS programs
and/or
DBMS interface
manages
End users
a
t
D
a

Chapter 16 Database Administration and Security 733 In small companies, the skills might be the domain of just one DBA. The skills can be
divided into two categories—managerial and technical—as summarized in Table 16.2.
TABLE 16.2
DESIRED DBA SKILLS
MANAGERIAL
TECHNICAL
Broad business understanding
Broad data-processing background and up-to-date knowledge of database technologies
Coordination skills
Understanding of Systems Development Life Cycle
Analytical skills
Structured methodologies
• Data flow diagrams
• Structure charts
• Programming languages
Conflict resolution skills
Knowledge of Database Life Cycle
Communication skills (oral and written)
Database modeling and design skills • Conceptual
• Logical
• Physical
Negotiation skills
Operational skills: Database implementation, data dictionary management, security, and so on
Experience: 10 years in a large DP department
As you examine Table 16.2, keep in mind that the DBA must perform two distinct roles. The DBA’s managerial role is focused on personnel management and on interac- tions with end users. The DBA’s technical role involves the use of the DBMS—database design, development, and implementation—as well as the production, development, and use of application programs. Both roles are examined in greater detail in the following sections.
16-5a The DBA’s Managerial Role
As a manager, the DBA must concentrate on the control and planning of database
administration. Therefore, the DBA is responsible for the following:
• Coordinating, monitoring, and allocating database administration resources: people and data
• Defining goals and formulating strategic plans for database administration More specifically, the DBA’s responsibilities are shown in Table 16.3.
TABLE 16.3
DBA ACTIVITIES AND SERVICES
DBA ACTIVITY
DBA SERVICE
Planning
End-user support
Organizing
Policies, procedures, and standards
Testing
of
Data security, privacy, and integrity
Monitoring
Data backup and recovery
Delivering
Data distribution and use
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Table 16.3 illustrates that the DBA is generally responsible for planning, organizing, testing, monitoring, and delivering quite a few services. Those services might be per- formed by the DBA, although they are more likely to be performed by the DBA’s person- nel. The following sections examine the services in greater detail.
End-UserSupport TheDBAinteractswithendusersbyprovidingdataandinformation support to their departments. Because end users usually have dissimilar computer back- grounds, support services include the following:
• Gathering user requirements. The DBA must work with end users to help gather the data required to identify and describe their present and future information needs. The DBA’s communication skills are important in working closely with people who have varying computer backgrounds and communication styles.
• Building end-user confidence. Finding adequate solutions to end users’ problems increases their trust and confidence in the DBA. The DBA also should educate end users about the services provided and how they enhance data stewardship and data security.
• Resolving conflicts and problems. Finding solutions to end users’ problems in one department might trigger conflicts with other departments. End users are typically concerned with their own data needs rather than those of others, and they might not consider how their data might affect other departments within the organization. When conflicts arise, the DBA must have the authority and responsibility to resolve them.
• Finding solutions to information needs. The ability and authority to resolve data con- flicts enables the DBA to develop solutions that will properly fit within the data man- agement framework and address end users’ information needs. Given the growing importance of the Internet, those solutions are likely to require the development and management of web servers to interface with the databases. In fact, the explosive growth of e-commerce requires the use of dynamic interfaces to facilitate interactive product queries and product sales.
• Ensuring quality and integrity of data and applications. Once the right solution has been found, it must be properly implemented and used. The DBA must work with application programmers and end users to teach them the database standards and procedures required for data quality, access, and manipulation. The DBA must also make sure that the database transactions do not adversely affect data quality. Likewise, certifying the quality of application programs that access the database is a crucial DBA function. Special attention must be given to DBMS Internet interfaces because they are prone to security issues, particularly when using cloud data services.
• Managing the training and support of DBMS users. One of the most time-consuming DBA activities is teaching end users how to use the database. The DBA must ensure that all users understand the basic functions of the DBMS software. The DBA coordi- nates and monitors all DBMS training activities.
Policies, Procedures, and Standards A successful data administration strategy requires the continuous enforcement of policies, procedures, and standards for correct data cre- ation, usage, and distribution within the database. The DBA must define, document, and communicate the following before they can be enforced:
• Policies are general statements of direction or action that communicate and support DBA goals.
• Standards describe the minimum requirements of a given DBA activity; they are more detailed and specific than policies. In effect, standards are rules that evaluate
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policy
General statement of direction that is used
to manage company operations through
the communication
and support of the organization’s objectives.
standard
A detailed and specific set of instructions that describes the minimum requirements for a given activity. Standards are used to evaluate the quality of the output.
Copyright 2017 Cengage Le Editorial review has deemed that

Chapter 16 Database Administration and Security 735 the quality of the activity. For example, standards define the structure of application
programs and the naming conventions programmers must use.
• Procedures are written instructions that describe a series of steps to be fol- lowed during the performance of a given activity. Procedures must be developed within existing working conditions, and they must support and enhance the work environment.
To illustrate the distinctions among policies, standards, and procedures, look at the following examples:
Policies
• All users must have passwords.
• Passwords must be changed every six months.
Standards
• A password must have a minimum of 5 characters.
• A password must have a maximum of 12 characters.
• Social Security numbers, names, and birth dates cannot be used as passwords.
Procedures
To create a password, (1) the end user sends the DBA a written request for the cre- ation of an account; (2) the DBA approves the request and forwards it to the computer operator; (3) the computer operator creates the account, assigns a temporary password, and sends the account information to the end user; (4) a copy of the account information is sent to the DBA; and (5) the user changes the temporary password to a permanent one.
Standards and procedures defined by the DBA apply to all end users who want to benefit from the database. Standards and procedures must complement each other and must constitute an extension of data administration policies. Procedures must facilitate the work of end users and the DBA. The DBA must define, communicate, and enforce procedures that cover areas such as:
• End-user database requirements gathering. What documentation is required? What forms must be used?
• Database design and modeling. What database design methodology will be used (nor- malization or object-oriented)? What tools will be used (CASE tools, data dictionar- ies, UML or ER diagrams)?
• Documentation and naming conventions. What documentation must be used in the definition of all data elements, sets, and programs that access the database?
• Design, coding, and testing of database application programs. The DBA must define the standards for application program coding, documentation, and testing. The DBA standards and procedures are given to the application programmers, and the DBA must enforce those standards.
• Database software selection. The selected DBMS must properly interface with existing software, have the features needed by the organization, and provide a positive return on investment. In today’s Internet environment, the DBA must also work with web and network administrators to implement efficient and secure web and cloud data- base connectivity.
• Database security and integrity. The DBA must define policies that govern security and integrity. Database security is especially crucial. Security standards must be
procedure
Series of steps to be followed during the performance of an activity or process.
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clearly defined and strictly enforced. Security procedures must handle a multitude of scenarios to ensure that problems are minimized. Although no system can ever be completely secure, procedures must meet critical standards. The growing use of Internet interfaces to databases opens the door to new security threats that are far more complex and difficult to manage than those in traditional interfaces—this is particularly important when working with cloud data services. Therefore, the DBA must work closely with Internet security specialists to ensure that the databases are properly protected from attacks.
• Database backup and recovery. Database backup and recovery procedures must include information that guarantees proper execution and management of the backups. The DBA must work closely with any cloud-based data services provider to ensure the proper procedures are in place to manage data backups and restores and to ensure ownership and security of the data.
• Database maintenance and operation. The DBMS’s daily operations must be clearly documented. Operators must keep job logs and must write operator instructions and notes. Such notes help pinpoint the causes and solutions of problems. Opera- tional procedures must also include precise instructions for backup and recovery procedures.
• End-user training. A full-featured training program must be established within the organization, and training procedures must be clearly specified. Each end user must be aware of available training.
Procedures and standards must be revised at least annually to keep them up to date and to ensure that the organization can adapt quickly to changes in the work environ- ment. Naturally, the introduction of new DBMS software, the discovery of security or integrity violations, company reorganizations, and similar changes require revision of procedures and standards.
Data Security, Privacy, and Integrity Data security, privacy, and integrity are of great concern to DBAs who manage DBMS installations. Technology has pointed the way to greater productivity through information management, and it has enabled the distri- bution of data across multiple sites, making it more difficult to maintain data control, security, and integrity. Thus, the DBA must use the security and integrity mechanisms provided by the DBMS to enforce the database administration policies defined in the previous section. In addition, DBAs must team up with Internet security experts to build security mechanisms that safeguard data from possible attacks or unauthorized access. Section 16-6 covers security issues in more detail.
Data Backup and Recovery When data is not readily available, companies face potentially ruinous losses. Therefore, data backup and recovery procedures are crit- ical in all database installations. The DBA must also ensure that data can be fully recovered in case of data loss or loss of database integrity. These losses can be partial or total; therefore, backup and recovery procedures are the cheapest database insur- ance you can buy.
The management of database security, integrity, backup, and recovery is so criti- cal that many DBA departments have created a position called the database security officer (DSO). The DSO’s sole job is to ensure database security and integrity. In large organizations, the DSO’s activities are often classified as disaster management.
Disaster management includes all of the DBA activities designed to secure data availability following a physical disaster or a database integrity failure. Disaster man- agement includes all planning, organizing, and testing of database contingency plans
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database security officer (DSO)
The person responsible for the security, integrity, backup, and recovery of the database.
disaster management
The set of DBA activities dedicated to securing data availability following a physical disaster or a database integrity failure.
Copyright 2017 Cengage Le Editorial review has deemed that

Chapter 16 Database Administration and Security 737 and recovery procedures. The backup and recovery measures must include at least the
following:
• Periodic data and application backups. Some DBMSs include tools to ensure automatic backup and recovery of the database. Products such as IBM’s DB2 allow different types of backups: full, incremental, and concurrent. A full backup, also known as a database dump, produces a complete copy of the entire database. An incremental backup pro- duces a backup of all data since the last backup date. A concurrent backup takes place while the user is working on the database.
• Proper backup identification. Backups must be clearly identified through detailed descriptions and date information, thus enabling the DBA to ensure that the correct backups are used to recover the database. The most common backup medium has traditionally been tape; computer operators must diligently store and label the tapes, and the DBA must keep track of the current tape’s location. However, organizations that are large enough to hire a DBA do not typically use tapes for enterprise backup. Other solutions include optical and disk-based backup devices. Such backup solu- tions include online storage based on network-attached storage (NAS), storage area networks (SAN), and cloud-based data storage. Enterprise backup solutions use a lay- ered approach in which the data is first backed up to fast disk media for intermediate storage and fast restoration. Later, the data is transferred to tape for archival storage.
• Convenient and safe backup storage. Multiple backups of the same data are required, and each backup copy must be stored in a different location. The storage locations must include sites inside and outside the organization. (Keeping different backups in the same place defeats the purpose of having multiple backups.) The storage locations must be properly prepared, and they may include fire-safe and quakeproof vaults as well as humidity and temperature controls. The DBA must establish a policy to respond to two questions: (1) Where are the backups to be stored? (2) How long are backups to be stored?
• Physical protection of both hardware and software. Protection might include the use of closed installations with restricted access, as well as preparation of the computer sites to provide air conditioning, backup power, and fire protection. Physical protection also includes a backup computer and DBMS to be used in case of emergency. For example, when Hurricane Sandy hit the east coast of North America in 2012, the U.S. Northeast suffered widespread destruction of its communications infrastructure. The storm served as a wake-up call for many organizations and educational institutions that did not have adequate disaster recovery plans for such an extreme level of service interruption.
• Personal access control to the software of a database installation. Multilevel passwords and privileges as well as hardware and software challenge/response tokens can be used to identify authorized users of resources.
• Insurance coverage for the data in the database. The DBA or security officer must buy an insurance policy to provide financial protection in the event of a database failure. The insurance might be expensive, but it is less expensive than the disaster created by massive data loss.
Two additional points are worth making are:
• Data recovery and contingency plans must be thoroughly tested and evaluated, and they must be practiced frequently. So-called fire drills should not be disparaged, and they require top-level management’s support and enforcement.
• A backup and recovery program is not likely to cover all components of an informa- tion system. Therefore, it is appropriate to establish priorities for the nature and extent of data recovery.
full backup (database dump)
A complete copy of an entire database saved and periodically updated in a separate memory location. A full backup ensures a full recovery of all data after a physical disaster or database integrity failure.
incremental backup
A process that only backs up data that has changed in the database since the last incremental or full backup.
concurrent backup
A backup that takes place while one or more users are working on a database.
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DataDistributionandUse Dataisusefulonlywhenitreachestherightusersinatimely fashion. The DBA is responsible for ensuring that data is distributed to the right people, at the right time, and in the right format. These tasks can become very time-consuming, especially when data delivery capacity is based on a typical applications programming environment, where users depend on programmers to deliver the programs that access the database. Although the Internet and its intranet and extranet extensions have opened databases to corporate users, they have also created a new set of challenges for the DBA.
Current data distribution philosophy makes it easy for authorized end users to access the database. One way to accomplish this task is to facilitate the use of new, more sophis- ticated query tools and new web front ends. They enable the DBA to educate end users to produce required information without being dependent on applications program- mers. Naturally, the DBA must ensure that users adhere to appropriate standards and procedures.
This data-sharing philosophy is common today, and it probably will become more common as database technology marches on. Such an environment is more flexible for end users; by becoming more self-sufficient in the acquisition and use of data, they can make better decisions. Yet, this “data democracy” can also produce some trouble- some side effects. Letting end users micromanage their data subsets could inadvertently sever the connection between those users and data administrators. The DBA’s job could become more complicated, and the efficiency of data administration could be com- promised. Data duplication might flourish again without checks at the organizational level to ensure the uniqueness of data elements. Thus, end users who do not completely understand the nature and sources of data might use the data elements improperly.
16-5b The DBA’s Technical Role
The DBA’s technical role requires a broad understanding of DBMS functions, config- uration, programming languages, and data-modeling and design methodologies. For example, the DBA’s technical activities include the selection, installation, operation, maintenance, and upgrading of the DBMS and utility software, as well as the design, development, implementation, and maintenance of application programs that interact with the database.
Many of the DBA’s technical activities are a logical extension of the DBA’s managerial activities. For example, the DBA deals with database security and integrity, backup and recovery, and training and support. The technical aspects of the DBA’s job are rooted in the following areas of operation:
• Evaluating, selecting, and installing the DBMS and related utilities
• Designing and implementing databases and applications
• Testing and evaluating databases and applications
• Operating the DBMS, utilities, and applications
• Training and supporting users
• Maintaining the DBMS, utilities, and applications
The following sections explore the details of each area.
Evaluating,Selecting,andInstallingtheDBMSandUtilities OneoftheDBA’sfirstand most important technical responsibilities is selecting the database management system, utility software, and supporting hardware to be used in the organization. The DBMS selection might also include the consideration of cloud-based data services. This task requires extensive planning, which must be based on the organization’s needs rather than
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specific software and hardware features. The DBA must recognize that the objective is solving problems rather than buying a computer or DBMS software. Put simply, a DBMS is a management tool and not a technological toy.
The first and most important step of the plan is to determine company needs. The DBA must make sure that all end users, including top-level and midlevel managers, are involved in the process. Once the needs are identified, the objectives of data adminis- tration can be clearly established and the DBMS features and selection criteria can be defined.
To match DBMS capability to the organization’s needs, the DBA would be wise to develop a checklist of desired DBMS features that addresses at least the following issues:
• DBMS model. Are the company’s needs better served by a relational, object-oriented, object/relational, or a NoSQL DBMS? If a data warehouse application is required, should a relational or multidimensional DBMS be used? Does the DBMS support star schemas? To determine which model is best, you need to identify the main goal of the application: is it high availability, high performance, transaction accuracy (ACID enforcement), or being able to manage a variety of types of data and complex relationships?
• DBMS storage capacity. What maximum disk and database sizes are required? How many disk packages must be supported? What is the minimum number of indepen- dent disk spindles required for the “recommended” installation? What are other stor- age needs? If using a cloud storage service, in addition to initial data size, special attention should be given to expected data growth rates because of contracted incre- mental data storage costs. Cloud storage introduces issues such as location, security, replication, redundancy, and data synchronization.
• Application development support. Which programming languages are supported? What application development tools are available? (Options include database schema design, a data dictionary, performance monitoring, and screen and menu painters.) Are end-user query tools provided? Does the DBMS provide web front-end access?
• Security and integrity. Does the DBMS support referential and entity integrity rules, access rights, and so on? Does the DBMS support the use of audit trails to spot errors and security violations? Can the audit trail’s size be modified? If the data is stored in a public cloud, how secure is the data?
• Backup and recovery. Does the DBMS provide automated backup and recovery tools? Does the DBMS support tape, optical disc, or network-based backups? Does the DBMS automatically back up the transaction logs?
• Concurrency control. Does the DBMS support multiple users? What levels of isolation (table, page, row) does the DBMS offer? How much manual coding is needed in the application programs?
• Performance. How many transactions per second does the DBMS support? Are addi- tional transaction processors needed? Is an in-memory database required to ensure top performance?
• Database administration tools. Does the DBMS offer some type of DBA management interface? What type of information does the DBA interface provide? Does the DBMS provide alerts to the DBA when errors or security violations occur?
• Interoperability and data distribution. Can the DBMS work with other DBMS types in the same environment? What coexistence or interoperability level is achieved? Does the DBMS support read and write operations to and from other DBMS packages? Does the DBMS support a client/server architecture? Would a cloud-based data ser- vice be a better choice for the given system?
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• Portability and standards. Can the DBMS run on different operating systems and plat- forms? Can the DBMS run on mainframes, midrange computers, and personal com- puters? Can the DBMS applications run without modification on all platforms? What national and industry standards does the DBMS follow?
• Hardware. What hardware does the DBMS require? Can the DBMS run in a virtual machine? Does the DBMS implementation require the use of hardware clusters or a distributed environment?
• Data dictionary. Does the DBMS have an “accessible” data dictionary? Does the DBMS interface with any data dictionary tool? Does the DBMS support any open management tools?
• Vendor training and support. Does the vendor offer in-house training? What type and level of support does the vendor provide? Is the DBMS documentation easy to read and helpful? What is the vendor’s upgrade policy?
• Available third-party tools. What additional tools are offered by third-party vendors? Do they include query tools, a data dictionary, access management and control, and storage allocation management tools?
• Costs. What costs are involved in the acquisition of the software and hardware? How many additional personnel are required, and what level of expertise is required of them? What are the recurring costs? What is the expected payback period?
If cloud data services are being considered, there are additional issues that need to be addressed with any potential cloud provider. Recall that the use of cloud databases frees the client organization from costs of acquiring and implementing the infrastructure as well as daily costs of maintenance. However, these services come with a loss of control over the data and the infrastructure. Any potential cloud-based vendors need to be eval- uated based on several factors, including:
• Downtime history. Historically, how often are the cloud provider’s services unavail- able, and what provisions will they make to ensure that your data is always accessible?
• Security. How does the provider secure your data using firewalls, authentication, security audits, and encryption? Who at the cloud company will have access to your data files?
• Support. What customer support options are available if the client has issues or con- cerns with the data services provided?
• Data loss contingencies. The expectation is that the cloud provider will keep the data safe. However, what happens if they lose the client’s data? What type of compensation or insurance against data loss is provided? What types of redundancies and backups are used to ensure that data loss will not happen? Where are the backups and redun- dancies kept to ensure that a natural disaster in one geographic area cannot cause the loss of all copies of the data?
Pros and cons of several alternative solutions must be evaluated during the selection process. Available alternatives are often restricted because software must be compat- ible with the organization’s existing computer system. Remember that a DBMS is just part of a solution; it requires support from collateral hardware, application software, and utility programs. For example, the DBMS’s use is likely to be constrained by the available CPU(s), front-end processor(s), auxiliary storage devices, data communica- tion devices, the operating system, a transaction processor system, and so on. The costs associated with the hardware and software components must be included in the estimations.
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The selection process must also consider the site’s preparation costs. For example, the DBA must include both one-time and recurring expenditures for preparing and main- taining the computer room installations.
The DBA must supervise the installation of all software and hardware that supports the data administration strategy, and must thoroughly understand the components being installed, including their installation, configuration, and startup procedures. The installation procedures include the location of backup and transaction log files, network configuration information, and physical storage details.
Keep in mind that installation and configuration details are DBMS-dependent. There- fore, such details cannot be addressed in this book. Consult the installation and configu- ration sections of your system’s DBMS administration guide for details.
Designing and Implementing Databases and Applications The DBA also provides data-modeling and design services to end users. Such services are often coordinated with an application development group within the data-processing department. There- fore, one of the primary activities of a DBA is to determine and enforce standards and procedures to be used. Once a framework of appropriate standards and procedures are in place, the DBA must ensure that the database-modeling and design activities are per- formed within the framework. The DBA then provides necessary assistance and support during the design of the database at the conceptual, logical, and physical levels. (Remem- ber that the conceptual design is both DBMS- and hardware-independent, the logical design is DBMS-dependent and hardware-independent, and the physical design is both DBMS- and hardware-dependent.)
The DBA function usually requires that several people be dedicated to database model- ing and design activities. Those people might be grouped according to the organizational areas covered by the application. For example, database modeling and design personnel may be assigned to production systems, financial and managerial systems, or executive and decision support systems. The DBA schedules the design jobs to coordinate the data design and modeling activities. That coordination may require reassignment of available resources based on externally determined priorities.
The DBA also works with application programmers to ensure the quality and integrity of database design and transactions. Such support services include reviewing the data- base application design to ensure that transactions are:
• Correct. The transactions mirror real-world events.
• Efficient. The transactions do not overload the DBMS.
• Compliant. Transactions comply with integrity rules and standards.
These activities require personnel with broad database design and programming skills.
The implementation of the applications requires the implementation of the physical database. Therefore, the DBA must provide assistance and oversight during the physical design, including determination and creation of storage space, data loading, conversion, and database migration services. The DBA’s implementation tasks also include the gen- eration, compilation, and storage of the application’s access plan. An access plan is a set of instructions generated when the application is compiled that predetermines how the application will access the database at run time. To be able to create and validate the access plan, the user must have the required rights to access the database (see Chapter 11, Database Performance Tuning and Query Optimization).
Before an application comes online, the DBA must develop, test, and implement the operational procedures required by the new system. Such procedures include training, security, and backup and recovery plans, as well as assigning responsibility for database
access plan
A set of instructions generated at application compilation time that is created and managed by a DBMS. The access plan predetermines how an application’s query will access the database at run time.
Chapter 16 Database Administration and Security 741
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control and maintenance. Finally, the DBA must authorize application users to access the database from which the applications draw the required data.
The addition of a new database might require fine-tuning or reconfiguring of the DBMS. Remember that the DBMS assists all applications by managing the shared corpo- rate data repository. Therefore, when data structures are added or modified, the DBMS might require the assignment of additional resources to serve new and original users with equal efficiency (see Chapter 11).
Testing and Evaluating Databases and Applications The DBA must also provide test- ing and evaluation services for all database and end-user applications. These services are the logical extension of the design, development, and implementation services described in the preceding section. Testing procedures and standards must already be in place before any application program can be approved for use in the company.
Although testing and evaluation services are closely related to database design and implementation services, they usually are maintained independently. The reason for the separation is that application programmers and designers are often too close to the prob- lem being studied to detect errors and omissions.
Testing usually starts with the loading of the “test bed” database, which contains test data for the applications. Its purpose is to check the data definition and integrity rules of the database and application programs.
The testing and evaluation of a database application cover all aspects of the system, from the simple collection and creation of data to its use and retirement. The evaluation process covers the following:
• Technical aspects of both the applications and the database; backup and recov- ery, security and integrity, use of SQL, and application performance must be evaluated
• Evaluation of the written documentation and procedures to ensure that they are accu- rate and easy to follow
• Observance of standards for naming, documenting, and coding
• Checking for data duplication conflicts with existing data
• The enforcement of all data validation rules
Following the thorough testing of all applications, the database, and the procedures, the system is declared operational and can be made available to end users.
OperatingtheDBMS,Utilities,andApplications DBMSoperationscanbedividedinto four main areas:
• System support
• Performance monitoring and tuning
• Backup and recovery
• Security auditing and monitoring
System support activities cover all tasks directly related to the day-to-day operations of the DBMS and its applications. These activities include filling out job logs, changing tape, and verifying the status of computer hardware, disk packages, and emergency power sources. System-related activities include periodic tasks such as running spe- cial programs and resource configurations for new and upgraded versions of database applications.
Performance monitoring and tuning require much of the DBA’s attention and time. These activities are designed to ensure that the DBMS, utilities, and applications
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Chapter 16 Database Administration and Security 743 maintain satisfactory performance levels. To carry out performance monitoring and
tuning tasks, the DBA must:
• Establish DBMS performance goals.
• Monitor the DBMS to evaluate whether the performance objectives are being met.
• Isolate the problem and find solutions if performance objectives are not met.
• Implement the selected performance solutions.
DBMSs often include performance-monitoring tools that allow the DBA to query database usage information. Performance-monitoring tools are available from many different sources: DBMS utilities are provided by third-party vendors, or they might be included in operating system utilities or transaction processor facilities. Most of the performance-monitoring tools allow the DBA to focus on selected system bottlenecks. The most common bottlenecks in DBMS performance tuning are related to the use of indexes, query optimization algorithms, and management of storage resources.
Because improper index selection can have a deleterious effect on system perfor- mance, most DBMS installations adhere to a carefully defined index creation and usage plan. Such a plan is especially important in a relational database environment.
To produce satisfactory performance, the DBA might train programmers and end users in the proper use of SQL statements. Typically, DBMS programming manuals and administration manuals contain useful performance guidelines and examples that demonstrate the proper use of SQL statements, both at the command line and within application programs. Because relational systems do not give the user an index choice within a query, the DBMS makes the index selection for the user. Therefore, the DBA should create indexes that can be used to improve system performance. (For examples of database performance tuning, see Chapter 11.)
Query optimization routines are usually integrated into the DBMS package, allowing few tuning options. Query optimization routines are oriented toward improving concur- rent access to the database. Several database packages let the DBA specify parameters for determining the desired level of concurrency. Concurrency is also affected by the types of locks used by the DBMS and requested by the applications. Because concurrency is important to the efficient operation of the system, the DBA must be familiar with the factors that influence concurrency. (See Chapter 10, Transaction Management and Con- currency Control, for more information.)
During DBMS performance tuning, the DBA must also consider available storage resources in terms of both primary and secondary memory. The allocation of storage resources is determined when the DBMS is configured. Storage configuration parame- ters can be used to determine:
• The number of databases that may be opened concurrently
• The number of application programs or users supported concurrently
• The amount of primary memory (buffer pool size) assigned to each database and each database process
• The size and location of the log file (remember that these files are used to recover the database; the log files can be located in a separate volume to reduce the disk’s head movement and to increase performance)
Performance-monitoring issues are DBMS-specific. Therefore, the DBA must become familiar with the DBMS manuals to learn the technical details involved in performance monitoring (see Chapter 11).
Because data loss could be devastating to the organization, backup and recovery activ- ities are of primary concern during the DBMS operation. The DBA must establish a
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schedule for backing up database and log files at appropriate intervals. Backup frequency is dependent on the application type and on the relative importance of the data. All crit- ical system components—the database, the database applications, and the transaction logs—must be backed up periodically.
Most DBMS packages include utilities that schedule automated database backups, either full or incremental. Although incremental backups are faster than full backups, an incremental backup requires the existence of a periodic full backup to be useful for recovery purposes.
Database recovery after a media or systems failure requires application of the transac- tion log to the correct database copy. The DBA must plan, implement, test, and enforce a “bulletproof ” backup and recovery procedure.
Security auditing and monitoring assumes the appropriate assignment of access rights and the proper use of access privileges by programmers and end users. The technical aspects of security auditing and monitoring involve creating users, assigning access rights, and using SQL commands to grant and revoke access rights to users and database objects. The DBA also must periodically generate an audit trail report to find actual or attempted security violations. If any are found, the DBA must ascertain where the viola- tions occurred, and if possible, who committed them. For a comprehensive discussion of database security, see Section 16-6.
TrainingandSupportingUsers TrainingpeopletousetheDBMSanditstoolsispartof the DBA’s technical activities. In addition, the DBA provides or secures technical train- ing for applications programmers in the use of the DBMS and its utilities. Applications programmer training covers the use of the DBMS tools as well as the procedures and standards required for database programming.
Unscheduled, on-demand technical support for end users and programmers is also part of the DBA’s activities. A technical troubleshooting procedure can be developed to facilitate such support. The procedure might include the development of a technical database to find solutions to common technical problems.
Part of the support is provided by interaction with DBMS vendors. Establishing good relationships with software suppliers is one way to ensure that the company has a good external support source. Vendors are the source for up-to-date information con- cerning new products and personnel retraining. Good vendor-company relations also are likely to give organizations an edge in determining the future direction of database development.
Maintaining the DBMS, Utilities, and Applications The maintenance activities of the DBA are an extension of the operational activities. Maintenance activities are dedicated to the preservation of the DBMS environment.
Periodic DBMS maintenance includes management of the physical or secondary stor- age devices. One of the most common maintenance activities is reorganizing the physical location of data in the database. (This is usually done as part of the DBMS fine-tuning activities.) The reorganization of a database might be designed to allocate contiguous disk-page locations to the DBMS to increase performance. The reorganization process also might free the space allocated to deleted data, thus providing more disk space for new data.
Maintenance activities also include upgrading the DBMS and utility software. The upgrade might require installing a new version of the DBMS software or an Internet front-end tool. Or, it might create an additional DBMS gateway to allow access to a host DBMS running on a different host computer. DBMS gateway services are very common in distributed DBMS applications running in a client/server environment. Also, new-generation databases include features such as spatial data support, data
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warehousing and star query support, and support for Java programming interfaces for Internet access (see Chapter 15, Database Connectivity and Web Technologies).
Quite often companies are faced with the need to exchange data in dissimilar formats or between databases. The maintenance efforts of the DBA include migration and con- version services for data in incompatible formats or for different DBMS software. Such conditions are common when the system is upgraded from one version to another or when the existing DBMS is replaced by an entirely new DBMS. Database conversion ser- vices also include downloading data from the host DBMS (mainframe-based) to an end user’s personal computer to allow the user to perform a variety of activities—spreadsheet analysis, charting, statistical modeling, and so on. Migration and conversion services can be done at the logical level (DBMS-specific or software-specific) or at the physical level (storage media or operating system-specific). Current-generation DBMSs support XML as a standard format for data exchange among database systems and applications (see Chapter 15).
16-6 Security
Information system security refers to activities and measures that ensure the confidenti- ality, integrity, and availability of an information system and its main asset, data.4 Secur- ing data requires a comprehensive, company-wide approach. That is, you cannot secure data if you do not secure all the processes and systems around it, including hardware systems, software applications, the network and its devices, internal and external users, procedures, and the data itself. To understand the scope of data security, consider each of the three security goals in more detail:
• Confidentiality deals with ensuring that data is protected against unauthorized access, and if the data is accessed by an authorized user, that it is used only for an authorized purpose. In other words, confidentiality entails safeguarding data against disclosure of any information that would violate the privacy rights of a person or organization. Data must be evaluated and classified according to the level of confi- dentiality: highly restricted (very few people have access), confidential (only certain groups have access), and unrestricted (can be accessed by all users). The data security officer spends a great amount of time ensuring that the organization is in compliance with desired levels of confidentiality. Compliance refers to activities that meet data privacy and security reporting guidelines. These guidelines are either part of internal procedures or are imposed by external regulatory agencies such as the federal govern- ment. Examples of U.S. legislation enacted to ensure data privacy and confidentiality include the Health Insurance Portability and Accountability Act (HIPAA), Gramm- Leach-Bliley Act (GLBA), and Sarbanes-Oxley Act (SOX).
• Integrity, within the data security framework, is concerned with keeping data con- sistent and free of errors or anomalies. (See Chapter 1 to review the concepts of data inconsistencies and data anomalies.) The DBMS plays a pivotal role in ensuring the integrity of the data in the database. However, from the security point of view, the organizational processes, users, and usage patterns also must maintain integrity. For example, a work-at-home employee using the Internet to access product costing could be considered an acceptable use; however, security standards might require the employee to use a secure connection and follow strict procedures to manage the data at home, such as shredding printed reports and using encryption to copy data to the local hard drive. Maintaining data integrity is a process that starts with data collection
4 Krause, M. and Tipton, H., Handbook of Information Security Management, CRC Press LLC, 1999.
confidentiality
In the context of data security, ensuring that data is protected against unauthorized access, and if the data is accessed by an authorized user, that the data is used only for an authorized purpose.
compliance
Activities that meet data privacy and security reporting guidelines or requirements.
integrity
In a data security framework, refers to keeping data consistent and free of errors or anomalies. See also data integrity.
Chapter 16 Database Administration and Security 745
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and continues with data storage, processing, usage, and archiving (see Chapter 13, Business Intelligence and Data Warehouses). The rationale behind integrity is to treat data as the most-valuable asset in the organization and to ensure that rigorous data validation is carried out at all levels within the organization.
• Availability refers to the accessibility of data whenever required by authorized users and for authorized purposes. To ensure data availability, the entire system must be protected from service degradation or interruption caused by any internal or external source. Service interruptions could be very costly for companies and users alike. Sys- tem availability is an important goal of security.
16-6a Security Policies
Normally, the tasks of securing the system and its main asset, the data, are performed by the database security officer and the database administrator(s), who work together to establish a cohesive data security strategy. Such a strategy begins with defining a sound and comprehensive security policy. A security policy is a collection of standards, pol- icies, and procedures created to guarantee the security of a system and ensure auditing and compliance. The security audit process starts by identifying security vulnerabilities in the organization’s information system infrastructure and identifying measures to pro- tect the system and data against those vulnerabilities.
16-6b SecurityVulnerabilities
A security vulnerability is a weakness in a system component that could be exploited to allow unauthorized access or cause service disruptions. Such vulnerabilities could fall under one of the following categories:
• Technical. An example would be a flaw in the operating system or web browser.
• Managerial. For example, an organization might not educate users about critical secu-
rity issues.
• Cultural. Users might hide passwords under their keyboards or forget to shred confi- dential reports.
• Procedural. Company procedures might not require complex passwords or the check- ing of user IDs.
When a security vulnerability is left unchecked, it could become a security threat. A security threat is an imminent security violation.
A security breach occurs when a security threat is exploited to endanger the integrity, confidentiality, or availability of the system. Security breaches can lead to a database whose integrity is either preserved or corrupted:
• Preserved. In these cases, action is required to avoid the recurrence of similar security problems, but data recovery may not be necessary. As a matter of fact, most security violations are produced by unauthorized and unnoticed access for information pur- poses, but such snooping does not disrupt the database.
• Corrupted. Action is required to avoid the recurrence of similar security problems, and the database must be recovered to a consistent state. Corrupting security breaches include database access by computer viruses and by hackers who intend to destroy or alter data.
Table 16.4 illustrates some security vulnerabilities of system components and typical protective measures against them.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
availability
In the context of data security, it refers to the accessibility of data whenever required by authorized users and for authorized purposes.
security policy
A collection of standards, policies, and procedures created to guarantee
the security of a system and ensure auditing and compliance.
security vulnerability
A weakness in a system component that
could be exploited to allow unauthorized access or cause service disruptions.
security threat
An imminent security violation that could occur due to unchecked security vulnerabilities.
security breach
An event in which
a security threat
is exploited to
endanger the integrity, confidentiality, or availability of the system.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 16 Database Administration and Security 747
TABLE 16.4
SAMPLE SECURITY VULNERABILITIES AND RELATED PROTECTIVE MEASURES
SYSTEM COMPONENT
SECURITY VULNERABILITY
SECURITY MEASURES
People
• The user sets a blank password.
• The password is short or includes a
birth date.
• The user leaves the office door open all
the time.
• The user leaves payroll information on
the screen for long periods of time.
• Enforce complex password policies.
• Use multilevel authentication.
• Use security screens and screen savers.
• Educate users about sensitive data.
• Install security cameras.
• Use automatic door locks.
Workstation and servers
• The user copies data to a flash drive.
• The workstation is used by multiple
users.
• A power failure crashes the computer.
• Unauthorized personnel can use the
computer.
• Sensitive data is stored on a laptop
computer.
• Data is lost due to a stolen hard disk or
laptop.
• A natural disaster occurs.
• Use group policies to restrict the use of flash drives.
• Assign user access rights to workstations.
• Install uninterrupted power supplies (UPSs).
• Add security locks to computers.
• Implement a kill switch for stolen laptops.
• Create and test data backup and recovery
plans.
• Protect the system against natural
disasters—use co-location strategies.
Operating system
• Buffer overflow attacks
• Virus attacks
• Root kits and worm attacks
• Denial-of-service attacks
• Trojan horses
• Spyware applications
• Password crackers
• Apply OS security patches and updates.
• Apply application server patches.
• Install antivirus and antispyware software.
• Enforce audit trails on the computers.
• Perform periodic system backups.
• Install only authorized applications.
• Use group policies to prevent unauthorized
installations.
Applications
• Application bugs—buffer overflow
• SQL injection, session hijacking, etc.
• Application vulnerabilities—cross-site
scripting, nonvalidated inputs
• Email attacks—spamming, phishing,
etc.
• Social engineering emails
• Test application programs extensively.
• Build safeguards into code.
• Do extensive vulnerability testing in
applications.
• Install spam filters and antivirus software for
email systems.
• Use secure coding techniques (see www.
owasp.org).
• Educate users about social engineering
attacks.
Network
• IP spoofing
• Packet sniffers
• Hacker attacks
• Clear passwords on network
• Install firewalls.
• Use virtual private networks (VPNs).
• Use intrusion detection systems (IDSs).
• Use network access control (NAC).
• Use network activity monitoring.
Data
• Data shares are open to all users.
• Data can be accessed remotely.
• Data can be deleted from a shared
resource.
• Implement file system security.
• Implement share access security.
• Use access permission.
• Encrypt data at the file system or database
level.
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16-6c Database Security
Note
Database security refers to DBMS features and other related measures that comply with the organization’s security requirements. From the DBA’s point of view, security measures should be implemented to protect the DBMS against service degradation and to protect the database against loss, corruption, or mishandling. In short, the DBA should secure the DBMS from the point of installation through operation and maintenance.
James Martin’s excellent description of the desirable attributes of a database security strat- egy remains relevant today (Managing the Database Environment, Prentice-Hall, 1977). Martin’s security strategy is based on the seven essentials of database security, and may be summarized as one in which data is protected, reconstructable, auditable, and tamper- proof, and users are identifiable, authorized, and monitored.
To protect the DBMS against service degradation, some security safeguards are rec- ommended. For example:
• Change default system passwords.
• Change default installation paths.
• Apply the latest patches.
• Secure installation folders with proper access rights.
• Make sure that only required services are running.
• Set up auditing logs.
• Set up session logging.
• Require session encryption.
Furthermore, the DBA should work closely with the network administrator to imple- ment network security that protects the DBMS and all services running on the network. In modern organizations, one of the most critical components in the information archi- tecture is the network.
Protecting the data in the database is a function of authorization management. Authorization management defines procedures to protect and guarantee database security and integrity. Those procedures include the following:
• User access management. This function is designed to limit access to the database; it likely includes at least the following procedures:
– Define each user to the database. The DBA performs this function at the operat- ing system level and the DBMS level. At the operating system level, the DBA can request the creation of a unique user ID for each end user who logs on to the com- puter system. At the DBMS level, the DBA can either create a different user ID or employ the same one to authorize the end user to access the DBMS.
– Assign passwords to each user. The DBA also performs this function at both the operating system and DBMS levels. The database passwords can be assigned with predetermined expiration dates, which enable the DBA to screen end users peri- odically and remind them to change their passwords, thus making unauthorized access less likely.
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any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
database security
The use of DBMS features and other related measures to comply with the security requirements of an organization.
authorization management Procedures that protect and guarantee database security and integrity. Such procedures
include user access management, view definition, DBMS access control, and DBMS usage monitoring.
Copyright 2017 Cengage Lea Editorial review has deemed that

– Define user groups. Classifying users into groups according to common access needs can help the DBA control and manage the access privileges of individual users. Also, the DBA can use database roles and resource limits to minimize the impact of rogue users in the system. (See Section 16-10d for more information about these topics.)
– Assign access privileges. The DBA assigns access privileges to specific users to access certain databases. Access rights may be limited to read-only, or the authorized access might include read, write, and delete privileges. Access privileges in rela- tional databases are assigned through SQL GRANT and REVOKE commands.
– Control physical access. Physical security can prevent unauthorized users from directly accessing the DBMS installation and facilities. Common physical security for large database installations includes secured entrances, password-protected workstations, electronic personnel badges, closed-circuit video, voice recognition, and biometric technology.
• View definition. The DBA must define data views to protect and control the scope of the data that are accessible to an authorized user. The DBMS must provide tools that allow the definition of views composed of one or more tables, and must assign access rights to users. The SQL CREATE VIEW command is used in relational databases to define views. Oracle DBMS offers Virtual Private Database (VPD), which allows the DBA to create customized views of the data for different users. With this feature, the DBA could restrict regular users who query a payroll database to see only the nec- essary rows and columns, while department managers would see only the rows and columns pertinent to their departments.
• DBMS access control. Database access can be controlled by placing limits on the use of DBMS query and reporting tools. The DBA must make sure the tools are used prop- erly and only by authorized personnel.
• DBMS usage monitoring. The DBA must also audit the use of data in the data- base. Several DBMS packages contain features that allow the creation of an audit log, which automatically records a brief description of database opera- tions performed by all users. Such audit trails enable the DBA to pinpoint access violations. The audit trails can be tailored to record all database accesses or just failed ones.
The integrity of a database could be lost because of external factors beyond the DBA’s control. For example, the database might be damaged or destroyed by an explosion, a fire, or an earthquake. Whatever the reason, the specter of database corruption or destruction makes backup and recovery procedures crucial to any DBA.
16-7 Database Administration Tools
The extraordinary growth of data management activities within organizations created the need for better management standards, processes, and tools. Over the years, a new industry arose dedicated exclusively to data administration tools. These tools cover the entire spectrum of data administration tasks, from selection to inception, deployment, migration, and day-to-day operations. For example, you can find sophisticated data administration tools for:
• Database monitoring
• Database load testing
• Database performance tuning
audit log
A security feature of a database management system that automatically records a brief description of the database operations performed by all users.
Chapter 16 Database Administration and Security 749
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• SQL code optimization
• Database bottleneck identification and remediation
• Database modeling and design
• Database data extraction, transformation, and loading
All the above-mentioned tools have something in common. They all expand the data- base’s metadata or data dictionary. The importance of the data dictionary as a DBA tool cannot be overstated. This section examines the data dictionary as a data administration tool, as well as the DBA’s use of computer-aided systems engineering (CASE) tools to support database analysis and design.
16-7a The Data Dictionary
In Chapter 1, a data dictionary was defined as “a DBMS component that stores the definition of data characteristics and relationships.” You may recall that such “data about data” are called metadata. The DBMS data dictionary provides the DBMS with its self-describing characteristic. In effect, the data dictionary resembles an x-ray of the company’s entire data set, and it is a crucial element in data administration.
Two main types of data dictionaries exist: integrated and standalone. An integrated data dictionary is included with the DBMS. For example, all relational DBMSs include a built-in data dictionary or system catalog that is frequently accessed and updated by the RDBMS. Other DBMSs, especially older types, do not have a built-in data dictionary; instead, the DBA may use third-party standalone systems.
Data dictionaries can also be classified as active or passive. An active data dictionary is automatically updated by the DBMS with every database access to keep its access infor- mation up to date. A passive data dictionary is not updated automatically and usually requires running a batch process. Data dictionary access information is normally used by the DBMS for query optimization.
The data dictionary’s main function is to store the description of all objects that inter- act with the database. Integrated data dictionaries tend to limit their metadata to the data managed by the DBMS. Standalone data dictionary systems are usually more flexible and allow the DBA to describe and manage all of the organization’s data, whether they are computerized or not. Whatever the data dictionary’s format, it provides database design- ers and end users with a much-improved ability to communicate. In addition, the data dictionary is the tool that helps the DBA resolve data conflicts.
Although there is no standard format for the information stored in the data dictio- nary, several features are common. For example, the data dictionary typically stores descriptions of the following:
• Data elements that are defined in all tables of all databases. Specifically, the data dic- tionary stores element names, data types, display format, internal storage format, and validation rules. The data dictionary explains where an element is used, who used it, and so on.
• Tables defined in all databases. For example, the data dictionary is likely to store the name of the table creator, the date of creation, access authorizations, and the number of columns.
• Indexes defined for each database table. For each index, the DBMS stores at least the index name, the attributes used, the location, specific index characteristics, and the creation date.
• Defined databases. This information includes who created each database, when the database was created, where the database is located, the DBA’s name, and so on.
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active data dictionary
A data dictionary
that is automatically updated by the database management system every time the database is accessed, thereby keeping its information current.
passive data dictionary
A DBMS data dictionary that requires a command initiated by an end user to update its data access statistics.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 16 Database Administration and Security 751 • End users and administrators of the database. This information defines the users of the
database.
• Programs that access the database. This information includes screen formats, report formats, application programs, and SQL queries.
• Access authorizations for all users of all databases. This information defines who can manipulate which objects and what types of operations can be performed.
• Relationships among data elements. This information includes which elements are involved, whether the relationships are mandatory or optional, and connectivity and cardinality requirements.
If the data dictionary can be organized to include data external to the DBMS itself, it becomes an especially flexible tool for more general corporate resource management. Such an extensive data dictionary thus makes it possible to manage the use and alloca- tion of all of the organization’s information, regardless of whether it has its roots in the database data. For this reason, some managers consider the data dictionary to be a key element of information resource management, which is why the data dictionary can be described as the information resource dictionary.
The metadata stored in the data dictionary is often the basis for monitoring data- base use and for assigning access rights to database users. The information stored in the data dictionary is usually based on a relational table format, thus enabling the DBA to query the database with SQL commands. For example, SQL commands can be used to extract information about the users of a specific table or the access rights of a particular user. In the following section, the IBM DB2 system catalog tables are the basis for several examples of how a data dictionary is used to derive information:
• SYSTABLES stores one row for each table or view.
• SYSCOLUMNS stores one row for each column of each table or view.
• SYSTABAUTH stores one row for each authorization given to a user for a table or view in a database.
Examples of Data Dictionary Usage
Example 1
List the names and creation dates of all tables created by the user JONESVI in the current database.
SELECT NAME, CTIME
FROM SYSTABLES
WHERE CREATOR = ‘JONESVI’;
Example 2
List the names of the columns for all tables created by JONESVI in the current database.
SELECT NAME
FROM SYSCOLUMNS
WHERE TBCREATOR = ‘JONESVI’;
Example 3
List the names of all tables for which the user JONESVI has DELETE authorization.
information resource dictionary
Another name for data dictionary.
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SELECT TTNAME
FROM SYSTABAUTH
WHERE GRANTEE = ‘JONESVI’ AND DELETEAUTH = ‘Y’;
Example 4
List the names of all users who have some type of authority over the INVENTORY table.
SELECT DISTINCT GRANTEE FROM SYSTABAUTH
WHERE TTNAME = ‘INVENTORY’;
Example 5
List the user and table names for all users who can alter the database structure for any table in the database.
SELECT GRANTEE, TTNAME FROM SYSTABAUTH
WHERE ALTERAUTH = ‘Y’ ORDER BY GRANTEE, TTNAME;
As you can see in the preceding examples, the data dictionary can be a tool for monitoring database security by checking the assignment of data access privileges. Although the preceding examples targeted database tables and users, information about the application programs that access the database can also be drawn from the data dictionary.
The DBA can use the data dictionary to support data analysis and design. For exam- ple, the DBA can create a report that lists all data elements to be used in a particular application; a list of all users who access a particular program; a report that checks for data redundancies, duplications, and the use of homonyms and synonyms; and a number of other reports that describe data users, data access, and data structure. The data dictionary can also be used to ensure that application programmers have met the naming standards for data elements in the database, and that the data validation rules are correct. Thus, the data dictionary can be used to support a wide range of data administration activities and facilitate the design and implementation of information systems. Integrated data dictionaries are also essential to the use of computer-aided systems engineering tools.
16-7b CaseTools
CASE is the acronym for computer-aided systems engineering. A CASE tool provides an automated framework for the Systems Development Life Cycle (SDLC). CASE uses structured methodologies and powerful graphical interfaces. Because they automate many tedious system design and implementation activities, CASE tools play an increas- ingly important role in information systems development.
CASE tools are usually classified according to the extent of support they pro- vide for the SDLC. For example, front-end CASE tools provide support for the planning, analysis, and design phases; back-end CASE tools provide support for the coding and implementation phases. The benefits associated with CASE tools include:
• A reduction in development time and costs
• Automation of the SDLC
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computer-aided systems engineering (CASE)
Tools used to automate part or all of the Systems Development Life Cycle.
front-end CASE tool
A computer-aided software tool that provides support for the planning, analysis, and design phases of the SDLC.
back-end CASE tool
A computer-aided software tool that provides support
for the coding and implementation phases of the SDLC.
Copyright 2017 Cengage Lea Editorial review has deemed that

Chapter 16 Database Administration and Security 753 • Easier maintenance of application systems developed with CASE tools
One of the CASE tools’ most important components is an extensive data dictio- nary, which keeps track of all objects created by the systems designer. For example, the CASE data dictionary stores data flow diagrams, structure charts, descriptions of all external and internal entities, data stores, data items, report formats, and screen formats. A CASE data dictionary also describes the relationships among system components.
Several CASE tools provide interfaces that work with the DBMS and allow the CASE tool to store its data dictionary information using the DBMS. Such inter- action demonstrates the interdependence that exists between systems develop- ment and database development, and it helps create a fully integrated development environment.
In a CASE development environment, database and application designers use the CASE tool to store the description of the database schema, data elements, application processes, screens, reports, and other data relevant to development. The CASE tool inte- grates all systems development information in a common repository, which the DBA can check for consistency and accuracy.
As an additional benefit, a CASE environment tends to improve the extent and quality of communication among the DBA, application designers, and end users. The DBA can use the CASE tool to check the definition of the application’s data schema, the obser- vance of naming conventions, the duplication of data elements, validation rules for the data elements, and a host of other developmental and managerial variables. When the CASE tool finds conflicts, rules violations, and inconsistencies, it facilitates making cor- rections. Better yet, the CASE tool can make a correction and then cascade its effects throughout the applications environment, which greatly simplifies the job of the DBA and the application designer.
A typical CASE tool provides five components:
• Graphics designed to produce structured diagrams such as data flow diagrams, ER diagrams, class diagrams, and object diagrams
• Screen painters and report generators to produce the information system’s input and output formats (for example, the end-user interface)
• An integrated repository for storing and cross-referencing the system design data; this repository includes a comprehensive data dictionary
• An analysis segment to provide a fully automated check on system consistency, syn- tax, and completeness
• A program documentation generator
Figure 16.7 illustrates how Microsoft Visio Professional can be used to produce an ER diagram.
Most CASE tools, produce fully documented ER diagrams that can be displayed at different abstraction levels. For example, ERwin Data Modeler by Computer Asso- ciates can produce detailed relational designs. The user specifies the attributes and primary keys for each entity and describes the relations. Current generation data modeling tools assign foreign keys based on the specified relationships among the entities. Changes in primary keys are always updated automatically throughout the system. Table 16.5 shows a short list of the many available CASE Data Modeling tool vendors.
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• Standardization of systems development methodologies

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FIGURE16.7 ANEXAMPLEOFACASETOOL:MICROSOFTVISIOPROFESSIONAL
Main menu
Modeling options
Completed ERD
TABLE 16.5
CASE DATA MODELING TOOLS
COMPANY
PRODUCT
WEBSITE
Casewise
Corporate Modeler Suite
www.casewise.com
Computer Associates
ERwin
www.erwin.com
Embarcadero Technologies
ER/Studio
www.embarcadero.com/products/er-studio-data-architect
Microsoft
Visio
office.microsoft.com/en-us/visio
Oracle
SQL Developer Data Modeler
www.oracle.com/technetwork/developer-tools/datamodeler/ overview/index.html
IBM
Rational Software Architect
www-01.ibm.com/software/rational/products/swarchitect/
SAP
Power Designer
http://www.sap.com/pc/tech/database/software/model- driven-architecture/index.html
Visible
Visible Analyst
www.visible.com/Products/Analyst
Major relational DBMS vendors, such as Oracle, now provide fully integrated CASE tools for their own DBMS software as well as for RDBMSs supplied by other vendors. For example, Oracle’s CASE tools can be used with IBM’s DB2, and Microsoft’s SQL Server to produce fully documented database designs. Some vendors even take nonrelational DBMSs, develop their schemas, and produce the equivalent relational designs automatically.
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There is no doubt that CASE tools have enhanced the efficiency of database designers and application programmers. However, no matter how sophisticated the CASE tool, its users must be well versed in conceptual design. In the hands of database novices, CASE tools produce impressive-looking but bad designs.
16-8 Developing a Data Administration Strategy
For a company to succeed, its activities must be committed to its main objectives or mis- sion. Therefore, regardless of its size, a critical step for any organization is to ensure that its information system supports its strategic plans for each business area.
The database administration strategy must not conflict with the information systems plans. After all, these plans are derived from a detailed analysis of the company’s goals, its condition or situation, and its business needs. Several methodologies are available to ensure the compatibility of data administration and information systems plans and to guide strategic plan development. The most commonly used methodology is known as information engineering.
Information engineering (IE) allows for translation of the company’s strategic goals into the data and applications that will help the company achieve those goals. IE focuses on the description of corporate data instead of the processes. The IE rationale is simple: business data types tend to remain fairly stable, but processes change often and thus require frequent modification of existing systems. By placing the emphasis on data, IE helps decrease the impact on systems when processes change.
The output of the IE process is an information systems architecture (ISA) that serves as the basis for planning, development, and control of future information systems. Figure 16.8 shows the forces that affect ISA development.
Implementing IE in an organization is a costly process that involves planning, a com- mitment of resources, management liability, well-defined objectives, identification of critical factors, and control. An ISA provides a framework that includes computerized, automated, and integrated tools such as a DBMS and CASE tools.
The success of the overall information systems strategy and data administration strategy depends on several critical success factors that the DBA needs to understand. Critical suc- cess factors include the following managerial, technological, and corporate culture issues:
• Management commitment. The commitment of top-level management is necessary to enforce the use of standards, procedures, planning, and controls. The example must be set at the top.
• Thorough analysis of the company situation. The current state of the corporate data administration must be analyzed to understand the company’s position and to have
information engineering (IE)
A methodology that translates a company’s strategic goals into helpful data and applications. IE focuses on the description of corporate data instead of the processes.
information systems architecture (ISA) The output of the information engineering (IE) process that
serves as the basis for planning, developing, and controlling future information systems.
Chapter 16 Database Administration and Security 755
FIGURE16.8 FORCESAFFECTINGTHEDEVELOPMENTOFTHEISA
Company managers (provide Goals and Critical Success Factors)
Company mission
Information engineering
Information systems architecture
Strategic plan
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a clear vision of what must be done. For example, how are database analysis, design, documentation, implementation, standards, codification, and other issues handled? Needs and problems should be identified first and then prioritized.
• End-user involvement. What degree of organizational change is involved? Successful change requires that people be able to adapt to it. Users should have an open com- munication channel to upper management to ensure success of the implementation. Good communication is key to the overall process.
• Defined standards. Analysts and programmers must be familiar with appropriate methodologies, procedures, and standards. If not, they might need training.
• Training. The vendor must train DBA personnel in the use of the DBMS and other tools. End users must be trained to use the tools, standards, and procedures. Key per- sonnel should be trained first so they can train others.
• A small pilot project. A small project is recommended to ensure that the DBMS will work in the company, that it produces expected output, and that the personnel have been trained properly.
This list of factors is not comprehensive, but it does provide the framework for devel- oping a successful strategy. Remember that no matter how comprehensive you make the list, it must be based on developing and implementing a data administration strategy that is tightly integrated with the organization’s overall information systems planning.
Developing a comprehensive data administration strategy within an organization is a large undertaking encompassing technical, operational, and managerial roles. Enterprises today also have the option of moving entire computing functions (such as servers, storage, backup, and even the database) outside the walls of the enterprise and into the cloud.
16-9 The DBA’s Role in the Cloud
The use of cloud-based data services does not signal the end of DBAs, but it does have a significant impact on their role. As discussed in previous chapters, services such as Microsoft Azure and Amazon Web Services (AWS) allow outsourcing database tech- nology as a highly scalable, capability-on-demand service. In this new world, some of the tasks that once resided in a single “in-house” DBA function are now split between the internal DBA and the cloud service provider. As a result, the use of cloud-based data services alters and expands the typical DBA’s role in both technical and manage- rial dimensions. In general, the cloud services partner company provides:
• DBMS installation and updates. The DBMS is installed on a virtual server by the ser- vice provider. As the DBMS vendor releases required updates and security fixes to the DBMS software, the service provider manages the application of the updates within a specified maintenance window. The DBA’s role now has to carefully coordinate such updates with the external cloud-based data service provider.
• Server/network management. The service provider configures and manages the server where the DBMS resides, including scaling the database across multiple servers as needed. If the database is distributed across multiple servers, the service provider can supply load balancing to ensure a high level of performance. However, the DBA must work with his or her /her company’s network department to ensure that the network is properly configured for security, performance, availability and management.
• Backup and recovery operations. The service provider performs regular backups and stores backups in secure facilities. The DBA must ensure that internal data privacy and retention policies are enforced and maintained.
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Although these services are valuable and free the DBA from these tasks, the primary benefit of cloud-based data services is their ability to provide and manage computing hardware and software configuration at a low cost. The preceding tasks are only a small part of the DBA’s responsibilities; the DBA’s managerial role is largely unchanged and sometimes is even augmented with the new cloud data services dimension. User require- ments must still be gathered; data solutions must still be designed; end users need train- ing; and policies, standards, and procedures must be developed and enforced.
Even the technical role of the DBA still exists with the use of cloud data services. There are many cloud data service providers, and some offer a variety of DBMS products, including proprietary systems. Only some versions of these DBMSs are available, includ- ing multiple versions of the same DBMS. For example, a given service provider may sup- port both MySQL 5.1 and MySQL 5.5. In this environment, the DBA evaluates different DBMSs to determine which software product to use, and evaluates from which provider to purchase the DBMS. In addition, the DBA must work with the cloud data services provider to reconcile the required database technical features with the ones supported by the cloud data service provider and ensure data availability, security, and integrity within the expanded boundaries of the company network.
A variety of pricing schemes are offered by cloud data service providers. Pricing is typically based on factors such as storage space, computing resources (CPU cycles and memory), and data transfer sizes. Service users are billed monthly for the amount of resources used. Service providers have a vested interest in their clients’ databases being as large as possible; it is also in their interest for database designs to be inefficient in processing queries because clients will have to buy more memory and CPU capacity. Service providers benefit if your database is filled with poorly designed tables that con- tain lots of unnecessarily redundant data, with every attribute in every table indexed, and queries that take a long time to run or return thousands of rows of data that must be transferred to a front-end application for additional processing. Therefore, the DBA can save the organization significant time and money by ensuring that databases are properly designed with minimal redundancy and that database coding is efficient. Clearly, the DBA’s technical role is still critical to organizations that use cloud-based data services. The DBA’s efforts in efficient and effective database design, coding, monitoring database performance, and database tuning still affect the organization’s ability to use data and information as a resource, and they have an immediate visible impact on the monthly data service bill.
Regardless of whether the database is stored in the enterprise’s server or in the cloud, the DBA must ensure the data’s availability, security, and integrity.
16-10 The DBA at Work: Using Oracle for Database Administration
Thus far, you have learned about the DBA’s work environment and responsibilities in general terms. This section provides a more detailed look at how a DBA might handle the following technical tasks in a specific DBMS:
• Creating and expanding database storage structures
• Managing the end-user database environment, including the type and extent of data-
base access
• Customizing database initialization parameters
Many of these tasks require the DBA to use software tools and utilities that are commonly provided by the database vendor. In fact, all DBMS vendors provide a set
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Note
of programs to interface with the database and to perform a wide range of database administrative tasks.
Oracle 12c for Windows is used to illustrate selected DBA tasks in this section because Oracle is typically used in organizations that are large and complex enough to employ a DBA. Also, Oracle has good market presence and is often used in small colleges and universities.
Although Microsoft Access is a superb DBMS, it is typically used in smaller organizations or in organizations and departments with relatively simple data environments. Access has a superior database prototyping environment, and its easy-to-use GUI tools enable rapid front-end application development. Also, Access is a component in the MS Office suite, which makes applications integration relatively simple and seamless for end users. Finally, while Access does provide some important database administration tools, an Access- based database environment does not typically require a DBA, so MS Access is not a good fit for this section.
Note
Most of the tasks described in this section are not particular to any DBMS or operat- ing system. However, the execution of those tasks tends to be specific to the DBMS and operating system. Therefore, if you use IBM DB2 Universal Database or Microsoft SQL Server, you must adapt the procedures shown here to your DBMS. Also, these examples run under the Windows operating system, so you must adapt the procedures shown in this section if you use a different OS.
This section is not a database administration manual; it offers a brief introduction to performing typical DBA tasks in Oracle. Before learning these tasks, you should become familiar with Oracle’s database administration tools and its procedures for logging on. These tools and procedures are discussed in the next two sections.
16-10a Oracle Database Administration Tools
All database vendors supply a set of database administration tools. In Oracle, you per- form most DBA tasks via the Oracle Enterprise Manager interface. (See Figure 16.9.)
Note that the interface shows the status of the current database. (This section uses the BASEORA database.) In the following sections, you examine the tasks most commonly encountered by a DBA.
16-10b Ensuring that the RDBMS Starts Automatically
One of a DBA’s basic tasks is to ensure that database access starts automatically when you turn on the computer. Startup procedures are different for each operating system. Oracle is used for this section’s examples; if you use a different system, you need to identify the required services to ensure automatic database startup. A service is the Windows name
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Although the format of creating a database tends to be generic, its execution tends to be DBMS-specific. For a step-by-step procedure of creating a database using the Oracle Data- base Configuration Assistant, see Appendix N, Creating a New Database Using Oracle 12c.

for a special program that runs automatically as part of the operating system. This pro- gram ensures the availability of required services to the system and to end users on the local computer or the network. Figure 16.10 shows the required Oracle services that are started automatically when Windows starts.
As you examine Figure 16.10, note the following Oracle services:
• OracleOraDB12Home1TNSListener is the process that “listens to” and processes end- user connection requests over the network. For example, when a SQL connection request such as “connect userid/password@BASEORA” is sent over the network, the listener service will validate the request and establish the connection.
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FIGURE 16.9 THE ORACLE ENTERPRISE MANAGER EXPRESS INTERFACE
FIGURE16.10 ORACLERDBMSSERVICES

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• OracleServiceBASEORA refers to the Oracle processes running in memory that are associated with the BASEORA database instance. You can think of a database instance as a separate location in memory that is reserved to run your database. Because you can have several databases (and therefore several instances) running in memory at the same time, you need to identify each database instance uniquely, using a different suffix for each one.
16-10c Creating Tablespaces and Datafiles
Each DBMS manages data storage differently. In this example, the Oracle RDBMS is used to illustrate how the database manages data storage at the logical and physical levels. In Oracle,
• A database is logically composed of one or more tablespaces. A tablespace is a logical storage space. Tablespaces are used primarily to group related data logically.
• The tablespace data is physically stored in one or more datafiles. A datafile physically stores the database’s data. Each datafile is associated with only one tablespace, but each datafile can reside in a different directory on the physical storage devices. For example, in Figure 16.11, the USERS tablespace data is physically stored in the datafile users01.dbf.
FIGURE16.11 ORACLESTORAGEMANAGEMENT
Given the preceding descriptions, you can conclude that a database has a one-to- many relationship with tablespaces and that a tablespace has a one-to-many relationship with datafiles. This set of 1:M hierarchical relationships isolates the end user from any physical details of data storage. However, the DBA must be aware of these details to prop- erly manage the database.
To manage database storage, such as creating and managing tablespaces and datafiles, the DBA uses the Enterprise Manager → Server → Tablespaces option.
When the DBA creates a database, Oracle automatically creates the tablespaces and datafiles shown in Figure 16.11. A few of them are described as follows:
• The SYSTEM tablespace is used to store the data dictionary data.
• The USERS tablespace stores the table data created by the end users.
• The TEMP tablespace stores the temporary tables and indexes created during the execution of SQL statements. For example, temporary tables are created when your SQL statement contains an ORDER BY, GROUP BY, or HAVING clause.
rning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
database instance
In an Oracle DBMS, the collection of processes and data structures used to manage a specific database.
tablespace
In a DBMS, a logical storage space used to group related data. Also known as a file group.
datafile
A file on the hard drive or storage system where the data in a tablespace is physically stored.
Copyright 2017 Cengage Lea Editorial review has deemed that

• The UNDOTBS1 tablespace stores database transaction recovery information. If a transaction must be rolled back (usually to preserve database integrity), the UNDOTBS1 tablespace stores the undo information.
Using the Enterprise Manager, the DBA can:
• Create additional tablespaces to organize the data in the database. Therefore, if you have a database with several hundred users, you can create several user tablespaces to segment data storage for different types of users. For example, you might create a teacher tablespace and a student tablespace.
• Create additional tablespaces to organize the various subsystems within the database. For example, you might create different tablespaces for human resources data, payroll data, accounting data, and manufacturing data. Figure 16.12 shows the wizard used to create a tablespace called CORMOR that holds the tables used in this book. This tablespace is stored in the datafile named CORMOR01.DBF, and its initial size is 100 megabytes. Note that the tablespace is available to users for data storage purposes. Also, you can click the Show SQL button at the top of the page to see the SQL code generated by Oracle to create the tablespace. (All DBA tasks can be accomplished through the direct use of SQL commands. In fact, some die-hard DBAs prefer writing their own SQL code rather than using the GUI.)
• Expand the tablespace storage capacity by creating additional datafiles. Remember that the datafiles can be stored in the same directory or on different disks to increase access performance. For example, you could increase storage and access performance to the USERS tablespace by creating a new datafile on a different drive.
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FIGURE16.12 CREATINGANEWORACLETABLESPACE

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16-10d Managing Users and Establishing Security
One of the most common database administration activities is creating and managing database users. The creation of user IDs is the first component of any well-planned data- base security function.
The Security section of the Oracle Enterprise Manager enables the DBA to create users, roles, and profiles.
• A user is a uniquely identifiable object that allows a given person to log on to the database. The DBA assigns privileges for accessing the objects in the database. Within the privilege assignment, the DBA may specify a set of limits that define how many database resources the user can use.
• A role is a named collection of database access privileges that authorize a user to connect to the database and use its system resources. Examples of roles are as follows:
– CONNECT allows a user to connect to the database and then create and modify tables, views, and other data-related objects.
– RESOURCE allows a user to create triggers, procedures, and other data manage- ment objects.
– DBA gives the user database administration privileges.
• A profile is a named collection of settings that control how much of the database resource a given user can access. For example, a runaway query could cause the database to lock up or stop responding to the user’s commands, so it is important to limit access to the data- base resource. By specifying profiles, the DBA can limit how much storage space a user can have, how long a user can be connected, how much idle time may be used before the user is disconnected, and so on. In an ideal world, all users would have unlimited access to all resources at all times, but realistically, such access is neither possible nor desirable.
Figure 16.13 shows the Oracle Enterprise Manager Users page. From here, the DBA can manage the database and create security objects such as users, roles, and profiles.
user
In a system, a uniquely identifiable object that allows a given person or process to log on to the database.
role
In Oracle, a named collection of database access privileges that authorize a user to connect to a database and use its system resources.
profile
In Oracle, a named collection of settings that controls how much of the database resource a given user can use.
FIGURE 16.13 THE ORACLE ENTERPRISE MANAGER USERS PAGE
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Chapter 16 Database Administration and Security 763 To create a new user, the DBA clicks the Create User button to start the wizard shown
in Figure 16.14.
The User page buttons and menu items support many actions. For example, from this page the DBA can:
• Drop the user from the database.
• Alter the user’s default and temporary tablespaces.
• Alter the privileges and roles assigned to the user.
• View the user details to adjust object privileges and quotas. Quotas allow the DBA to specify the maximum amount of storage that the user can have in each tablespace. For example, Figure 16.15 shows a user being assigned a maximum storage allocation of 20 megabytes on the CORMOR tablespace.
16-10e Customizing the Database Initialization Parameters
Fine-tuning a database is another important DBA task that usually requires the mod- ification of database configuration parameters, some of which can be changed in real time using SQL commands. Changes to other parameters require the database to be shut down and restarted. Also, some parameters may affect only the database instance, while others affect the entire RDBMS and all instances that are running. So, it is very important
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FIGURE 16.14 THE CREATE USER WIZARD

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FIGURE16.15 ASSIGNINGAUSERQUOTA
that the DBA become familiar with database configuration parameters, especially those that affect performance.
Each database has an associated initialization file that stores its run-time configu- ration parameters. The initialization file is read at instance startup and is used to set the working environment for the database. Oracle’s Enterprise Manager allows the DBA to start, shut down, view, and edit the database configuration parameters of a database instance; these parameters are stored in the initialization file. The Oracle Enterprise Manager provides a GUI to modify the file, as shown in Figure 16.16.
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FIGURE16.16 ORACLEENTERPRISEMANAGERINITIALIZATIONPARAMETERS

One of the important functions of the initialization parameters is to reserve the resources that the database uses at run time. One of those resources is the primary mem- ory reserved for database caching. Such caching is used to fine-tune database perfor- mance. For example, the “db_cache_size” parameter sets the amount of memory reserved for database caching. This parameter should be set to a value that is large enough to support all concurrent transactions. Once you modify the initialization parameters, you may be required to restart the database.
As you have seen in this brief section, the DBA is responsible for a wide range of tasks. The quality and completeness of administration tools go a long way toward making the DBA’s job easier. Even so, the DBA must become familiar with the tools and technical details of the RDBMS to perform tasks properly and efficiently.
• Data management is a critical activity for any organization, so data must be treated as a corporate asset. The value of a data set is measured by the utility of the information derived from it. Good data management is likely to produce good information, which is the basis for better decision making.
• Data quality is a comprehensive approach to ensure the accuracy, validity, and timeli- ness of data. Data quality focuses on correcting dirty data, preventing future inaccu- racies in the data, and building user confidence in the data.
• The DBMS is the most commonly used tool for corporate data management. The DBMS supports strategic, tactical, and operational decision making at all levels of the organization. The introduction of a DBMS into an organization is a delicate job; the impact of the DBMS on the organization’s managerial and cultural framework must be carefully examined.
• The database administrator (DBA) is responsible for managing the corporate data- base. The internal organization of database administration varies from company to company. Although no standard exists, it is common practice to divide DBA opera- tions according to phases of the Database Life Cycle. Some companies have created a position with a broader mandate to manage computerized data and other data; this activity is handled by the data administrator (DA).
• The DA and DBA functions tend to overlap. Generally speaking, the DA has more managerial tasks than the more technically oriented DBA. Compared to the DBA function, the DA function is DBMS-independent, with a broader and longer-term focus. However, when the organization does not include a DA position, the DBA exe- cutes all of the DA’s functions. In this combined role, the DBA must have a diverse mix of technical and managerial skills.
• A DBA’s managerial services include supporting end users; defining and enforcing policies, procedures, and standards for the database; ensuring data security, privacy, and integrity; providing data backup and recovery services; and monitoring distribu- tion and use of the data in the database.
• The DBA’s technical role requires involvement in at least the following activities: evaluating, selecting, and installing the DBMS; designing and implementing data- bases and applications; testing and evaluating databases and applications; operating and maintaining the DBMS, utilities, and applications; and training and supporting users.
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Chapter 16 Database Administration and Security 765
Summary

Key Terms
access plan
active data dictionary
audit log
authorization management
availability
back-end CASE tool
computer-aided systems engineering (CASE)
compliance
concurrent backup confidentiality
data administrator (DA) data quality
database administrator (DBA) database dump
database instance (Oracle) database security
database security officer (DSO)
datafile (Oracle)
data-profiling software
dirty data
disaster management
enterprise database
front-end CASE tool
full backup
incremental backup
information engineering (IE)
information resource dictionary
information resource manager (IRM)
information systems (IS) department
information systems architecture (ISA)
integrity
master data management (MDM) software
passive data dictionary policies
privacy
procedures
profile (Oracle)
role (Oracle)
security
security breach security policy security threat security vulnerability standards
systems administrator tablespace (Oracle) user (Oracle)
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• Security refers to activities and measures that ensure the confidentiality, integrity, and availability of an information system and its main asset, data. A security policy is a collection of standards, policies, and practices that guarantee the security of a system and ensure auditing and compliance.
• A security vulnerability is a weakness in a system component that could be exploited to allow unauthorized access or service disruption. A security threat is an imminent security violation caused by an unchecked vulnerability. Security vulnerabilities exist in all components of an information system: people, hardware, software, net- work, procedures, and data. Therefore, it is critical to have robust database security. Database security refers to DBMS features and related measures that comply with the organization’s security requirements.
• The development of a data administration strategy is closely related to the company’s mission and objectives. Therefore, the strategic plan requires a detailed analysis of company goals, its situation, and its business needs. To guide the development of this data administration plan, an integrating methodology is required. The most com- monly used integrating methodology is known as information engineering (IE).
• To help translate strategic plans into operational plans, the DBA has access to an arse- nal of database administration tools, including a data dictionary and computer-aided systems engineering (CASE) tools.
• With the introduction of reliable cloud-based data services, the role of the DBA has expanded beyond corporate walls.
Online Content
Flashcards and crossword puzzles for key term practice are available at www.cengagebrain.com.
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1. Explain the difference between data and information. Give some examples of raw data and information.
2. Define dirty data, and identify some of its sources.
3. What is data quality, and why is it important?
4. Explain the interactions among end users, data, information, and decision making. Draw a diagram and explain the interactions.
5. Suppose that you are a DBA. What data dimensions would you describe to top-level managers to obtain their support for data administration?
6. How and why did database management systems become the data management standard in organizations? Discuss some advantages of the database approach over the file-system approach.
7. Using a single sentence, explain the role of databases in organizations. Then explain your answer in more detail.
8. Define security and privacy. How are the two concepts related?
9. Describe and contrast information needs at the strategic, tactical, and operational
levels of an organization. Use examples to explain your answer.
10. What special considerations must you take into account when introducing a DBMS into an organization?
11. Describe the DBA’s responsibilities.
12. How can the DBA function be placed within the organization chart? What effects will that placement have on the DBA function?
13. Whyandhowarenewtechnologicaladvancesincomputersanddatabaseschanging the DBA’s role?
14. Explain the DBA department’s internal organization based on the DBLC approach.
15. Explain and contrast differences and similarities between the DBA and DA.
16. Explain how the DBA plays an arbitration role between an organization’s two main assets. Draw a diagram to illustrate your explanation.
17. Describe and characterize the skills desired for a DBA.
18. What are the DBA’s managerial roles? Describe the managerial activities and ser- vices provided by the DBA.
19. What DBA activities support end users?
20. Explain the DBA’s managerial role in the definition and enforcement of policies, procedures, and standards.
21. Protectingdatasecurity,privacy,andintegrityareimportantdatabasefunctions.What activities are required in the DBA’s managerial role of enforcing those functions?
22. Discusstheimportanceandcharacteristicsofdatabasebackupandrecoveryproce- dures. Then describe the actions that must be detailed in backup and recovery plans.
23. Assumethatyourcompanyhasassignedyoutheresponsibilityofselectingthecor- porate DBMS. Develop a checklist of the technical issues and other aspects involved in the selection process.
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24. Describe the activities that are typically associated with the design and implementation services of the DBA’s technical function. What technical skills are desirable in a DBA?
25. Why are testing and evaluation of the database and applications not done by the same people who are responsible for design and implementation? What minimum standards must be met during testing and evaluation?
26. IdentifysomebottlenecksinDBMSperformance,andthenproposesomesolutions used in DBMS performance tuning.
27. What are typical activities in the maintenance of the DBMS and its utilities and applications? Would you consider application performance tuning to be part of the maintenance activities? Explain your answer.
28. Howdoyounormallydefinesecurity?Howisyourdefinitionsimilartoordifferent from the definition of database security in this chapter?
29. What are the levels of data confidentiality?
30. Whataresecurityvulnerabilities?Whatisasecuritythreat?Givesomeexamplesof security vulnerabilities in different IS components.
31. Definetheconceptofadatadictionary,anddiscussthedifferenttypesofdatadictio- naries. If you managed an organization’s entire data set, what characteristics would you want for the data dictionary?
32. Using SQL statements, give some examples of how you would use the data dictio- nary to monitor database security.
33. WhatcharacteristicsdoaCASEtoolandaDBMShaveincommon?Howcanthose characteristics be used to enhance data administration?
34. Briefly explain the concepts of information engineering (IE) and information systems architecture (ISA). How do those concepts affect the data administra- tion strategy?
35. Identifyandexplainsomecriticalsuccessfactorsinthedevelopmentandimplemen- tation of a good data administration strategy.
36. How have cloud-based data services affected the DBA’s role?
37. What tool is used in Oracle to create users?
38. In Oracle, what is a tablespace?
39. In Oracle, what is a database role?
40. In Oracle, what is a datafile? How does it differ from a file systems file?
41. In Oracle, what is a database profile?
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GLOSSARY
A
abstract data type (ADT)
Data type that describes a set of similar objects with shared and encapsulated data representation and methods. An abstract data type is generally used to describe complex objects. See also class.
access plan
A set of instructions generated at application compilation time that is created and managed by a DBMS. The access plan predetermines how an application’s query will access the database at run time.
access point
In the case of wireless networks, this allows you to connect wireless devices to a wired or wireless network.
active data dictionary
A data dictionary that is automatically updated by the database management system every time the database is accessed, thereby keeping its information current.
ActiveX
Microsoft’s alternative to Java. A specification for writing programs that will run inside the Microsoft client browser. Oriented mainly to Windows applica- tions, it is not portable. It adds controls such as drop-down windows and calendars to webpages.
ActiveX Data Objects (ADO)
A Microsoft object framework that provides a high-level, application-oriented interface to OLE- DB, DAO, and RDO. ADO provides a unified interface to access data from any programming language that uses the underlying OLE-DB objects.
ad hoc query
A“spur-of-the-moment”question.
ADO.NET
The data access component of Microsoft’s .NET application development framework.
algorithms
A process or set of operations in a calculation.
alias
An alternative name for a column or table in a SQL statement.
ALTER TABLE
The SQL command used to make changes to table structure. When the command is followed by a keyword (ADD or MODIFY), it adds a column or changes column characteristics.
American National Standards Institute (ANSI)
The group that accepted the DBTG recommenda- tions and augmented database standards in 1975 through its SPARC committee.
analytical database
A database focused primarily on storing historical data and business metrics used for tactical or strategic decision making.
AND
The SQL logical operator used to link multiple conditional expressions in a WHERE or HAVING
clause. It requires that all conditional expressions evaluate to true.
anonymous PL/SQL block
A PL/SQL block that has not been given a specific name.
application processor (AP)
See transaction processor (TP).
Application Program-to-Program Communications (APPC)
A communications protocol used in IBM mainframe systems network architecture (SNA). Allows for communications between personal computers and IBM mainframe applications.
application programming interface (API)
Software through which programmers interact with middleware. An API allows the use of generic SQL code, thereby allowing client processes to be database server-independent.
AREA
In DB2, a named section of permanent storage space that is reserved to store the database.
associative entity
See composite entity.
associative object
In Object-oriented modeling, an object used to rep- resent a relationship between two or more objects.
asymmetric encryption
A form of encryption that uses two numeric keys— the public key and the private key. Both keys are able to encrypt and decrypt each other’s messages. See also public-key encryption.
atomic attribute
An attribute that cannot be further subdivided to produce meaningful components. For example, a person’s last name attribute cannot be meaningfully subdivided.
atomic transaction property
A property that requires all parts of a transaction to be treated as a single, logical unit of work in which all operations must be completed (committed) to produce a consistent database.
atomicity
The transaction property that requires all parts of a transaction to be treated as a single, indivisible, logical unit of work. All parts of a transaction must be completed or the entire transaction is aborted.
attribute
A characteristic of an entity or object. An attribute has a name and a data type.
attribute hierarchy
A top-down data organization that is used for two main purposes: aggregation and drill-down/roll-up data analysis.
audit log
A security feature of a database management system that automatically records a brief description of the database operations performed by all users.
authentication
The process through which a DBMS verifies that only registered users can access the database.
authorization management
Procedures that protect and guarantee database security and integrity. Such procedures include user access management, view definition, DBMS access control, and DBMS usage monitoring.
automatic query optimization
A method by which a DBMS finds the most efficient access path for the execution of a query.
availability
In the context of data security, it refers to the ac- cessibility of data whenever required by authorized users and for authorized purposes.
AVG
A SQL aggregate function that outputs the mean average for a specified column or expression.
B
B-tree index
An ordered data structure organized as an upside-down tree.
back-end application
The process that provides service to clients.
back-end CASE tool
A computer-aided software tool that provides support for the coding and implementation phases of the SDLC. In comparison, front-end CASE tools provide support for the planning, analysis, and design phases.
balancing
Ensuring that the processing load is distributed evenly among multiple servers.
base data types
A term used to describe the data types frequently used in traditional programming languages. Base data types include real, integer, and string.
base tables
The table on which a view is based.
basically available, soft state, eventually consistent (BASE)
A data consistency model in which data changes are not immediate but propagate slowly through the system until all replicas are eventually consistent.
batch processing
A data processing method that runs data processing tasks from beginning to end without any user interaction.
batch update routine
A routine that pools transactions into a single group to update a master table in a single operation.
BETWEEN
In SQL, a special comparison operator used to check whether a value is within a range of specified values.
bidirectional physically paired logical relationships
In the hierarchical model, a relationship that links a logical child with its logical parent in two directions.
bidirectional virtually paired logical relationships
In the hierarchical model, a relationship created when a logical child segment is linked to its logical parent in two directions. The virtually paired relationship is different from the phys- ically paired relationship in that no duplicates are created.
Big Data
A movement to find new and better ways to manage large amounts of web-generated data and derive business insight from it, while simultaneous- ly providing high performance and scalability at a reasonable cost.
binary lock
A lock that has only two states: locked (1) and un- locked (0). If a data item is locked by a transaction, no other transaction can use that data item.
binary relationship
An ER term for an association (relationship) between two entities. For example, PROFESSOR teaches CLASS.
bitmap index
An index that uses a bit array (0s and 1s) to repre- sent the existence of a value or condition.
block report
In the Hadoop Distributed File System (HDFS), a report sent every 6 hours by the data node to the name node informing the name node which blocks are on that data node.
Boolean algebra
A branch of mathematics that uses the logical operators OR, AND, and NOT.
bottom-up design
A design philosophy that begins by identifying individual design components and then aggregates them into larger units. In database design, the process begins by defining attributes and then groups them into entities.
boundaries
The external limits to which any proposed system is subjected. These limits include budgets, personnel, and existing hardware and software.
Boyce-Codd normal form (BCNF)
A special type of third normal form (3NF) in which every determinant is a candidate key. A table in BCNF must be in 3NF. See also determinant.
bridge
A device that connects similar networks. Allows computers in one network to communicate with computers in another network.
bridge entity
See composite entity.
BSON (Binary JSON)
A computer-readable format for data interchange that expands the JSON format to include additional data types including binary objects.
bucket
In a key-value database, a logical collection of related key-value pairs.
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Glossary 769

770 Glossary buffer cache
See data cache.
buffers
Temporary storage area in primary memory used to speed up disk operations.
bus topology
Network topology requiring that all computers be connected to a main network cable. It bears the disadvantage that a single lost computer can result in network segment breakdown.
Business intelligence (BI)
A comprehensive, cohesive, and integrated set
of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support business decision making.
business rule
A description of a policy, procedure, or principle within an organization. For example, a pilot cannot be on duty for more than 10 hours during a 24-hour period, or a professor may teach up to four classes during a semester.
Business to business (B2B)
Electronic commerce between businesses.
Business to consumer (B2C)
Electronic commerce between a business and consumers.
C
Call Level Interface (CLI)
A standard developed by the SQL Access Group for database access.
campuswide network (CWN)
A typical college or university network in which buildings containing LANs are connected through a network backbone.
candidate key
A minimal superkey; that is, a key that does not contain a subset of attributes that is itself a superkey. See key.
cardinality
A property that assigns a specific value to connec- tivity and expresses the range of allowed entity occurrences associated with a single occurrence of the related entity.
cascading order sequence
A nested ordering sequence for a set of rows, such as a list in which all last names are alphabetically ordered and, within the last names, all first names are ordered.
CASE
See Computer-aided software engineering.
centralized data allocation
A data allocation strategy in which the entire database is stored at one site. Also known as a centralized database.
centralized database
A database located at a single site.
centralized design
A process by which all database design decisions are carried out centrally by a small group of people. Suitable in a top-down design approach when the problem domain is relatively small, as in a single unit or department in an organization.
certification authority (CA)
In the context of Internet security, a private entity or company that certifies the user or vendor is who (s) he claims to be.
checkpoints
In transaction management, an operation in which the database management system writes all of its updated buffers to disk.
Chen notation
See entity relationship (ER) model.
class
A collection of similar objects with shared structure (attributes) and behavior (methods). A class encapsulates an object’s data representation and a method’s implementation. Classes are organized in a class hierarchy.
class diagram notation
The set of symbols used in the creation of class diagrams in UML object modeling.
class diagrams
A diagram used to represent data and their relation- ships in UML object notation.
class hierarchy
The organization of classes in a hierarchical tree in which each parent class is a superclass and each child class is a subclass. See also inheritance.
class instance
Each individual object stored in a class. Each class instance must share the same structure and respond to the same messages if they are located in the same class. Also known as object instance.
class lattice
The class hierarchy is known as a class lattice if its classes can have multiple parent classes.
client
Any process that requests specific services from server processes in a client/server environment.
client node
One of three types of nodes used in the Hadoop Distributed File System (HDFS). The client node acts as the interface between the user application and the HDFS. See also name node and data node.
client-side extensions
Extension that adds functionality to a web browser. The most common extensions are plug-ins, Java, JavaScript, ActiveX, and VBScript.
client/server architecture
A hardware and software system composed of clients, servers, and middleware. Features a user of resources (client) and a provider of resources (server).
closure
A property of relational operators that permits the use of relational algebra operators on existing tables (relations) to produce new relations.
cloud computing
A computing model that provides ubiquitous, on-demand access to a shared pool of configurable resources that can be rapidly provisioned.
cloud database
A database that is created and maintained using cloud services, such as Microsoft Azure or Amazon AWS.
cloud services
The services provided by cloud computing. Cloud services allow any organization to quickly and economically add information technology services such as applications, storage, servers, processing power, databases, and infrastructure.
cluster tables
A data storage structure that physically stores related rows from different tables together to improve the speed at which related data can be accessed.
clustered index table
See index organized table.
coaxial cable
Copper cables enclosed in two layers of insulation or shielding. Often referred to as“coax.”Very similar to cable used for home cable TV.
cohesivity
The strength of the relationships between a module’s components. Module cohesivity must be high.
ColdFusion Markup Language (CFML)
A server-side markup language (HTML extensions or tags) that is used to create ColdFusion application pages known as scripts.
collection object
An object that contains one or more objects.
column family
In a column family database, a collection of columns or super columns related to a collection of rows.
column family database
A NoSQL database model that organizes
data into key-value pairs, in which the value component is composed of a set of columns that vary by row.
column-centric storage
A physical data storage technique in which data is stored in blocks, which hold data from a single column across many rows.
COMMIT
The SQL command that permanently writes data changes to a database.
Common Gateway Interface (CGI)
A web server interface standard that uses script files to perform specific functions based on a client’s parameters.
community cloud
A type of cloud built by and for a specific group of organizations that share a common trade, such as agencies of the federal government, the military, or higher education.
completeness constraint
A constraint that specifies whether each entity supertype occurrence must also be a member of at least one subtype. The completeness constraint can be partial or total.
complex object
An object formed by several different objects in complex relationships. See also abstract data types.
compliance
In the context of data security, activities that meet data privacy and security reporting guidelines or requirements.
composite attribute
An attribute that can be further subdivided to yield additional attributes. For example, a phone number such as 615-898-2368 may be divided into an
area code (615), an exchange number (898), and a four-digit code (2368). Compare to simple attribute.
composite entity
An entity designed to transform an M:N relation- ship into two 1:M relationships. The composite entity’s primary key comprises at least the primary keys of the entities that it connects. Also known as a bridge entity or associative entity. See also linking table.
composite identifier
In ER modeling, a key composed of more than one attribute.
composite key
A multiple-attribute key.
composite object
An object that contains at least one multivalued attribute and has no attributes that refer to another object.
compound object
An object that contains at least one attribute that references another object.
computer-aided software engineering (CASE)
Tools used to automate part or all of the Systems Development Life Cycle. Also known as computer-aided systems engineering.
computer-aided systems engi- neering
See computer-aided software engineering.
concentrator
A device that takes multiple wires and combines them into a single method of transfer to allow multiple users to access the line simultaneously. It resembles a network wiring closet.
conceptual design
A process that uses data-modeling techniques to create a model of a database structure that represents real-world objects as realistically as possible. The The design is both software- and hardware-independent.
conceptual model
The output of the conceptual design process. The conceptual model provides a global view of an entire database and describes the main data objects, avoiding details.
conceptual schema
A representation of the conceptual model, usually expressed graphically. See also conceptual model.
concurrency control
A DBMS feature that coordinates the simultaneous execution of transactions in a multiprocessing database system while preserving data integrity.
concurrent backup
A backup that takes place while one or more users are working on a database.
confidentiality
In the context of data security, ensuring that data
is protected against unauthorized access, and if the data is accessed by an authorized user, that the data is used only for an authorized purpose.
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connectivity
The type of relationship between entities. Classifica- tions include 1:1, 1:M, and M:N.
consistency
A database condition in which all data integrity constraints are satisfied. To ensure consistency of a database, every transaction must begin with the database in a known consistent state. If not, the transaction will yield an inconsistent database that violates its integrity and business rules.
consistent database state
A database state in which all data integrity constraints are satisfied.
constraint
A restriction placed on data, usually expressed in the form of rules. For example, “A student’s GPA must be between 0.00 and 4.00.”Constraints are important because they help to ensure data integrity.
content management
Automation of the creation and management of a Web site’s contents.
conventional data types
See base data types.
coordinator
The transaction processor (TP) node that coordinates the execution of a two-phase COMMIT in a DDBMS.
correlated subquery
A subquery that executes once for each row in the outer query.
cost-based optimizer
A query optimization mode that uses an algorithm based on statistics about the objects being accessed, including number of rows, indexes available, index sparsity, and so on.
COUNT
A SQL aggregate function that outputs the number of rows containing not null values for a given col- umn or expression, sometimes used in conjunction with the DISTINCT clause.
CREATE INDEX
A SQL command that creates indexes on the basis of a selected attribute or attributes.
CREATE TABLE
A SQL command that creates a table’s structures using the characteristics and attributes given.
CREATE VIEW
A SQL command that creates a logical,“virtual” table. The view can be treated as a real table.
cross join
A join that performs a relational product (or Cartesian product) of two tables.
Crow’s Foot notation
A representation of the entity relationship diagram that uses a three-pronged symbol to represent the “many”sides of the relationship.
cube cache
In multidimensional OLAP, the shared, reserved memory area where data cubes are held. Using the cube cache assists in speeding up data access.
currency
In the Network Data Model, this term indicates the position of the record pointer within the database and refers to the most recently accessed record.
cursor
A special construct used in procedural SQL to hold the data rows returned by a SQL query. A cursor may be considered a reserved area of memory in which query output is stored, like an array holding columns and rows. Cursors are held in a reserved memory area in the DBMS server, not in the client computer.
D
dashboards
In business intelligence, a web-based system that presents key business performance indicators or information in a single, integrated view with clear and concise graphics.
data
Raw facts, or facts that have not yet been processed to reveal their meaning to the end user.
Data Access Objects (DAO)
An object-oriented application programming interface used to access MS Access, FileMaker Pro, and other Jet-based databases.
data administrator (DA)
The person responsible for managing the entire data resource, whether it is computerized or not. The DA has broader authority and responsibility than the database administrator (DBA). Also known as an information resource manager (IRM).
data allocation
In a distributed DBMS, the process of deciding where to locate data fragments.
data analytics
A subset of business intelligence functionality that encompasses a wide range of mathematical, statis- tical, and modeling techniques with the purpose of extracting knowledge from data.
data anomaly
A data abnormality in which inconsistent changes have been made to a database. For example, an employee moves, but the address change is not corrected in all files in the database.
data cache
A shared, reserved memory area that stores the most recently accessed data blocks in RAM. Also called buffer cache.
data cube
The multidimensional data structure used to store and manipulate data in a multidimensional DBMS. The location of each data value in the data cube is based on its x-, y-, and z-axes. Data cubes are static, meaning they must be created before they are used, so they cannot be created by an ad hoc query.
data definition language (DDL)
The language that allows a database administrator to define the database structure, schema, and subschema.
data dependence
A data condition in which data representation and manipulation are dependent on the physical data storage characteristics.
data dictionary
A DBMS component that stores metadata—data about data. Thus, the data dictionary contains the data definition as well as their characteristics and relationships. A data dictionary may also include data that are external to the DBMS. Also known as an information resource dictionary. See also
active data dictionary, metadata, and passive data dictionary.
Data Encryption Standard (DES)
The most widely used standard for private-key encryption. DES is used by the U.S. government.
data files
A named physical storage space that stores a database’s data. It can reside in a different directory on a hard disk or on one or more hard disks. All data in a database is stored in data files. A typical enterprise database is normally composed of several data files. A data file can contain rows from one or more tables.
data fragmentation
A characteristic of a DDBMS that allows a single object to be broken into two or more segments or fragments. The object might be a user’s database, a system database, or a table. Each fragment can be stored at any site on a computer network.
data inconsistency
A condition in which different versions of the same data yield different (inconsistent) results.
data independence
A condition in which data access is unaffected by changes in the physical data storage characteristics.
data integrity
In a relational database, a condition in which the data in the database complies with all entity and referential integrity constraints.
data management
A process that focuses on data collection, storage, and retrieval. Common data management functions include addition, deletion, modification, and listing.
data manager (DM)
See data processor (DP).
data manipulation language
The set of commands that allows an end user to manipulate the data in the database, such as SELECT, INSERT, UPDATE, DELETE, COMMIT, and ROLLBACK.
data mart
A small, single-subject data warehouse subset that provides decision support to a small group of people.
data mining
A process that employs automated tools to analyze data in a data warehouse and other sources and to pro- actively identify possible relationships and anomalies.
data model
A representation, usually graphic, of a complex “real-world”data structure. Data models are used in the database design phase of the Database Life Cycle.
data modeling
The process of creating a specific data model for a determined problem domain.
data node
One of three types of nodes used in the Hadoop Distributed File System (HDFS). The data node stores fixed-size data blocks (that could be replicated to other data nodes). See also client node and name node.
data processing (DP) specialist
The person responsible for developing and manag- ing a computerized file processing system.
Glossary 771 data processor (DP)
The resident software component that stores and re- trieves data through a DDBMS. The DP is responsible for managing the local data in the computer and coordinating access to that data. Also known as data manager (DM).
data quality
A comprehensive approach to ensuring the accura- cy, validity, and timeliness of data.
data redundancy
Exists when the same data is stored unnecessarily at different places.
data replication
The storage of duplicated database fragments at mul- tiple sites on a DDBMS. Duplication of the fragments is transparent to the end user. Data replication provides fault tolerance and performance enhancements.
data source name (DSN)
With ODBC, a name that identifies and defines an ODBC data source.
data sparsity
A column distribution of values or the number of different values a column can have.
data visualization
Abstracting data to provide information in a visual format that enhances the user’s ability to effectively comprehend the meaning of the data.
data warehouse
An integrated, subject-oriented, time-variant, nonvolatile collection of data in a specialized database that stores historical and aggregated data in a format that provides support for decision making.
data-profiling software
Programs that analyze data and metadata to deter- mine patterns that can help assess data quality.
database
A shared, integrated computer structure that houses
a collection of related data. A database contains two types of data: end-user data (raw facts) and metadata.
database administrator (DBA)
The person responsible for planning, organizing, controlling, and monitoring the centralized
and shared corporate database. The DBA is the general manager of the database administration department.
Database Administrator Control System (DBACS)
In the network model, the database definition pro- cessor that reads the database definition and validates the schema (The DBACS works like a compiler.).
database description (DBD) statement
In the hierarchical model, the series of commands that define the hierarchical tree structure and how the segments are stored in the database.
database design
The process that yields the description of the database structure and determines the database components. The second phase of the Database Life Cycle.
database development
The process of database design and implemen- tation.
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772 Glossary database dump
A complete copy of an entire database saved and periodically updated in a separate memory location. A full backup ensures a full recovery of all data after a physical disaster or database integrity failure.
database fragment
A subset of a distributed database. Although the fragments may be stored at different sites within a computer network, the set of all fragments is treated as a single database. See also horizontal fragmentation and vertical fragmentation.
database instance
In an Oracle DBMS, the collection of processes and data structures used to manage a specific database.
database integration
In the context of e-commerce databases, a business enabling service where a company’s database is used in their Web page development.
Database Life Cycle (DBLC)
A cycle that traces the history of a database within an information system. The cycle is divided into six phases: initial study, design, implementation and loading, testing and evaluation, operation and maintenance, and evolution.
database management system (DBMS)
The collection of programs that manages the database structure and controls access to the data stored in the database.
database middleware
Database connectivity software through which application programs connect and communicate with data repositories.
database performance tuning
A set of activities and procedures designed to reduce the response time of a database system— that is, to ensure that an end-user query is processed by the DBMS in the minimum amount of time.
database recovery
The process of restoring a database to a previous consistent state.
database request
The equivalent of a single SQL statement in an application program or a transaction.
database role
A set of database privileges that could be assigned as a unit to a user or group.
database security
The use of DBMS features and other related measures to comply with the security requirements of an organization.
database security officer (DSO)
The person responsible for the security, integrity, backup, and recovery of the database.
database statistics
In query optimization, measurements about database objects, such as the number of rows in a table, number of disk blocks used, maximum and average row length, number of columns in each row, and number of distinct values in each column. Such statistics provide a snapshot of database characteristics.
database system
An organization of components that defines and regulates the collection, storage, management, and use of data in a database environment.
database translator
A middleware component that translates generic SQL calls into specific database server syntax to create database server independence.
database-level lock
A type of lock that restricts database access to the owner of the lock and allows only one user at a time to access the database. This lock works for batch processes but is unsuitable for online multiuser DBMSs.
datafile
A file on the hard drive or storage system where the data in a tablespace is physically stored.
DataSet
In ADO.NET, a disconnected, memory-resident representation of the database. The DataSet contains tables, columns, rows, relationships, and constraints.
DBGEN
In the hierarchical model, the component that generates the physical database with all its necessary structures.
DBMS performance tuning
Activities to ensure that clients’requests are addressed as quickly as possible while making optimum use of existing resources.
deadlock
A condition in which two or more transactions wait indefinitely for the other to release the lock on a previously locked data item. Also called deadly embrace.
deadly embrace
See deadlock.
decentralized design
A process in which conceptual design models subsets of an organization’s database requirements, which are then aggregated into a complete design. Such modular designs are typical of complex systems with a relatively large number of objects and procedures.
decision support system (DSS)
An arrangement of computerized tools used to as- sist managerial decision making within a business.
deferred update
In transaction management, a condition in which transaction operations do not immediately update a physical database. Also called deferred write technique.
deferred-write technique
See deferred update.
DELETE
A SQL command that allows data rows to be deleted from a table.
denial-of-service
One of the most common hacker activities. This attack overloads Web servers and routers with mil- lions of requests for service, rendering the services unavailable to legitimate users.
denormalization
A process by which a table is changed from a higher-level normal form to a lower-level normal
form, usually to increase processing speed. Denor- malization potentially yields data anomalies.
dependency diagram
A representation of all data dependencies (primary key, partial, or transitive) within a table.
dependent
An attribute whose value is determined by another attribute.
derived attribute
An attribute that does not physically exist within the entity and is derived via an algorithm.
For example, the Age attribute might be derived by subtracting the birth date from the current date.
description of operations
A document that provides a precise, detailed, up-to-date, and thoroughly reviewed description of the activities that define an organization’s operating environment.
design trap
A problem that occurs when a relationship is improperly or incompletely identified and therefore is represented in a way that is not consistent with the real world. The most common design trap is known as a fan trap.
desktop database
A single-user database that runs on a personal computer.
determinant
Any attribute in a specific row whose value directly determines other values in that row. See also Boyce- Codd normal form (BCNF).
determination
The role of a key. In the context of a database table, the statement“A determines B”indicates that knowing the value of attribute A means that the value of attribute B can be looked up.
DIFFERENCE
In relational algebra, an operator used to yield all rows from one table that are not found in another union-compatible table.
differential backup
A level of database backup in which only the last modifications to the database are copied.
digital cash
In an e-commerce environment, the digital equivalent of hard currency (coins or bills of a given denomination).
digital certificate
A unique identifier given to an entity. The certificate holder may be an end user, a Web site, a computer, a Web page, or even a program. Digital certificates are used in combination with encryption to provide security and authentication.
digital signature
An encrypted attachment added to an electronic message to verify the sender’s identity.
dimension tables
In a data warehouse, tables used to search, filter, or classify facts within a star schema.
dimensions
In a star schema design, qualifying characteristics that provide additional perspectives to a given fact.
dirty data
Data that contain inaccuracies and/or inconsis- tencies.
dirty read
In transaction management, when a transaction reads data that is not yet committed.
disaster management
The set of DBA activities dedicated to securing data availability following a physical disaster or a database integrity failure.
discipline-specific databases
A database that contains data focused on specific subject areas.
disjoint subtypes
In a specialization hierarchy, a unique and nonover- lapping subtype entity set.
diskpage
In permanent storage, the equivalent of a disk block, which can be described as a directly addressable section of a disk. A diskpage has a fixed size, such as 4K, 8K, or 16K.
DISTINCT
A SQL clause that produces only a list of values that are different from one another.
distributed data catalog (DDC)
A data dictionary that contains the description (fragment names and locations) of a distributed database.
distributed data dictionary (DDD)
See distributed data catalog.
distributed database
A logically related database that is stored in two or more physically independent sites.
distributed database management system (DDBMS)
A DBMS that supports a database distributed across several different sites; a DDBMS governs the storage and processing of logically related data over interconnected computer systems in which both data and processing functions are distributed among several sites.
distributed global schema
The database schema description of a distributed database as seen by the database administrator.
distributed processing
Sharing the logical processing of a database over two or more sites connected by a network.
distributed request
A database request that allows a single SQL statement to access data in several remote data processors (DPs) in a distributed database.
distributed transaction
A database transaction that accesses data in
several remote data processors (DPs) in a distributed database.
distribution transparency
A DDBMS feature that allows a distributed database to look like a single logical database to an end user.
DIVIDE
In relational algebra, an operator that answers queries about one set of data being associated with all values of data in another set of data.
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DO-UNDO-REDO protocol
A protocol used by a data processor (DP) to roll back or roll forward transactions with the help of a system’s transaction log entries.
document databases
A NoSQL database model that stores data in key-value pairs in which the value component is composed of a tag-encoded document.
document type definition (DTD)
A file with a .dtd extension that describes XML el- ements; in effect, a DTD file describes a document’s composition and defines the syntax rules or valid tags for each type of XML document.
domain
In data modeling, the construct used to organize and describe an attribute’s set of possible values.
drill down
To decompose data into more atomic compo- nents—that is, data at lower levels of aggregation. This approach is used primarily in a decision support system to focus on specific geographic areas, business types, and so on.
DROP INDEX
A SQL command used to delete database objects such as tables, views, indexes, and users.
DROP TABLE
A SQL command used to delete database objects such as tables, views, indexes, and users.
durability
The transaction property that ensures that once transaction changes are done and committed, they cannot be undone or lost, even in the event of a system failure.
dynamic query optimization
The process of determining the SQL access strategy at run time, using the most up-to-date information about the database.
dynamic SQL
An environment in which the SQL statement is not known in advance, but instead is generated at run time. In a dynamic SQL environment, a program can generate the SQL statements that are required to respond to ad hoc queries.
dynamic statistical generation mode
In a DBMS, the capability to automatically evaluate and update the database access statistics after each data access operation.
dynamic webpage
Page with contents that change over time and can- not be anticipated; for example, an online ordering system. The content of dynamic Web pages is not predetermined, but is generated at run-time.
dynamic-link libraries (DLLs)
Shared code module that is treated as part of the operating system or server process so it can be dynamically invoked at run time.
E
early binding
A property by which the data type of an object’s attribute must be known at definition time, bonding the data type to the object’s attribute. Characteristic of an object oriented data model. See also late binding.
edge
In a graph database, the representation of a relationship between nodes.
EER diagram (EERD)
The entity relationship diagram resulting from the application of extended entity relationship concepts that provide additional semantic content in the
ER model.
electronic commerce (e-commerce)
The use of electronic computer-based technology to bring products, services, or ideas to market and to support or enhance business operations.
electronic data interchange (EDI)
A communications protocol that enabled companies to exchange business documents over private phone networks.
electronic mail (email)
The graphics and text messages sent to other, specific computer end users connected to the same network. Widely used for its low cost and ability
to instantly send and receive information to one or more people. Also called e-mail.
electronic wallet
Software that securely stores digital cash to facilitate online transactions.
embedded SQL
SQL statements contained within application programming languages such as COBOL, C++, ASP, Java, and ColdFusion.
encapsulation
A feature by which the object can hide the internal data representation and method’s implementation from external objects. Characteristic of an object oriented data model.
encryption
A process of inputting data in“plain text”to yield an output“encoded”version of the data, making the data unintelligible to unauthorized users.
encryption key
Used by encryption algorithms to encode data. The encryption key is a very large number used to encrypt and decrypt data.
enterprise database
The overall company data representation, which provides support for present and expected future needs.
entity
A person, place, thing, concept, or event for which data can be stored. See also attribute.
entity cluster
A“virtual”entity type used to represent multiple entities and relationships in the ERD. An entity cluster is formed by combining multiple interrelat- ed entities into a single abstract entity object. An entity cluster is considered“virtual”or“abstract” because it is not actually an entity in the final ERD.
entity instance
A row in a relational table. Also known as entity occurrence.
entity integrity
The property of a relational table that guarantees each entity has a unique value in a primary key and that the key has no null values.
entity occurrence
A row in a relational table. Also known as entity instance.
entity relationship (ER) model
A data model that describes relationships (1:1, 1:M, and M:N) among entities at the conceptual level with the help of ER diagrams. The model was developed by Peter Chen.
entity relationship diagram (ERD)
A diagram that depicts an entity relationship model’s entities, attributes, and relations.
entity set
A collection of like entities.
entity subtypes
In a generalization/specialization hierarchy, a subset of an entity supertype. The entity supertype con- tains the common characteristics and the subtypes contain the unique characteristics of each entity.
entity supertype
In a generalization/specialization hierarchy, a generic entity type that contains the common characteristics of entity subtypes.
equijoin
A join operator that links tables based on an equality condition that compares specified columns of the tables.
ERM
A data model that describes relationships (1:1, 1:M, and M:N) among entities at the conceptual level with the help of ER diagrams. The model was developed by Peter Chen.
Ethernet
The dominant LAN standard used to interconnect computer systems. Ethernet is based on a bus or star topology that can use coaxial, twisted-pair, or fiber-optic cabling.
eventual consistency
A model for database consistency in which updates to the database will propagate through the system so that all data copies will be consistent eventually.
exclusive lock
An exclusive lock is issued when a transaction requests permission to update a data item and
no locks are held on that data item by any other transaction. An exclusive lock does not allow other transactions to access the database.
existence-dependent
A property of an entity whose existence depends on one or more other entities. In such an environment, the existence-independent table must be created and loaded first because the existence-dependent key cannot reference a table that does not yet exist.
existence-independent
A property of an entity that can exist apart from one or more related entities. Such a table must be created first when referencing an existencedepen- dent table.
EXISTS
In SQL, a comparison operator that checks whether a subquery returns any rows.
explanatory analytics
Data analysis that provides ways to discover relationships, trends, and patterns among data.
Glossary 773 In procedural SQL, a cursor created to hold the
output of a SQL statement that may return two or more rows, but could return zero or only one row.
extended entity relationship model (EERM)
Sometimes referred to as the enhanced entity relationship model; the result of adding more semantic constructs, such as entity supertypes, entity subtypes, and entity clustering, to the original entity relationship (ER) model.
extended relational data model
A model that includes the object-oriented model’s best features in an inherently simpler relational database structural environment. See extended entity relationship model (EERM).
extensible
Capable of being extended by adding new data types and the operations to be performed on them.
Extensible Markup Language (XML)
A meta-language used to represent and manipulate data elements. Unlike other markup languages, XML permits the manipulation of a document’s data elements. XML facilitates the exchange of structured documents such as orders and invoices over the Internet.
extents
In a DBMS environment, refers to the ability of data files to expand in size automatically using predefined increments.
external model
The application programmer’s view of the data environment. Given its business focus, an external model works with a data subset of the global database schema.
external schema
The specific representation of an external view; the end user’s view of the data environment.
extraction, transformation, and loading (ETL)
In a data warehousing environment, the integrated processes of getting data from original sources into the data warehouse. ETL includes retrieving data from original data sources (extraction), manipulating the data into an appropriate form (transformation), and storing the data in the data warehouse (loading).
F
fact table
In a data warehouse, the star schema table that contains facts linked and classified through their com- mon dimensions. A fact table is in a one-to-many relationship with each associated dimension table.
facts
In a data warehouse, the measurements (values) that measure a specific business aspect or activity. For example, sales figures are numeric measure- ments that represent product or service sales. Facts commonly used in business data analysis include units, costs, prices, and revenues.
failure transparency
A feature that allows continuous operation of a DDBMS, even if a network node fails.
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explicit cursor

774 Glossary fan trap
A design trap that occurs when one entity is in two 1:M relationships with other entities, thus producing an association among the other entities that is not expressed in the model.
fat client
A client that carries a relatively larger proportion of the processing load than compared to the server. Fat clients are always paired with thin servers.
fat server
A server that carries a relatively larger proportion of the processing load than compared to the client. Fat servers are always paired with thin clients.
feedback loop processing
Analyzing stored data to produce actionable results.
fiber-optic cable
Data transmission medium for computer networks. It used light pulses to transmit the data from
node to node and allows for the highest speed of information transfer available.
field
An alphabetic or numeric character or group of characters that defines a characteristic of a person, place, or thing. For example, a person’s Social Security number, address, phone number, and bank balance all constitute fields.
field-level lock
A lock that allows concurrent transactions to access the same row as long as they require the use of different fields (attributes) within that row. This type of lock yields the most flexible multiuser data access but requires a high level of computer overhead.
file
A named collection of related records.
file group
See table space.
File Transfer Protocol (FTP)
Used to provide file transfer capabilities among computers on the Internet/intranet using a public or known name for classifying and grouping the messages.
firewall
Used to protect a network from unauthorized access from the outside world (public Internet). Specifically, a firewall is a hardware and/or software component that is used to limit and control Internet traffic going into a company’s network infrastruc- ture and data that are allowed to be moved outside a company’s network.
first normal form (1NF)
The first stage in the normalization process. It describes a relation depicted in tabular format, with no repeating groups and a primary key identified. All nonkey attributes in the relation are dependent on the primary key.
flags
Special codes implemented by designers to trigger a required response, alert end users to specified conditions, or encode values. Flags may be used to prevent nulls by bringing attention to the absence of a value in a table.
foreign key (FK)
An attribute or attributes in one table whose values must match the primary key in another table or whose values must be null. See key.
fourth normal form (4NF)
A table is in 4NF if it is in 3NF and contains no mul- tiple independent sets of multivalued dependencies.
fragmentation transparency
A DDBMS feature that allows a system to treat a distributed database as a single database even though it is divided into two or more fragments.
frame
Create in the data-link layer to add control to the information by specifying the network and physical media being used. The information is added at the beginning (header) and at the end (trailer) of a network packet to enclose (frame) the packet data.
FROM
A SQL clause that specifies the table or tables from which data is to be retrieved.
front-end application
Any process that the end user interacts with to request services from a server process.
front-end CASE tool
A computer-aided software tool that provides support for the planning, analysis, and design phases of the SDLC.
full backup
A complete copy of an entire database saved and periodically updated in a separate memory location. A full backup ensures a full recovery of all data after a physical disaster or database integrity failure.
full functional dependence
A condition in which an attribute is functionally dependent on a composite key but not on any subset of the key.
fully heterogeneous DDBMS
A system that integrates different types of database management systems (hierarchical, network, and relational) over a network. It supports different database management systems that may even support different data models running under different computer systems. See also heterogeneous DDBMS and homogeneous DDBMS.
fully replicated database
In a DDBMS, the distributed database that stores multiple copies of each database fragment at multiple sites.
function-based index
A type of index based on a specific SQL function or expression.
functional dependence
Within a relation R, an attribute B is functionally dependent on an attribute A if and only if a given value of attribute A determines exactly one value of attribute B.The relationship“B is dependent on A”is equivalent to“A determines B,”and is written as A B.
G
gateway
A type of middleware software that is used to trans- late client requests into the appropriate protocols needed to access specific services.
gateway server firewall
A type of firewall that operates at the application level.
general-purpose databases
A database that contains a wide variety of data used in multiple disciplines.
generalization
In a specialization hierarchy, the grouping of common attributes into a supertype entity.
Get Hold (GH)
In an IMS hierarchical DBMS, this statement is used to hold a segment for delete or replace operations. There are three different Get Hold statements: Get Hold Next (GHN), Get Hold Next within Parent (GHNP), and Get Hold Unique (GHU).
Get Next (GN)
In hierarchical databases, a statement to retrieve sequential segments.
Get Next Within Parent (GNP)
In hierarchical databases, a statement to return all segments within the current parent.
Get Unique (GU)
In an IMS hierarchical DBMS, a statement that is used to retrieve a database segment into the application program input area or record area.
governance
In business intelligence, the methods for controlling and monitoring business health and promoting consistent decision making.
government to business (G2B)
Special case of the Business to Business and Business to Commerce e-commerce styles. See also government to consumer (G2C).
government to consumer (G2C)
Special case of the Business to Business and Business to Commerce e-commerce styles. See also government to business (G2B).
granularity
The level of detail represented by the values stored in a table’s row. Data stored at its lowest level of granularity is said to be atomic data.
graph database
A NoSQL database model based on graph theory that stores data on relationship-rich data as a collection of nodes and edges.
GROUP BY
A SQL clause used to create frequency distributions when combined with any of the aggregate functions in a SELECT statement.
H
hacker
A person who maliciously and illegally accesses a Web site with the intention of stealing data, chang- ing Web pages, or impairing Web site operations.
Hadoop
AJavabased,opensource,highspeed,fault-tolerant distributed storage and computational framework. Hadoop uses low-cost hardware to create clusters of thousands of computer nodes to store and process data.
Hadoop Distributed File System (HDFS)
A highly distributed, fault-tolerant file storage system designed to manage large amounts of data at high speeds.
hardware independence
A condition in which a model does not depend on the hardware used in the model’s implementation. Therefore, changes in the hardware will have no effect on the database design at the conceptual level.
hash index
An index based on an ordered list of hash values.
HAVING
A clause applied to the output of a GROUP BY operation to restrict selected rows.
heartbeat
In the Hadoop Distributed File System (HDFS), a signal sent every 3 seconds from the data node to the name node to notify the name node that the data node is still available.
heterogeneity transparency
A feature that allows a system to integrate several centralized DBMSs into one logical DDBMS.
heterogeneous DDBMSs
A system that integrates different types of centralized database management systems over a network.
hierarchical model
An early database model whose basic concepts and characteristics formed the basis for subsequent database development. This model is based on an upside-down tree structure in which each record
is called a segment. The top record is the root segment. Each segment has a 1:M relationship to the segment directly below it.
homogeneous DDBMSs
A system that integrates only one type of centralized database management system over a network.
homonyms
The use of the same name to label different attributes. Homonyms generally should be avoided. Some relational software automatically checks
for homonyms and either alerts the user to their existence or automatically makes the appropriate adjustments. See also synonym.
horizontal fragmentation
The distributed database design process that breaks a table into subsets of unique rows.
host language
Any language that contains embedded SQL statements.
hub
A warehouse of data packets housed in a central location on a local area network. It contains multiple ports that copy the data in the data packets to make it accessible to selected or all segments of the network.
hybrid object
The type of object classification that contains and represents a repeating group of attributes. One or more of the attributes reference another object that usually summarizes the contents of the hybrid object.
hyperlink
Link betweenWeb pages in hypertext or other electronic document types.
Hypertext Markup Language (HTML)
Standard document-formatting language for Web pages.
Hypertext Transfer Protocol (HTTP)
Standard protocol used by Web browser and Web server to communicate.
I
I/O accelerators
A device used to improve throughput for input/ output operations.
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identifiers
One or more attributes that uniquely identify each entity instance.
imaging server
A process that runs on a computer and provides image management services to client computers.
immediate update
See write-through technique.
implicit cursor
A cursor that is automatically created in procedural SQL when the SQL statement returns only one row.
IN
In SQL, a comparison operator used to check whether a value is among a list of specified values.
in-memory database
A database optimized to store large portions (if not all) of the database in primary (RAM) storage rather than secondary (disk) storage.
inconsistent retrievals
A concurrency control problem that arises when a transaction-calculating summary (aggregate) functions over a set of data while other transac- tions are updating the data, yielding erroneous results.
incremental backup
A process that only backs up data that has changed in the database since the last incremental or full backup.
index
An ordered array of index key values and row ID values (pointers). Indexes are generally used to speed up and facilitate data retrieval. Also known as an index key.
index key
See index.
index selectivity
A measure of how likely an index is to be used in query processing.
index-organized table
In a DBMS, a type of table storage organization that stores end-user data and index data in consecutive locations in permanent storage. Also known as cluster-indexed table.
information
The result of processing raw data to reveal its meaning. Information consists of transformed data and facilitates decision making.
Information Engineering (IE)
A methodology that translates a company’s strategic goals into helpful data and applications.
IE focuses on the description of corporate data instead of the processes.
information resource dictionary
Another name for data dictionary.
information resource manager (IRM)
See data administrator (DA).
information system
A system that provides for data collection, storage, and retrieval; facilitates the transformation of data into information; and manages both data and information. An information system is composed of hardware, the DBMS and other software, database(s), people, and procedures.
information systems (IS) department
A department responsible for all information technology services and production functions in an organization.
information systems
architecture (ISA)
The output of the information engineering (IE) process that serves as the basis for planning, devel- oping, and controlling future information systems.
Infrastructure as a Service (IaaS)
A model in which the cloud service provider offers consumers the ability to provision their own resources on demand; these resources include storage, servers, databases, processing units, and even a complete virtualized desktop.
inheritance
In the object-oriented data model, the ability of an object to inherit the data structure and methods of the classes above it in the class hierarchy. See also class hierarchy.
inner join
A join operation in which only rows that meet a given criterion are selected. The join criterion can be an equality condition (natural join or equijoin) or an inequality condition (theta join). The inner join is the most commonly used type of join. Contrast with outer join.
inner query
A query that is embedded or nested inside another query. Also known as a nested query or a subquery.
input/output (I/O) request
A low-level data access operation that reads or writes data to and from computer devices.
Insert (ISRT)
In hierarchical databases, a statement used to add a segment to the database.
INSERT
A SQL command that allows the insertion of one or more data rows into a table.
instance variables
In the object-oriented model, another term for an attribute. See attribute.
Institute of Electrical and Electronics Engineers (IEEE)
An organization that develops standards to provide uniformity among the technical details that define network topology and data transmission across shared media for networks.
Integrity
In a data security framework, refers to keeping data consistent and free of errors or anomalies. See also data integrity.
intelligent terminals
A device that provides enhanced I/O functions to a mainframe system such as a PC connected to a mainframe computer.
Interactive Database Processor (IDP)
In an IDS/II network DBMS, a processor that allows users to manipulate databases. The IDP front end is intended for users who have some programming knowledge and is not well-suited for most end users.
internal model
In database modeling, a level of data abstraction that adapts the conceptual model to a specific DBMS model for implementation. The internal model is the representation of a database as “seen” by the DBMS. In other words, the internal model requires a designer to match the conceptual model’s characteristics and constraints to those of the selected implementation model.
internal schema
A representation of an internal model using the data- base constructs supported by the chosen database.
International Organization for Standardization (ISO)
An organization formed to develop standards for diverse network systems.
Internet
A global network of computers connected together through a standard network protocol known as Transmission Control Protocol/Internet Protocol (TCP/IP). You can think of the Internet as the “high- way” on which the data travel. The terms Internet and World Wide Web are often used interchange- ably, but they are not synonyms.
Internetwork Packet Exchange/ Sequenced Packet Exchange (IPX/SPX)
A data communications protocol that determines how messages between computers are sent interpreted and processed.
interobject relationship
An attribute-class relationship created when an object’s attribute references another object of the same or a different class.
interprocess communication (IPC)
A capability supported by various operating systems to allow two processes to communicate with each other so that applications can share data without interfering with each other.
interrogate
To ask for the interrogated object’s instance variable value or values. An object may send messages to interrogate another object’s state.
INTERSECT
In relational algebra, an operator used to yield only the rows that are common to two union-compatible tables.
intrabusiness
A style of e-commerce that involves interactions internal to a company.
intranets
Company-owned and -operated computer net- works that are restricted to the company’s internal use. Such systems can only be accessed by the computers inside the company’s computer network. The purpose of such a system is to enhance company operations through improved data access management and communication.
IS NULL
In SQL, a comparison operator used to check whether an attribute has a value.
islands of information
In the old file system environment, pools of inde- pendent, often duplicated, and inconsistent data created and managed by different departments.
isolation
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Glossary 775 A database transaction property in which a data
item used by one transaction is not available to other transactions until the first one ends.
iterative process
A process based on repetition of steps and procedures.
J
Java
An object-oriented programming language developed by Sun Microsystems that runs on top of the web browser software. Java applications are compiled and stored on the web server. Java’s main advantage is its ability to let application developers create their applications once and then run them in many environments.
Java Database Connectivity (JDBC)
An application programming interface that allows a Java program to interact with a wide range of data sources, including relational databases, tabular data sources, spreadsheets, and text files.
JavaScript
A scripting language that allows web authors to design interactive websites. JavaScript code is embedded in webpages, and then downloaded with the page and activated when a specific event takes place, such as a mouse click on an object.
job tracker
A central control program used to accept, distribute, monitor, and report on MapReduce processing jobs in a Hadoop environment.
JOIN
In relational algebra, a type of operator used to yield rows from two tables based on criteria. There are many types of joins, such as natural join, theta join, equijoin, and outer join.
join columns
Columns that are used in the criteria of join opera- tions. The join columns generally share similar values (have a compatible domain).
JSON (JavaScript Object Nota- tion)
A human-readable text format for data interchange that defines attributes and values in a document.
K
key
One or more attributes that determine other attributes. See also superkey, candidate key, primary key (PK), secondary key, and foreign key.
key attribute
The attributes that form a primary key. See also prime attribute.
key performance indicators (KPIs)
In business intelligence, quantifiable numeric or scale-based measurements that assess a company’s effectiveness or success in reaching strategic and operational goals. Examples of KPIs are product turnovers, sales by promotion, sales by employee, and earnings per share.
key-value (KV) databases
A NoSQL database model that stores data as a collection of key-value pairs in which the value component is unintelligible to the DBMS.

776 Glossary key-value
A data model based on a structure composed of two data elements: a key and a value, in which every key has a corresponding value or set of values. The keyvalue data model is also called the associative or attribute-value data model.
knowledge
The body of information and facts about a specific subject. Knowledge implies familiarity, awareness, and understanding of information as it applies to an environment. A key characteristic is that new knowledge can be derived from old knowledge.
L
late binding
A characteristic in which the data type of an attri- bute is not known until execution time or run-time.
left outer join
In a pair of tables to be joined, a join that yields all the rows in the left table, including those that have no matching values in the other table. For example, a left outer join of CUSTOMER with AGENT will yield all of the CUSTOMER rows, including the ones that do not have a matching AGENT row. See also outer join and right outer join.
LIKE
In SQL, a comparison operator used to check wheth- er an attribute’s text value matches a specified string pattern.
linking table
In the relational model, a table that implements an M:M relationship. See also composite entity.
load testing
Services to ensure that an application will support the load imposed by having thousands of users access it.
local area network
A network of computers that spans a small area, such as a single building.
local mapping transparency
A property of a DDBMS in which database access requires the user to know both the name and location of the fragments.
LOCATION MODE clause
In an IDS/II network DBMS, this clause determines where a record will be (physically) stored in the database and how the record will be retrieved.
location transparency
A property of a DDBMS in which database access requires the user to know only the name of the database fragments. (Fragment locations need not be known.)
lock
A device that guarantees unique use of a data item in a particular transaction operation. A transaction requires a lock prior to data access; the lock is re- leased after the operation’s execution to enable other transactions to lock the data item for their own use.
lock granularity
The level of lock use. Locking can take place at the following levels: database, table, page, row, and field (attribute).
lock manager
A DBMS component that is responsible for assigning and releasing locks.
logical data format
The way a person views data within the context of a problem domain.
logical design
A stage in the design phase that matches the con- ceptual design to the requirements of the selected DBMS and is therefore software dependent. Logical design is used to translate the conceptual design into the internal model for a selected database management system, such as DB2, SQL Server, Oracle, IMS, Informix, Access, or Ingress.
logical independence
A condition in which the internal model can be changed without affecting the conceptual model. (The internal model is hardware independent because it is unaffected by the computer on which the software is installed. Therefore, a change in storage devices or operating systems will not affect the internal model.)
lost update
A concurrency control problem in which a data update is lost during the concurrent execution of transactions.
M
mandatory participation
A relationship in which one entity occurrence must have a corresponding occurrence in another entity. For example, an EMPLOYEE works in a DIVISION. (A person cannot be an employee without being assigned to a company’s division.)
manual query optimization
An operation mode that requires the end user or programmer to define the access path for the execution of a query.
manual statistical
generation mode
A mode of generating statistical data access information for query optimization. In this
mode, the DBA must periodically run a routine to generate the data access statistics—for example, running the RUNSTAT command in an IBM DB2 database.
many-to-many (M:N or *..*) relationship
Association among two or more entities in which one occurrence of an entity is associated with many occurrences of a related entity and one occurrence of the related entity is associated with many occurrences of the first entity.
map
The function in a MapReduce job that sorts and filters data into a set of key-value pairs as a subtask within a larger job.
mapper
A program that performs a map function.
MapReduce
An open-source application programming interface (API) that provides fast data analytics services;
one of the main Big Data technologies that allows organizations to process massive data stores.
master data management (MDM)
In business intelligence, a collection of concepts, techniques, and processes for the proper identifica- tion, definition, and management of data elements within an organization.
master data management (MDM) software
Software the provides a“master copy”of entities such as customers, that appear in numerous systems throughout the organization. This software helps prevent dirty data by coordinating common data across multiple systems.
materialized view
A dynamic table that not only contains the SQL query command to generate rows but stores the actual rows. The materialized view is created the first time the query is run and the summary rows are stored in the table. The materialized view rows are automatical- ly updated when the base tables are updated.
MAX
A SQL aggregate function that yields the maximum attribute value in a given column.
message
In the OO data model, the name of a method sent to an object in order to perform an action. A message triggers the object’s behavior. See method.
messaging
A service to ensure the proper routing and delivery of application-oriented data among multiple services.
metadata
Data about data; that is, data about data character- istics and relationships. See also data dictionary.
method
In the object-oriented data model, a named set of instructions to perform an action. Methods represent real-world actions, and are invoked through messages.
metrics
In a data warehouse, numeric facts that measure a business characteristic of interest to the end user.
metropolitan area network (MAN)
Network type used to connect computers across a city or metropolitan area.
Microsoft .NET framework
A component-based platform for the development of distributed, heterogeneous, interoperable applications aimed at manipulating any type of data over any network regardless of operating system and programming language.
middleware
The computer software that allows clients and servers to communicate within the client/server architecture. It is used to insulate client processes from the network protocols and the details of the server process protocols.
MIN
A SQL aggregate function that yields the minimum attribute value in a given column.
minimal data rule
Defined as“All that is needed is there, and all that is there is needed.”In other words, all data elements required by database transactions must be defined in the model, and all data elements defined in
the model must be used by at least one database transaction.
mixed fragmentation
A combination of horizontal and vertical strategies for data fragmentation, in which a table may be divided into several rows and each row has a subset of the attributes (columns).
module
(1) A design segment that can be implemented
as an autonomous unit, and is sometimes linked
to produce a system. (2) An information system component that handles a specific function, such as inventory, orders, or payroll.
module coupling
The extent to which modules are independent of one another.
monotonicity
A quality that ensures that time stamp values always increase. (The time stamping approach to scheduling concurrent transactions assigns a global, unique time stamp to each transaction. The time stamp value produces an explicit order in which transactions are submitted to the DBMS.)
multidimensional database management systems (MDBMSs) A database management system that uses propri- etary techniques to store data in matrixlike arrays of n dimensions known as cubes.
multidimensional online analytical processing (MOLAP)
An extension of online analytical processing to multidimensional database management systems.
multiple access unit (MAU)
A wiring concentrator through which a token ring’s computers are connected physically.
multiple inheritance
Exists when a class can have more than one immediate (parent) superclass above it in an object-oriented database environment.
multiple-site processing, multiple-site data (MPMD)
A scenario describing a fully distributed database management system with support for multiple data processors and transaction processors at multiple sites.
multiple-site processing, single-site data (MPSD)
A scenario in which multiple processes run on different computers sharing a single data repository.
multitenant database
A database environment in which a container database can hold other databases.
multiuser database
A database that supports multiple concurrent users.
multivalued attributes
An attribute that can have many values for a single entity occurrence. For example, an EMP_ DEGREE attribute might store the string“BBA, MBA, PHD”to indicate three different degrees held.
mutual consistency rule
A data replication rule that requires all copies of data fragments to be identical.
mutual exclusive rule
A condition in which only one transaction at a time can own an exclusive lock on the same object.
N
name node
One of three types of nodes used in the Hadoop Distributed File System (HDFS). The name node stores all the metadata about the file system. See also client node and data node.
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natural identifier
A generally accepted identifier for real-world objects. As its name implies, a natural key is familiar to end users and forms part of their day-to-day business vocabulary.
natural join
A relational operation that yields a new table composed of only the rows with common values in their common attribute(s).
natural key
See natural identifier.
nested query
In SQL, a query that is embedded in another query. See subquery.
network backbone
The main network cabling system for one or more local area networks.
Network Basic Input/Output System (NetBIOS)
A network protocol originally developed by IBM and SYTEK Corporation in 1984.
Network interface cards (NICs)
Electronic circuit board that allows computers to communicate within a network.
network latency
The delay imposed by the amount of time required for a data packet to make a round trip from point A to point B.
network model
An early data model that represented data as a collection of record types in 1:M relationships.
network operating system (NOS)
A computer operating system oriented
toward providing server side services to clients (such as file and printer sharing and security management).
network partitioning
The delay that occurs when nodes become suddenly unavailable due to a network failure. In distributed databases, the system must account for the possibility of this condition.
network protocol
A set of rules (at the physical level) that determines how messages between computers are sent, processed, and interpreted.
network segment
A single section of cable that connects several computers.
network translator
A middleware component that manages the network communications protocols.
news and discussion group services
Specialized services that allow the creation of “virtual communities”in which users exchange messages regarding specific topics.
NewSQL
A database model that attempts to provide ACID-compliant transactions across a highly distributed infrastructure.
node
In a graph database, the representation of a single entity instance.
non-identifying relationship
A relationship in which the primary key of the relat- ed entity does not contain a primary key component of the parent entity.
nonkey attribute
See nonprime attribute.
nonoverlapping subtypes
See disjoint subtype.
nonprime attribute
An attribute that is not part of a key.
nonrepeatable read
In transaction management, when a transaction reads a given row at time t1, then reads the same row at time t2, yielding different results because the original row may have been updated or deleted.
nonserialized items
Items for which the attributes describe a generalized view of the that kind of item, without identifying each individual instance of the item.
normalization
A process that assigns attributes to entities so that data redundancies are reduced or eliminated.
NoSQL
A new generation of database management sys- tems that is not based on the traditional relational database model.
NOT
A SQL logical operator that negates a given predicate.
null
The absence of an attribute value. Note that a null is not a blank.
O
object
An abstract representation of a real world entity that has a unique identity, embedded properties, and the ability to interact with other objects and itself.
object ID (OID)
In an object-oriented database environment,
a system-generated object identifier that is independent of the object state and any physical address in memory.
object instance
Each particular object belonging to a class.
Object Linking and Embedding for Database (OLE-DB)
Based on Microsoft’s Component Object Model (COM), OLE-DB is database middleware that adds object-oriented functionality for accessing relational and nonrelational data.
object orientation
A set of modeling and development principles focused on an autonomous entity with embedded intelligence to interact with other objects and itself.
object query language (OQL)
The database query language used by an object oriented database management system.
object schema
See Object space.
object space
The equivalent of the database schema, as seen by the designer in an object oriented database.
object state
The set of values that the object’s attributes have at a given time.
object table
The equivalent of a relational table composed of many rows, where each row is an object of the same type. Each row object has a unique system generated object ID (OID) or object identifier.
object-oriented data model (OODM)
A data model whose basic modeling structure is an object.
object-oriented database management system (OODBMS) Data management software used to manage data in an object-oriented database model.
object-oriented programming (OOP)
An alternative to conventional programming meth- ods based on object oriented concepts. It reduces programming time and lines of code, and increases programmers’ productivity.
object-oriented programming languages (OOPLs)
A programming language based on object oriented concepts.
object/relational database management system (O/R DBMS) A DBMS based on the extended relational model (ERDM). The ERDM, championed by many relational database researchers, constitutes the relational model’s response to the OODM. This model includes many of the object-oriented model’s best features within an inherently simpler relational database structure.
one-to-many (1:M or 1..*) relationship
Associations among two or more entities that are used by data models. In a 1:M relationship, one entity instance is associated with many instances of the related entity.
one-to-one (1:1 or 1..1) relationship
Associations among two or more entities that are used by data models. In a 1:1 relationship, one entity instance is associated with only one instance of the related entity.
online analytical processing (OLAP)
Decision support system (DSS) tools that use multidimensional data analysis techniques. OLAP creates an advanced data analysis environment that supports decision making, business modeling, and operations research.
online transaction processing (OLTP) database
See operational database.
Open Database Connectivity (ODBC)
Microsoft database middleware that provides a database access API to Windows applications.
Open Systems Interconnection (OSI)
A seven-layer reference model developed by the International Organization for Standardization (ISO) to help standardize diverse network systems.
Glossary 777 operational database
A database designed primarily to support a company’s day-to-day operations. Also known as a transactional database, OLTP database, or production database.
optimistic approach
In transaction management, a concurrency control technique based on the assumption that most database operations do not conflict.
optimizer hints
Special instructions for the query optimizer that are embedded inside the SQL command text.
optional attribute
In ER modeling, an attribute that does not require a value; therefore, it can be left empty.
optional participation
In ER modeling, a condition in which one entity occurrence does not require a corresponding entity occurrence in a particular relationship.
OR
The SQL logical operator used to link multiple condition- al expressions in a WHERE or HAVING clause. It requires only one of the conditional expressions to be true.
ORDER BY
A SQL clause that is useful for ordering the output of a SELECT query (for example, in ascending or descending order).
outer join
A relational algebra join operation that produces
a table in which all unmatched pairs are retained; unmatched values in the related table are left null. Contrast with inner join. See also left outer join and right outer join.
overlapping subtypes
In a specialization hierarchy, a condition in which each entity instance (row) of the supertype can appear in more than one subtype.
P
packet filter firewall
A type of firewall that works at the TCP/IP packet level.
page
In permanent storage, the equivalent of a disk block, which can be described as a directly addressable section of a disk. A diskpage has a fixed size, such as 4K, 8K, or 16K.
page-level lock
In this type of lock, the database management system locks an entire diskpage, or section of a disk. A diskpage can contain data for one or more rows and from one or more tables.
partial completeness
In a generalization/specialization hierarchy, a condition in which some supertype occurrences might not be members of any subtype.
partial dependency
A condition in which an attribute is dependent on only a portion (subset) of the primary key.
partially replicated database
A distributed database in which copies of only some database fragments are stored at multiple sites.
participants
An ER term for entities that participate in a relation- ship. For example, in the relationship “PROFESSOR
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778 Glossary
teaches CLASS,”the teaches relationship is based on
the participants PROFESSOR and CLASS.
partition key
In partitioned databases, one or more attributes in a table that determine the fragment in which a row will be stored.
partitioned data allocation
A data allocation strategy of dividing a database into two or more fragments that are stored at two or more sites.
partitioning
The process of splitting a table into subsets of rows or columns.
passive data dictionary
A DBMS data dictionary that requires a command initiated by an end user to update its data access statistics.
performance transparency
A DDBMS feature that allows a system to perform as though it were a centralized DBMS.
performance tuning
Activities that make a database perform more efficiently in terms of storage and access speed.
periodicity
Information about the time span of data stored in a table, usually expressed as current year only, previous years, or all years.
persistent stored module (PSM)
A block of code with standard SQL statements and procedural extensions that is stored and executed at the DBMS server.
personalization
In an e-commerce environment, customization of a Web page for individual users.
pessimistic locking
The use of locks based on the assumption that conflict between transactions is likely.
phantom read
In transaction management, when a transaction executes a query at time t1, then runs the same query at time t2, yielding additional rows that satisfy the query.
physical data format
The way a computer“sees”(stores) data.
physical design
A stage of database design that maps the data storage and access characteristics of a database. Because these characteristics are a function of the types of devices supported by the hardware, the data access methods supported by the system phys- ical design are both hardware- and software-de- pendent. See also physical model.
physical independence
A condition in which the physical model can be changed without affecting the internal model.
physical model
A model in which physical characteristics such as location, path, and format are described for the data. The physical model is both hardware- and software dependent. See also physical design.
Platform as a Service (PaaS)
A model in which the cloud service provider can build and deploy consumer-created applications using the provider’s cloud infrastructure.
plug-in
On the web, a client-side, external application that is automatically invoked by the browser when needed to manage specific types of data.
pluggable database
In a multitenant database environment, a database that can be contained within a container database.
policies
General statement of direction that is used to manage company operations through the communi- cation and support of the organization’s objectives.
polyglot persistence
The coexistence of a variety of data storage and data management technologies within an organization’s infrastructure.
polymorphism
An object oriented data model characteristic by which different objects can respond to the same message in different ways.
portals
In terms of business intelligence, a unified, single point of entry for information distribution.
predicate logic
Used extensively in mathematics to provide a framework in which an assertion (statement of fact) can be verified as either true or false.
predictive analytics
Data analytics that use advanced statistical and modeling techniques to predict future business outcomes with great accuracy.
Pretty Good Privacy (PGP)
An example of public-key encryption by Pretty Good Privacy Inc. PGP is a fairly popular and inexpensive method for encrypting e-mail messages on the Internet.
primary key (PK)
In the relational model, an identifier composed of one or more attributes that uniquely identifies a row. Also, a candidate key selected as a unique entity identifier. See also key.
prime attribute
A key attribute; that is, an attribute that is part of a key or is the whole key. See also key attributes.
privacy
The rights of individuals and organizations to determine access to data about themselves.
private cloud
A form of cloud computing in which an internal cloud is built by an organization to serve its own needs.
private key
A key that is known only to the owner of the key.
private-key encryption
Encryption that uses a single numeric key to encode and decode data. Both sender and receiver must know the encryption key. See also symmetric encryption.
Procedural Language SQL (PL/SQL)
An Oracle-specific programming language based on SQL with procedural extensions designed to run inside the Oracle database.
procedure cache
See SQL cache.
procedures
Series of steps to be followed during the perfor- mance of an activity or process.
processing option (PROCOPT)
A type of access granted to a program.
PRODUCT
In relational algebra, an operator used to yield all possible pairs of rows from two tables. Also known as the Cartesian product.
production database
See operational database.
profile
In Oracle, a named collection of settings that controls how much of the database resource a given user can use.
program communication
block (PCB)
In a hierarchical database, after the physical database has been defined through the DBD, a way through which application programs are given a subset of the physical database.
program specification block (PSB)
In a hierarchical database, this represents a logical view of a selected portion of the database and also defines the database(s), segments, and types of op- erations that can be performed by the application. Using PSBs yields better data security as well as improved program efficiency by allowing access to only the portion of the database that is required to perform a given function.
PROJECT
In relational algebra, an operator used to select a subset of columns.
properties
In a graph database, the attributes or characteristics of a node or edge that are of interest to the users.
protocol
A specific set of rules to accomplish a specific function. In the object oriented data model, protocol refers to a collection of messages to which an object responds.
proxy server firewall
A firewall that operates as an intermediary between client computers inside a private network and the Internet.
public cloud
A form of computing in which the cloud infrastruc- ture is built by a third-party organization to sell cloud services to the general public.
public key
A key that is available to anyone wanting to communicate securely with the key’s owner.
public-key encryption
A form of encryption that uses two numeric keys— the public key and the private key. Both keys are able to encrypt and decrypt each other’s messages. See also asymmetric encryption.
Q
query
A question or task asked by an end user of a database in the form of SQL code. A specific request for data manipulation issued by the end user or the application to the DBMS.
query language
A nonprocedural language that is used by a DBMS to manipulate its data. An example of a query language is SQL.
query optimizer
A DBMS process that analyzes SQL queries and finds the most efficient way to access the data. The query optimizer generates the access or execution plan for the query.
query processing bottleneck
In query optimization, a delay introduced in the processing of an I/O operation that causes the overall system to slow down.
query result set
The collection of data rows returned by a query.
R
RAID
An acronym for Redundant Array of Independent Disks. RAID systems use multiple disks to create virtual disks (storage volumes) from several individual disks. RAID systems provide performance improve- ment, fault tolerance, and a balance between the two.
read committed
An ANSI SQL transaction isolation level that allows transactions to read only committed data. This is the default mode of operations for most databases.
read uncommitted
An ANSI SQL transaction isolation level that allows transactions to read uncommitted data from other transactions, and which allows nonrepeatable reads and phantom reads. The least restrictive level defined by ANSI SQL.
record
A collection of related (logically connected) fields.
record at a time
This term indicates that the database commands affect a single record at a time.
RECORD NAME clause
In the IDS/II network DBMS, this clause initiates the record’s definition by assigning it a unique name. An IDS/II network database must contain at least one record type.
recursive query
A nested query that joins a table to itself.
recursive relationship
A relationship found within a single entity type. For example, an EMPLOYEE is married to an EMPLOYEE or a PART is a component of another PART.
reduce
The function in a MapReduce job that collects and summarizes the results of map functions to produce a single result.
reducer
A program that performs a reduce function.
redundant transaction logs
Multiple copies of the transaction log kept by database management systems to ensure that the physical failure of a disk will not impair the DBMS’s ability to recover data.
referential integrity
A condition by which a dependent table’s foreign key must have either a null entry or a matching entry in the related table.
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referential object sharing
When an object instance is referenced by other objects. That is, two or more different objects point to the same object instance, a change
in the referenced object instance values is automatically reflected in all other referring objects.
regular entity
See strong entity.
relation
A logical construct perceived to be a two dimen- sional structure composed of intersecting rows (entities) and columns (attributes) that represents an entity set in the relational model.
relational algebra
A set of mathematical principles that form the basis for manipulating relational table contents; the eight main functions are SELECT, PROJECT, JOIN, INTERSECT, UNION, DIFFERENCE, PRODUCT, and DIVIDE.
relational database management system (RDBMS)
A collection of programs that manages a relational database. The RDBMS software translates a user’s logical requests (queries) into commands that physically locate and retrieve the requested data.
relational diagram
A graphical representation of a relational database’s entities, the attributes within those entities, and the relationships among the entities.
relational model
Developed by E. F. Codd of IBM in 1970, the relational model is based on mathematical set theory and represents data as independent relations. Each relation (table) is conceptually represented as a two dimen- sional structure of intersecting rows and columns. The relations are related to each other through the sharing of common entity characteristics (values in columns).
relational online analytical processing (ROLAP)
Analytical processing functions that use relational databases and familiar relational query tools to store and analyze multidimensional data.
relational schema
The organization of a relational database as described by the database administrator.
relationship
An association between entities.
relationship degree
The number of entities or participants associated with a relationship. A relationship degree can be unary, binary, ternary, or higher.
relvar
Short for relation variable, a variable that holds a relation. A relvar is a container (variable) for holding relation data, not the relation itself.
Remote Data Objects (RDO)
A higher-level, object-oriented application interface used to access remote database servers. RDO uses the lower-level DAO and ODBC for direct access to databases.
remote request
A DDBMS feature that allows a single SQL statement to access data in a single remote DP.
roll up
(1) To aggregate data into summarized components, that is, higher levels of aggregation. (2) In SQL, an OLAP extension used with the GROUP BY clause
to aggregate data by different dimensions. Rolling up the data is the exact opposite of drilling down the data.
ROLLBACK
A SQL command that restores the database table contents to the condition that existed after the last COMMIT statement.
router
(1) An intelligent device used to connect dissimilar networks. (2) Hardware/software equipment that connects multiple and diverse networks.
row-centric storage
A physical data storage technique in which data is stored in blocks, which hold data from all columns of a given set of rows.
row-level lock
A less restrictive database lock in which the DBMS allows concurrent transactions to access different rows of the same table, even when the rows are on the same page.
row-level trigger
A trigger that is executed once for each row affected by the triggering SQL statement. A row-level trigger requires the use of the FOR EACH ROW keywords in the trigger declaration.
rule-based optimizer
A query optimization mode based on the rule- based query optimization algorithm.
rule-based query optimization algorithm
A query optimization technique that uses preset rules and points to determine the best approach to executing a query.
rules of precedence
Basic algebraic rules that specify the order in which operations are performed. For example, operations within parentheses are executed first, so in the equation 2 + (3 × 5), the multiplication portion is calculated first, making the correct answer 17.
S
scaling out
A method for dealing with data growth that involves distributing data storage structures across a cluster of commodity servers.
scaling up
A method for dealing with data growth that involves migrating the same structure to more powerful systems.
scheduler
The DBMS component that establishes the order
in which concurrent transaction operations are executed. The scheduler interleaves the execution of database operations in a specific sequence to ensure serializability.
schema
A logical grouping of database objects, such as tables, indexes, views, and queries, that are related to each other. Usually, a schema belongs to a single user or application.
scope
remote transaction
A DDBMS feature that allows a transaction (formed by several requests) to access data in a single remote DP.
repeatable read
An ANSI SQL transaction isolation level that uses shared locks to ensure that other transactions do not update a row after the original query updates it. However, phantom reads are allowed.
repeater
A device used in Ethernet networks to add network segments to the network and to extend its signal reach.
repeating group
In a relation, a characteristic describing a group of multiple entries of the same type for a single key attribute occurrence. For example, a car can have multiple colors for its top, interior, bottom, trim, and so on.
replica transparency
The DDBMS’s ability to hide the existence of multiple copies of data from the user.
replicated data allocation
A data allocation strategy in which copies of one or more database fragments are stored at several sites.
replication
The process of creating and managing duplicate versions of a database. Replication is used to place copies in different locations and to improve access time and fault tolerance.
required attribute
In ER modeling, an attribute that must have a value. In other words, it cannot be left empty.
reserved words
Words used by a system that cannot be used for any other purpose. For example, in Oracle SQL, the word INITIAL cannot be used to name tables or columns.
resource security
The protection of the resource(s) from external and internal threats. Specifically, resource security means protecting the resource from viruses, unauthorized access by hackers, or denial of service attacks.
RESTRICT
See SELECT.
right outer join
In a pair of tables to be joined, a join that yields all of the rows in the right table, including the ones with no matching values in the other table. For example, a right outer join of CUSTOMER with AGENT will yield all of the AGENT rows, including the ones that do not have a matching CUSTOMER row. See also left outer join and outer join.
ring topology
Network topology in which computers are connect- ed to one another via a cabling setup that, as the name implies, resembles a ring; it is more flexible than a bus topology, because addition or loss of a computer does not have a negative impact on other network activities.
role
In Oracle, a named collection of database access privileges that authorize a user to connect to a database and use its system resources.
Glossary 779 The part of a system that defines the extent of
the design, according to operational require- ments.
script
A programming language that is not compiled, but is interpreted and executed at run time.
search services
A business enabling web service that allows Web sites to perform searches on their contents.
second normal form (2NF)
The second stage in the normalization process, in which a relation is in 1NF and there are no partial dependencies (dependencies in only part of the primary key).
secondary key
A key used strictly for data retrieval purposes.
For example, customers are not likely to know
their customer number (primary key), but the combination of last name, first name, middle initial, and telephone number will probably match the appropriate table row. See also key.
Secure Electronic Transaction (SET)
Initiative to provide a standard for secure credit card transactions over the Internet.
Secure Hypertext Transfer Protocol (S-HTTP)
Protocol used to securely transfer Web docu- ments over the Internet. S-HTTP supports use of private and public keys for authentication and encryption. S-HTTP has not been widely used, because it only supports encrypted HTTP data and does not support other Internet protocols as does SSL.
Secure Sockets Layer (SSL)
A protocol used to implement secure communica- tion channels between client and server computers on the Internet.
security
Activities and measures to ensure the confidenti- ality, integrity, and availability of an information system and its main asset, data.
security breach
An event in which a security threat is exploited to endanger the integrity, confidentiality, or availability of the system.
security policy
A collection of standards, policies, and procedures created to guarantee the security of a system and ensure auditing and compliance.
security threat
An imminent security violation that could occur due to unchecked security vulnerabilities.
security vulnerability
A weakness in a system component that could be exploited to allow unauthorized access or cause service disruptions.
segment (SEGM)
In the hierarchical data model, the equivalent of a file system’s record type.
SELECT
(1) A SQL command that yields the values of all rows or a subset of rows in a table. The SELECT statement is used to retrieve data from tables.
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780 Glossary
(2) In relational algebra, an operator to select a
subset of rows. Also known as RESTRICT.
semantic data model
The first of a series of data models that more closely represented the real world, modeling both data and their relationships in a single structure known as an object. The SDM, published in 1981, was developed by M. Hammer and D. McLeod.
semistructured data
Data that has already been processed to some extent.
SENSEG (SENsitive SEGment)
In the IMS hierarchical database, this keyword de- clares the segments that will be available, starting with the root segment.
sentiment analysis
A method of text analysis that attempts to deter- mine if a statement conveys a positive, negative, or neutral attitude.
sequence
(1) A nested ordering sequence for a set of rows, such as a list in which all last names are alphabet- ically ordered and, within the last names, all first names are ordered. (2) An object for generating unique sequential values for a sequence field.
sequence field
An attribute that contains values that are unique and sequential (ascending or descending). Some DBMS allows the explicit definition of sequence or autonumber attributes that are generally used to uniquely identify each row.
serializability
A property in which the selected order of concurrent transaction operations creates the same final database state that would have been produced if the transactions had been executed in a serial fashion.
serializable
An ANSI SQL transaction isolation level that does not allow dirty reads, nonrepeatable reads, or phantom reads; the most restrictive level defined by the ANSI SQL standard.
serializable schedule
In transaction management, a schedule of operations in which the interleaved execution of the transactions yields the same result as if they were executed in serial order.
serialized items
Items for which each instance of the item must be tracked as an individually identifiable item.
server
Any process that provides requested services to clients. See client/server architecture.
server-side extension
A program that interacts directly with the server process to handle specific types of requests. Serv- er-side extensions add significant functionality to web servers and intranets.
set theory
A part of mathematical science that deals with sets, or groups of things, and is used as the basis for data manipulation in the relational model.
set-oriented
Dealing with or related to sets, or groups of things. In the relational model, SQL operators are
set-oriented because they operate over entire sets of rows and columns at once.
shared lock
A lock that is issued when a transaction requests permission to read data from a database and no exclusive locks are held on the data by another transaction. A shared lock allows other read-only transactions to access the database.
simple attribute
An attribute that cannot be subdivided into mean- ingful components. Compare to composite attribute.
simple object
An object that contains only single-valued attributes and has no attributes that refer to another object.
single inheritance
In the object oriented data model, the property of an object that allows it to have only one parent superclass from which it inherits its data structure and methods. See also inheritance, multiple inheritance.
single-site processing, single-site data (SPSD)
A scenario in which all processing is done on a single host computer and all data is stored on the host computer’s local disk.
single-user database
A database that supports only one user at a time.
single-valued attribute
An attribute that can have only one value.
site monitoring and data analysis
In an e-commerce environment, services to ensure that a Web site performs at an optimal level.
slice and dice
The ability to focus on slices of a data cube (drill down or roll up) to perform a more detailed analysis.
sneakernet
One of the original ways to share data. When users needed to share data, they would simply copy the data to a disk and walk to the coworker’s office, disk in hand.
snowflake schema
A type of star schema in which dimension tables can have their own dimension tables. The snowflake schema is usually the result of normalizing dimension tables.
social media
Web and mobile technologies that enable “any- where, anytime, always on”human interactions.
Software as a Service (SaaS)
A model in which the cloud service provider offers turnkey applications that run in the cloud.
software independence
A property of any model or application that does not depend on the software used to implement it.
sparse data
A case in which the number of table attributes is very large but the number of actual data instances is low.
sparsity
In multidimensional data analysis, a measurement of the data density held in the data cube.
specialization
In a specialization hierarchy, the grouping of unique attributes into a subtype entity.
specialization hierarchy
A hierarchy based on the top-down process of iden- tifying lower-level, more specific entity subtypes from a higher-level entity supertype. Specialization is based on grouping unique characteristics and relationships of the subtypes.
SQL cache
A shared, reserved memory area that stores the most recently executed SQL statements or PL/SQL procedures, including triggers and functions. Also called procedure cache.
SQL data services (SDS)
Data management services that provide relational data storage, access, and management over the Internet.
SQL performance tuning
Activities to help generate a SQL query that returns the correct answer in the least amount of time, using the minimum amount of resources at the server end.
standards
A detailed and specific set of instructions that describes the minimum requirements for a given activity. Standards are used to evaluate the quality of the output.
star schema
A data modeling technique used to map multidi- mensional decision support data into a relational database. The star schema represents data using a central table known as a fact table in a 1:M relation- ship with one or more dimension tables.
star topology
Network topology with all computers connected to one another in a star configuration through a central computer or network hub. Like a ring topology, allows for computers to be added to or released from the network without having an impact on other computers.
state inspection firewall
A type of firewall that compares parts of incoming packets and related outgoing packets.
stateless system
A system in which a web server does not know
the status of the clients communicating with it. The web does not reserve memory to maintain an open communications state between the client and the server.
statement-level trigger
A SQL trigger that is assumed if the FOR EACH ROW keywords are omitted. This type of trigger is executed once, before or after the triggering statement completes, and is the default case.
static query optimization
A query optimization mode in which the access path to a database is predetermined at compilation time.
static SQL
A style of embedded SQL in which the SQL statements do not change while the application is running.
static webpage
Web page used to display information that does not change much over time or is not time-critical.
statistically based query optimi- zation algorithm
A query optimization technique that uses statistical information about a database. The DBMS then uses these statistics to determine the best access strategy.
stored function
A named group of procedural and SQL statements that returns a value, as indicated by a RETURN statement in its program code.
stored procedure
(1) A named collection of procedural and SQL statements. (2) Business logic stored on a server in the form of SQL code or another DBMS-specific procedural language.
stream processing
The processing of data inputs in order to make decisions about which data to keep and which data to discard before storage.
strong (identifying) relationship
A relationship that occurs when two entities are existencedependent; from a database design perspective, this relationship exists whenever the primary key of the related entity contains the primary key of the parent entity.
strong entity
An entity that is existence-independent, that is, it can exist apart from all of its related entities. Also called a regular entity.
structural dependence
A data characteristic in which a change in the database schema affects data access, thus requiring changes in all access programs.
structural independence
A data characteristic in which changes in the database schema do not affect data access.
structured data
Data that has been formatted to facilitate storage, use, and information generation.
Structured Query Language
(SQL)
A powerful and flexible relational database language composed of commands that enable users to create database and table structures, perform various types of data manipulation and data administration, and query the database to extract useful information.
subclasses
See class hierarchy.
subordinates
In a DDBMS, a data processor (DP) node that participates in a distributed transaction using the two-phase COMMIT protocol.
subquery
A query that is embedded (or nested) inside another query. Also known as a nested query or an inner query.
subschema
The portion of the database that interacts with application programs.
subtype discriminator
The attribute in the supertype entity that determines to which entity subtype each supertype occurrence is related.
SUM
A SQL aggregate function that yields the sum of all values for a given column or expression.
super column
In a column family database, a column that is composed of a group of other related columns.
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superclass
In a class hierarchy, the superclass is the more general classification from which the subclasses inherit data structures and behaviors.
superkey
An attribute or attributes that uniquely identify each entity in a table. See key.
surrogate key
A system-assigned primary key, generally numeric and autoincremented.
switch
An intelligent device that connects computers. Unlike a hub, a switch allows multiple simultaneous transmissions between two ports (computers). Therefore, switches have greater throughput and speed than regular hubs.
symmetric encryption
Encryption that uses a single numeric key to encode and decode data. Both sender and receiver must know the encryption key. See also private-key encryption.
synonym
The use of different names to identify the same object, such as an entity, an attribute, or a relation- ship; synonyms should generally be avoided. See also homonym.
system catalog
A detailed system data dictionary that describes all objects in a database.
systems administrator
The person responsible for coordinating and performing day-to-day data-processing activities.
systems analysis
The process that establishes the need for an information system and its extent.
systems development
The process of creating an information system.
Systems Development Life Cycle (SDLC)
The cycle that traces the history of an information system. The SDLC provides the big picture within which database design and application development can be mapped out and evaluated.
Systems Network Architecture (SNA)
A network environment used by IBM mainframe computers
T
table
A logical construct perceived to be a two dimen- sional structure composed of intersecting rows (entities) and columns (attributes) that represents an entity set in the relational model.
table space
In a DBMS, a logical storage space used to group related data. Also known as a file group.
table-level lock
A locking scheme that allows only one transaction at a time to access a table. A table-level lock locks an entire table, preventing access to any row by transaction T2 while transaction T1 is using the table.
tablespace
In a DBMS, a logical storage space used to group related data. Also known as a file group.
tags
In markup languages such as HTML and XML, a command inserted in a document to specify how the document should be formatted. Tags are used in server-side markup languages and interpreted by a web browser for presenting data.
task trackers
A program in the MapReduce framework responsi- ble to running map and reduce tasks on a node.
ternary relationship
An ER term used to describe an association (relationship) between three entities. For example, a DOCTOR prescribes a DRUG for a PATIENT.
theta join
A join operator that links tables using an inequality comparison operator (<, >, <=, >=) in the join condition.
thin client
A client that carries a relatively smaller proportion of the processing load than compared to the server, thin clients are always paired with fat servers.
thin server
A server that carries a lesser processing load than the client processes, thin servers are always paired with fat clients.
third normal form (3NF)
A table is in 3NF when it is in 2NF and no nonkey attribute is functionally dependent on another nonkey attribute; that is, it cannot include transitive dependencies.
three-tier client/server system
A system design where the client’s requests are handled by intermediate servers, which coordinate the execution of the client requests with subordinate servers. See also client/server.
3Vs
Three basic characteristics of Big Data databases: volume, velocity, and variety.
time stamping
In transaction management, a technique used in scheduling concurrent transactions that assigns a global unique time stamp to each transaction.
time-variant data
Data whose values are a function of time. For example, timevariant data can be seen at work when a company’s history of all administrative appointments is tracked.
token
In a ring topology network, the marker that passes from computer to computer, similar to a baton in a relay race. Only the computer with the token can transmit at a given time.
token ring networks
Networks that use a ring topology and token passing access control.
top-down design
A design philosophy that begins by defining the main structures of a system and then moves to define the smaller units within those structures. In database design, this process first identifies entities and then defines the attributes within the entities.
total completeness
In a generalization/specialization hierarchy, a condition in which every supertype occurrence must be a member of at least one subtype.
transaction
A sequence of database requests that accesses the database. A transaction is a logical unit of work; that is, it must be entirely completed or aborted—no intermediate ending states are accepted. All transactions must have the properties of atomicity, consistency, isolation, and durability.
transaction log
A feature used by the DBMS to keep track of all transaction operations that update the database. The information stored in this log is used by the DBMS for recovery purposes.
transaction log backup
A backup of only the transaction log operations that are not reflected in a previous backup copy of the database.
transaction manager (TM)
See transaction processor (TP).
transaction processing
In electronic-commerce, this is the series of actions, changes, and/or functions among thousands of connected customers.
transaction processor (TP)
In a DDBMS, the software component on each computer that requests data. The TP is responsible for the execution and coordination of all database requests issued by a local application that accesses data on any DP. Also called transaction manager (TM) or application processor (AP).
transaction transparency
A DDBMS property that ensures database transactions will maintain the distributed database’s integrity and consistency, and that a transaction will be completed only when all database sites involved complete their part of the transaction.
transactional database
See operational database.
transitive dependency
A condition in which an attribute is dependent on another attribute that is not part of the primary key.
Transmission Control Protocol/ Internet Protocol (TCP/IP)
The official communications protocol of the Internet, a worldwide network of heterogeneous computer systems.
transparent
Indicating that the user is unaware of the system’s operations.
Transport Layer Security (TLS)
An updated version of Secure Sockets Layer (SSL) that supports more secure encrypted communica- tion between a client and server on the Internet.
traversal
A query in a graph database.
trigger
A procedural SQL code that is automatically invoked by the relational database management system when a data manipulation event occurs.
tuple
In the relational model, a table row.
Glossary 781 twisted pair cable
Network cable formed by pairs of wires that are twisted inside a protective insulating cover; choice cabling for most network installations because
it is easy to install and carries a low price tag. It resembles typical telephone cable.
two-phase commit protocol (2PC)
In a DDBMS, an algorithm used to ensure atomicity of transactions and database consistency as well as integrity in distributed transactions.
two-phase locking (2PL)
A set of rules that governs how transactions acquire and relinquish locks. Two-phase locking guarantees serializability, but it does not prevent deadlocks. The two-phase locking protocol is divided into two phases: (1) A growing phase occurs when the transaction acquires the locks it needs without unlocking any existing data locks. Once all locks have been acquired, the transaction is in its locked point. (2) A shrinking phase occurs when the transaction releases all locks and cannot obtain a new lock.
two-tier client/server system
A system within which a client requests services directly from the server. See also client/server.
U
unary relationship
An ER term used to describe an association
within an entity. For example, an EMPLOYEE might manage another EMPLOYEE.
uncommitted data
A concurrency control problem in which a trans- action accesses uncommitted data from another transaction.
unidirectional logical relationships
In a hierarchical database, relationships that are established by linking a logical child with a logical parent in a one-way arrangement.
Unified Modeling Language (UML)
A language based on object-oriented concepts that provides tools such as diagrams and symbols to graphically model a system.
Uniform Resource Locator (URL) or web address
Identifier for a resource on the Internet. Also called Web address.
UNION
In relational algebra, an operator used to merge (append) two tables into a new table, dropping the duplicate rows. The tables must be union-com- patible.
union-compatible
Two or more tables that have the same number of columns and the corresponding columns have compatible domains.
unique fragment
In a DDBMS, a condition in which each row is unique, regardless of which fragment it is located in.
unique index
An index in which the index key can have only one associated pointer value (row).
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

782 Glossary uniqueness
In concurrency control, a property of time stamping that ensures no equal time stamp values can exist.
Universal Data Access (UDA)
Within the Microsoft application framework, a collection of technologies used to access any type of data source and to manage the data through a common interface.
unreplicated database
A distributed database in which each database fragment is stored at a single site.
unstructured data
Data that exists in its original, raw state; that is, in the format in which it was collected.
updatable view
A view that can update attributes in base tables that are used in the view.
UPDATE
A SQL command that allows attribute values to be changed in one or more rows of a table.
usability testing
Testing performed to evaluate the degree to which a user interface is considered friendly and easy to use.
user
In a system, a uniquely identifiable object that allows a given person or process to log on to the database.
UWA
In the Network Database Model, a specific area of memory that contains several fields used to access and inform regarding the status of the database. The UWA also contains space for each record type defined in the subschema.
V
value
The degree to which data can be analyzed to provide meaningful insights.
value chain
All activities required to design, plan, manufacture, market, sell, and support a product or service.
variability
The characteristic of Big Data for the same data values to vary in meaning over time.
variety
A characteristic of Big Data that describes the variations in the structure of data to be stored.
VBScript
A Microsoft client-side extension that extends a browser’s functionality; VBScript is derived from Visual Basic.
velocity
A characteristic of Big Data that describes the speed at which data enters the system and must be processed.
veracity
The trustworthiness of a set of data.
verification
The process of refining a conceptual data model into a detailed design that is capable of supporting all required database transactions, and input and output requirements.
versioning
A property of an OODBMS that allows the database to keep track of the different transformations performed on an object.
vertical fragmentation
In distributed database design, the process that breaks a table into a subset of columns from the original table. Fragments must share a common primary key.
very large databases (VLDBs)
Database that contains huge amounts of data— gigabyte, terabyte, and petabyte ranges are not unusual.
view
A virtual table based on a SELECT query that is saved as an object in the database.
virtualization
A technique that creates logical representations of computing resources that are independent of the underlying physical computing resources.
virus
A malicious program that affects the normal operation of a computer system.
visualization
The ability to graphically present data in such a way as to make it understandable to users.
volume
A characteristic of Big Data that describes the quantity of data to be stored.
W
wait/die
A concurrency control scheme in which an older transaction must wait for the younger transaction to complete and release the locks before requesting the locks itself. Otherwise, the newer transaction dies and is rescheduled.
weak entity
An entity that displays existence dependence and inherits the primary key of its parent entity.
For example, a DEPENDENT requires the existence of an EMPLOYEE.
weak relationship
A relationship in which the primary key of the relat- ed entity does not contain a primary key component of the parent entity.
web application server
A middleware application that expands the functionality of web servers by linking them to a wide range of services, such as databases, directory systems, and search engines.
web browser
The end-user application used to navigate the Internet; runs on a client computer and requests services from a Web server.
web caching
Performance-enhancing technique in which a temporary storage area is created to provide Web pages at an optimal speed.
web development
The process of adding “business logic” to Web pages, thereby making them business-enabled.
web server
A specialized application whose only function is to “listen”for client requests, process them, and send the requested Web resource back to the client browser.
web-to-database middleware
A database server-side extension that retrieves data from databases and passes them to the web server, which in turn sends the data to the client’s browser for display.
webpage
A text document on the World Wide Web containing text and special commands written in Hypertext Markup Language.
website
Refers to the Web server and the collection of Web pages stored on the local hard disk of the server computer.
WHERE
A SQL clause that adds conditional restrictions to a SELECT statement that limit the rows returned by the query.
wide area network (WAN)
Network type used to connect computer users across distant geographical areas; generally makes use of telephone or specialized communications companies.
wildcard character
A symbol that can be used as a general substitute for: (1) all columns in a table (*) when used in an attribute list of a SELECT statement or, (2) zero or more charac- ters in a SQL LIKE clause condition ( % and _ ).
wireless adapter
In the case of wireless networks, this adapter, sometimes called a wireless NIC, allows a computer to communicate using a wireless network.
wireless device support
Services that facilitate data integration with mobile communication devices, such as smart phones; allow users to conduct business, make hotel reservations, purchase groceries, and pay bills from virtually anywhere.
wireless LANs (WLANs)
Local area networks that are connected by wireless technology rather than wires.
workgroup database
A multiuser database that usually supports fewer than 50 users or is used for a specific department in an organization.
World Wide Web
(WWW or the web)
Worldwide network collection of specially format- ted and interconnected documents known as Web pages. Also called the Web.
wound/wait
A concurrency control scheme in which an older transaction can request the lock, preempt the younger transaction, and reschedule it. Otherwise, the newer transaction waits until the older transaction finishes.
write-ahead protocol
A protocol that ensures transaction logs are written to permanent storage before any database data is actually updated.
write-ahead-log protocol
In concurrency control, a process that ensures transaction logs are written to permanent storage before any database data is actually updated. Also called a write-ahead protocol.
write-through technique
In concurrency control, a process that ensures a database is immediately updated by operations during the transaction’s execution, even before the transaction reaches its commit point. Also called immediate update.
X
XML database
A database system that stores and manages semistructured XML data.
XML schema
An advanced data definition language used to de- scribe the elements, data types, relationship types, ranges, and default values of XML data documents. One of the main advantages of an XML schema is that it more closely maps to database terminology and features.
XML schema definition (XSD)
A file that contains the description of an XML document.
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

INDEX
Note
Page numbers in boldface indicate key terms.
Symbols
asterisk (*), 704
colon (:), 410, 412 comma (,), 257
dash (-), 401
dollar sign ($), 701 forward slash (/), 390 Greek letter pi (π), 84 Greek letter sigma (σ), 83 Infinity symbol (∞), 44 parentheses (), 257 percent sign (%), 280 plus symbol (+), 704 question mark (?), 704 semicolon (;), 391, 394 underscore (_), 280
Numbers
1:1 relationship. See one-to-one (1:1) relationship
1:M relationship. See one-to-many (1:M) relationship
1NF. See first normal form 2NF. See second normal form 2PC. See two-phase commit
protocol
2PL. See two-phase locking 3NF. See third normal form
3 Vs. See volume, velocity, and
variety
4NF. See fourth normal form 5NF. See fifth normal form
A
ABS function, 366
access plan, 524, 542, 741
access rights, 454
ACID properties, 582
active data dictionary, 750 ActiveX, 697
ActiveX Data Objects (ADO), 687 adaptive maintenance, 445 ADDDATE function, 365 ADD_MONTHS function, 364
ad hoc query, 7
ADO. See ActiveX Data Objects ADO.NET, 687, 687–690 AGENT file, 16, 20–21
aggregate functions, 292–297 Agile Software Development, 441 algorithm, 653
alias
definition, 275
joining tables with, 303
ALL multirow operator, 353–355 ALTER TABLE command, 247,
283, 283–284 Amazon AWS, 8
American National Standards Institute (ANSI) Standards, 59, 248, 488, 504–505
analytical database, 9
AND logical operator, 277,
277–278
anonymous PL/SQL block, 390 ANY multirow operator, 353–355 AP. See application processor
API. See application programming
interface
application code, 525
application interface, 559 application processor (AP), 560 application programming interface
(API), 683, 695
arithmetic operators, 276–277 ASSIGNMENT table, 212, 216 associative entities, 98, 138–140 atomic attribute, 216
atomicity, 216, 487
atomic transaction property, 506 AT&T, 4
attribute(s)
atomic, 216
composite, 120–121 cursor, 408
definition, 38
derived, 123–124 description, 118–119 discriminator, 175 domains, 119 identifiers, 119–120 key, 77, 202
list subqueries, 356–358 multivalued, 121–123 nonkey, 202
nonprime, 202 optional, 119
prime, 202
required, 119
simple, 121 single-valued, 121
audit log, 749
audit trails, 454
authentication, 251
authorization management, 748 automated data mining, 673 automatic query optimization, 520 availability, 581, 746
AVG function, 296, 296–297
B
back-end CASE tools, 752 backup
concurrent, 737 differential, 455 full, 455, 737 incremental, 737 transaction log, 455
BASE. See basically available, soft state, eventually consistent
base tables, 377
basically available, soft state,
eventually consistent (BASE),
582
batch processing, 660 batch update routine, 379
BCNF. See Boyce-Codd normal form BETWEEN special operator, 279,
279–280
BI. See business intelligence Big Data
business intelligence, 601 characteristics, 649–650 current view of, 650 databases, 29
data models and, 50–53 definition, 51
NoSQL in, 53
original view of, 649 polyglot persistence, 655 sentiment analysis, 654 value, 654
variability, 654 variety, 649, 653–654 velocity, 649, 652–653 visualization, 654 volume, 649, 651–652
binary JavaScript Object Notation (BSON), 664
binary lock, 498, 498–499
binary relationship, 134, 134–135 bitmap index, 527
block report, 657
Boolean algebra, 278
bottom-up design, 473, 473–474 boundaries, 450
Boyce-Codd normal form (BCNF)
characteristics, 207 conversion to, 221–224 definition, 221
bridge entity, 98
BSON. See binary JavaScript Object
Notation B-tree index, 527
bucket, 663
buffer cache, 519 buffers, 506
business intelligence (BI)
architecture, 592–597 benefits, 598
components, 593
definition, 9, 590
evolution, 598–601 framework, 591–592 reporting styles, 597
solving problems and adding
values, 591
technology trends, 601–602 tools, 594
business rule
definition, 39
discovering, 39–40 examples of, 39
naming conventions, 41 translating into data model,
40–41
C
call level interface (CLI), 683 candidate key, 78 cardinality, 125, 125–126
cascading order sequence, 291 CASE. See computer-aided software
engineering
CAST function, 369, 370
CEIL function, 366
CEILING function, 366 centralized data allocation, 580 centralized databases, 8 centralized design, 474, 474–476 central processing unit (CPU)
conditional expression, 533 performance, 517
query processing, 525
CGI. See Common Gateway Interface
character data types, 254 CHAR data type, 392 CHECK command, 247 checkpoints, 507
Chen notation
definition, 46
derived attributes, 123 description, 118–119 multivalued attributes, 121–122 weak entities, 129–130
class
definition, 48 diagram, 49
diagram notation, 46 hierarchy, 48
CLI. See call level interface client node, 53, 657 client-side extensions
common forms of, 697–698
definition, 697 CLOSE command, 407 closure, 83
cloud computing
data architect, 29
in database administrators,
756–757
definition, 709 description, 709–712 implementation types, 712 SQL data services, 716
cloud databases, 8, 30 cloud services
advantages/disadvantages of, 714–716
definition, 710
types of, 713–714 clustered index table, 538 clustered table, 472 Codd, E. F., 74, 104–105 cohesivity, 466
cohorts, 572
column(s)
adding, 284–285
alias, 274–275 computed, 274–275
data characteristics, 284 data types, 284 dropping, 285
column-centric storage, 665 column family, 667
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Index 783

784 Index
column family database, 666
column-oriented databases, 665–667
“commandments,” for distributed databases, 583
commands (listed by name) ALTER TABLE command, 247,
283, 283–284
CHECK command, 247
CLOSE command, 407 COMMIT command, 248, 266 CREATE INDEX command, 247 CREATE SCHEMA
AUTHORIZATION
command, 247, 251–252 CREATE TABLE AS command,
247
CREATE TABLE command,
247, 255
CREATE VIEW command,
247, 378
DEFAULT command, 247 DELETE command, 248, 269 DROP INDEX command, 247,
264
DROP SEQUENCE command, 387
DROP TABLE command, 247,
290
DROP VIEW command, 247 FETCH command, 407 FOREIGN KEY command, 247 INSERT command, 248, 264,
270–271
NOT NULL command, 247 OPEN command, 407 PRIMARY KEY command, 247 ROLLBACK command, 248, 269 SELECT command, 248, 266 UNIQUE command, 247 UPDATE command, 248, 268
COMMIT command, 248, 266, 488 Common Gateway Interface (CGI),
695
communications media, 560 community cloud, 712 comparison operators
on character attributes, 273–274 on dates, 274
symbols, 273
completeness constraint, 175, 175–176
compliance, 745
composite attributes, 120, 121 composite entity, 98, 138–140 composite identifiers, 119–120, 120 composite key, 77
composite primary keys, 178–180 computer-aided software
engineering (CASE), 445, 752,
752–755
computerized file systems, 15–17 computer workstations, 560 conceptual design
data analysis and requirements, 459–461
data model verification, 464–467 definition, 457
description, 457–458
distributed database design, 467 entity relationship modeling and
normalization, 461–464 steps, 458
conceptual model advantages, 61–62 definition, 61
conceptual schema, 61 concurrency control
in DDBMS, 559
definition, 490
distributed, 571 inconsistent retrievals, 492,
492–493
lost update, 490, 490–491 optimistic approach, 503,
503–504
scheduler, 493–495, 494 time stamping, 502, 502–503 uncommitted data, 491,
491–492
concurrent backup, 737 conditional criteria, 533–534 conditional expression, 533–534 confidentiality, 745
connectivity, 46, 125, 125–126 consistency, 487, 581 consistency, availability, partition
tolerance (CAP) theorem,
581–582
consistent database state, 484 constraints
completeness, 175, 175–176 definition, 38
disjoint, 174–175
integrity, 470–471 overlapping, 174–175
SQL, 259–262
conversion functions, 368–370 CONVERT function, 362, 370 coordinator, 572
corporate database, 726 corrective maintenance, 445 correlated subquery, 358,
358–361
cost-based optimizer, 528–529 COUNT function, 293, 293–295 CPU. See central processing unit CREATE SCHEMA
AUTHORIZATION
command, 247, 251–252 CREATE TABLE AS command, 247 CREATE TABLE command, 247,
255
definition, 247, 255 MySQL, 257 Oracle, 258–259
CREATE VIEW command, 247,
378
cross join, 342
Crow’s Foot notation
associative entity, 139 cardinalities, 125
definition, 46
derived attributes, 123 mandatory participation, 131 multivalued attributes, 121–122
strong relationship, 128 symbols, 132
weak entities, 129–130 weak relationship, 127
cube cache, 628 cursor
attributes, 408
commands, 407
definition, 407
explicit, 407
implicit, 407
PL/SQL processing with, 407–409
D
DA. See data administrator DAO. See data access objects dashboards, 594
data
analysis, need for, 590
cache, 519, 536
as corporate asset, 723–724 cube, 628
database system component, 24 definition, 4, 15
dependence, 19
derived, 231
dirty, 724
distribution, 574
encryption, 454
file, 518, 760
grouping, 297–300 inconsistency, 7
independence, 19
versus information, 4–6 integrity, 20
logical view of, 73–76 management, 6
metadata, 6
modeling, 36
node, 52
nodes, 657
preaggregated, 231
quality, 8, 724
redundant, 231
sparse, 55
sparsity, 527
structured, 653
uncommitted, 491, 491–492 unstructured, 653 visualization, 596, 596–597
data abstraction
ANSI Standards, 59 conceptual model, 61, 61–62 description of, 57, 59 external model, 60, 60–61 internal model, 62, 62–63 levels, 63
physical model, 63
data access objects (DAO), 683, 683–685
DataAdapter object, 690 data administrator (DA)
versus database administrators, 731–732
definition, 730 data allocation
algorithms, 581 centralized, 580
definition, 580 partitioned, 580 replicated, 580
data analytics
data mining, 671, 671–673 definition, 670 explanatory analytics, 670,
670–671
predictive analytics, 670,
673–674 data anomaly
definition, 21 deletion, 21 insertion, 21 update, 21
database(s)
administrator, 29
analyst, 29
architect, 29
Big Data, 29
consultant, 29
corporate, 726 definition, 6
description, 2
designer, 23, 29 developer, 29 development, 441
dump, 455, 737 enterprise, 726
versus file systems, 22 fine-tuning, 454–455 fragment, 467, 557 in-memory, 30
instance, 760 introduction to, 6–11 middleware, 681
need for, 724–726 performance tuning, 516,
516–517
pervasive nature of, 3 professional careers, preparing
for, 28–30
request, 484
reasons for using, 3–4 recovery, 505
role, 472
role in organization,
724–726
security officer, 29
sources of failure, 456 spreadsheets and, comparison
of, 27 types of, 8–11 XML, 709
database administration evolution of, 727–730 human component, 731–745 Oracle for, 757–765
strategy, developing, 755–756
tools, 749–755
database administrators (DBAs), 23,
518–519
activities of, 732 CASE tools, 752–755 in cloud computing,
756–757
versus data administrators,
731–732
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

data backup and recovery, 736–737
in Database Life Cycle, 729 data dictionary, 750–752
data distribution and use, 738 data security, privacy, and
integrity, 736
DBMS technology, 728–729,
738–745
definition, 728
end-user support, 734 functional organization, 730 managerial role of, 733–738 policies, procedures, and
standards, 734–736 technical role of, 738–745 training and supporting users,
744
database connectivity. See also
Internet; network broad layers, 681 definition, 681 fundamentals of, 681 interfaces, 682–692 native, 682–683
database design
bottom-up design, 473, 473–474 centralized design, 474,
474–476
conflicting goals, 147–152 decentralized design, 474,
474–476
definition, 11
distributed, 467
importance of, 11–14 improvements in normalization,
215–219
poor and good design, 12–13 strategies, 473–474
top-down design, 473, 473–474
database design cases
fan trap, 186, 186–187 implementing 1:1 relationships,
182–183
redundant relationships,
187–188
time-variant data, 183, 183–186
database design challenges design standards, 148 information requirements,
149–150
processing speed, 148–149
database-level lock, 497 Database Life Cycle (DBLC)
analysis, 446–447
definition, 445 implementation and loading,
451–454
initial study, 445–450 maintenance and evolution, 457 objectives, 449
operational phase, 456–457 problems and constraints,
447–449
procedures, 450–451
scope and boundaries, 449,
449–450, 450
testing and evaluation, 454–456
database management system (DBMS)
access languages and application programming interfaces, 27
advantages of, 6–8 architecture, 518–520 backup and recovery
management, 27 communication interfaces, 27 cultural impact of, 727 database security, 749 definition, 6
dictionary management, 24–25 end user and database
interaction, 7
functions of, 24–27
integrity management, 27 management levels, 725–726 managerial aspect of, 727 multiuser access control, 26–27 performance tuning, 517, 517–
518, 536–538
role of, 6–8
security management, 26 software selection, 467 storage management, 25 technological aspect of, 726 transformation and
presentation, 26 database security
in DBMS, 749
definition, 748
user access management,
748–749
view definition, 749
database security officer (DSO), 736 database statistics
definition, 521 description, 521–522 measurements, 521
database systems components, 22–24 definition, 22 description of, 21–22 environment, 22–24 management of, 28
database table(s) joining, 300–304 linking table, 99
data definition language (DDL), 42, 247
data dictionary
active, 750
in database administrators,
750–752 definition, 25, 91 passive, 750
sample, 92
system analog, 91–93
data fragmentation
definition, 575
horizontal fragmentation, 575,
576
mixed fragmentation, 575,
577–578
strategies, 575–578
vertical fragmentation, 575,
576–577
data manager (DM), 560
data manipulation language (DML),
42, 247 data mart, 610
data mining
data analysis and classification
phase, 672–673
data preparation phase, 672–673 definition, 671
knowledge acquisition phase,
672–673
modes of, 673
prognosis phase, 672–673
data model(s)
advantages and disadvantages,
58
basic building blocks, 37–38
Big Data, 50–53
definition, 36
entity relationship model, 45–48 evolution of, 41–57
hierarchical model, 41 implementation-ready, 36 importance of, 37
key-value, 54
network model, 41, 41–42 NoSQL, 53–56
object-oriented model, 48–49 object/relational database
management system, 49–50 relational model, 43, 43–45 terminology comparison, 59
data-modeling checklist, 232–233 data processing (DP) specialist
in computerized file system, 15–17
definition, 15
data processor (DP), 560 data-profiling software, 724 DataReader object, 690 data redundancy
definition, 20
revisited, 101–103 data replication, 574
definition, 578
influencing factors, 580 mutual consistency rule, 578 push and pull replication,
578–579 scenarios, 580 DataSets, 688, 690
data source name (DSN), 683 DataTable object, 690
data types
character, 254 columns, 284 date, 254 numeric, 254 SQL, 252–255
data warehouse
components, 607–608
data mart, 610
definition, 9, 607
integrated, 607–609
nonvolatile, 608–609
versus operational database data,
609
subject-oriented, 608–609
Index 785 time-variant, 608–609
twelve rules, 610–611 date
comparison operators, 274 data types, 254
in Microsoft Access, 362–363 in MySQL, 365
in Oracle, 363–364
DATEADD function, 363 DATE_ADD function, 365 Date, C. J., 583
DATE data type, 392 DATEDIFF function, 363 Date_format function, 365 DATE function, 362
DAY function, 362, 365
DBA. See database administrator DBLC. See Database Life Cycle DBMS. See database management
system
DDBMSs. See distributed database
management systems
DDC. See distributed data catalog DDD. See distributed data
dictionary
DDL. See data definition language deadlock
avoidance, 502 definition, 500 description, 500–501 detection, 502 prevention, 502
deadly embrace, 501
decentralized design, 474, 474–476 decision support data
database schema, 605–606 database size, 607
data extraction and filtering, 607 operational data versus,
602–605
decision support system (DSS), 598 DECODE function, 370
DEFAULT command, 247
deferred update method, 508 deferred-write technique, 507 DELETE command, 248, 269 deletion anomalies, 21 denormalization
definition, 202 description, 229–232 examples, 231
dependency diagram definition, 210
first normal form, 210–211
dependent attribute, 76 derived attributes
advantages and disadvantages, 124
definition, 123
depiction, 124
derived data, 231
description of operations, 460 design trap, 186
desktop databases, 8
determinant, 76, 213 determination, 76
DIFFERENCE operator, 85, 85–86 differential backup, 455
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

786 Index
dimensional tables, normalizing,
618–619 dirty data, 724 dirty read, 504
disaster management, 736 discipline-specific databases, 9 discriminator attributes, 175 disjoint subtypes, 174, 174–175 diskblock, 497
diskless workstations, 454 diskpage, 497
DISTINCT clause, 292 distributed concurrency control,
571
distributed data access, 555 distributed database design
data allocation, 580–581 data fragmentation, 575–578 data replication, 578–580
distributed database management systems (DDBMSs)
advantages and disadvantages, 556–557
centralized database management, 554, 556
characteristics, 559–560 “commandments,” 583 components, 560–561 database requirements, 555 data levels and process
distribution, 561–564 definition, 554 distributed database, 556,
556–558
distributed processing, 556,
556–558
evolution of, 554–556
factors, 555
fully heterogeneous, 563 heterogeneous, 563 homogeneous, 563 transparency features, 564–575
distributed databases, 8
distributed data catalog (DDC), 567 distributed data dictionary (DDD),
567
distributed global schema, 567 distributed request, 570, 570–571 distributed transaction, 569,
569–570 distribution transparency
definition, 564 features, 565–567 levels of, 565
DIVIDE operator, 90, 90–91 DKNF. See domain-key normal
form
DLLs. See dynamic-link libraries DM. See data manager
DML. See data manipulation
language
document databases, 664, 664–665 document type definition (DTD),
704, 704–706
domain-key normal form (DKNF),
207 domains, 119
DO-UNDO-REDO protocol, 572
DP. See data processor
drill down, 602
DROP INDEX command, 247, 264 DROP SEQUENCE command, 387 DROP TABLE command, 247, 290 DROP VIEW command, 247
DSN. See data source name
DSO. See database security officer DSS. See decision support system DTD. See document type definition durability, 487
dynamic-link libraries (DLLs), 683 dynamic query optimization, 520 dynamic SQL, 414
dynamic statistical generation
mode, 521 E
echo function, 701
edge, 668
EERD. See extended entity
relationship diagram EERM. See extended entity
relationship model embedded SQL, 410–414 end users
in database administrator, 734 description, 24
external model, 60
online analytical processing, 623 SQL-based relational database
application, 45 enhanced entity relationship
model. See extended entity
relationship model (EERM) enterprise databases, 8, 726 entity
associative, 98, 138–140 bridge, 98
clustering, 176, 176–177 composite, 98, 138–140 definition, 37
description, 118 existence-dependent, 126 existence-independent, 126 instance, 46, 118
integrity, 78, 177–181 occurrence, 46, 118 regular, 126
set, 46
strong, 126
subtypes, 170–171, 171 supertypes, 170–171, 171 weak, 129–131
entity relationship diagrams (ERDs) description, 46
iterative process, 140–147 overview of, 118
entity relationship model (ERM) associative entities, 138–140 attributes, 118–124 cardinality, 125–126 components, 46
conceptual design, 461–464 connectivity, 125–126 definition, 46
entities, 118
existence dependence, 126
notations, 46–48
recursive relationship, 136–138 relationship, 124–125 relationship degree, 134–136 relationship participation,
131–134
relationship strength, 126–129 weak entities, 129–131
equijoin, 89
ERDs. See entity relationship
diagrams
ERM. See entity relationship model ETL. See extraction, transformation,
and loading
eventual consistency, 55
EXCEPT operator, 375–376 exclusive lock, 499, 499–500 existence-dependent entity, 126 existence-independent entity, 126 EXISTS special operator, 279, 283 explanatory analytics, 670,
670–671
extended entity relationship
diagram (EERD), 170 extended entity relationship model
(EERM)
completeness constraint, 175,
175–176
definition, 170
disjoint subtypes, 174, 174–175 entity subtypes, 170–171, 171 entity supertypes, 170–171, 171 generalization, 176 inheritance, 172, 172–173 nonoverlapping subtypes, 174 overlapping subtypes, 174,
174–175
specialization, 176 specialization hierarchy, 171,
171–172
subtype discriminator, 174
extended relational data model (ERDM), 49–50
Extensible Markup Language (XML)
applications, 708–709 characteristics, 703
data models, 50
definition, 10, 702
document type definition, 704,
704–706
features, 703–704 presentation, 706–708 schema, 705
Extensible Style Language Transformations (XSLT), 706–707
extensions
client-side, 697, 697–698 server-side, 693
extents, 519 external model
advantages, 61
definition, 60
entity relationships, 60–61
external schema, 60
extraction, transformation, and
loading (ETL), 593, 609
F
Facebook, 10 fact tables
aggregation levels, 619 denormalizing, 619–620 multiple, 619–620 periodicity, 621
failure transparency, 565, 573–575 fan trap, 186, 186–187
feedback loop processing, 653 FETCH command, 407
field, 15
field-level lock, 498
fifth normal form (5NF), 207 file
basic terminology, 15 definition, 15
group, 519
file systems
computerized, 15–17
versus databases, 22
data processing, evolution of,
14–17
manual, 14
modern end-user productivity
tools and, 17–18 problems with, 18–19 simple, 17
first normal form (1NF) characteristics, 207 conversion to, 208–211 definition, 211
dependencies, 209–210 dependency diagram, 210–211 primary key, 208–209 repeating groups, 208
flags, 81
FLOOR function, 366
Flume, Hadoop ecosystem, 661 foreign key(s)
1:1 relationships, 183 creating links through, 301 definition, 79 designations, adding, 289
FOREIGN KEY command, 247 fourth normal form (4NF)
characteristics, 207 definition, 226 description, 224–226
fragmentation transparency database supports, 566 definition, 565
FROM clause
conditional restrictions, 271–276 definition, 267
FROM subqueries, 355–356 front-end CASE tools, 752
full backup, 455, 737
full functional dependence, 77 fully heterogeneous distributed
database management systems,
563
fully replicated database, 580 function(s)
aggregate, 292–297 conversion, 368–370 date, 361–365 numeric, 366
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

SQL, 292–297, 361–370 string, 366–368
time, 361–365
functional dependence, 76, 207–208
function-based index, 532 functions (listed by name)
ABS function, 366
ADDDATE function, 365 ADD_MONTHS function, 364 AVG function, 296, 296–297 CAST function, 369, 370
CEIL function, 366
CEILING function, 366 CONVERT function, 362, 370 COUNT function, 293, 293–295 DATEADD function, 363 DATE_ADD function, 365 DATEDIFF function, 363 Date_format function, 365 DATE function, 362
DAY function, 362, 365 DECODE function, 370
echo function, 701
FLOOR function, 366 LAST_DAY function, 364, 365 LENGTH function, 368
MAX function, 295, 295–296 MIN function, 295, 295–296 MONTH function, 362, 365 odbc_close function, 701 odbc_connect function, 701 odbc_exec function, 701 odbc_result function, 701 ROUND function, 366 SUBSTRING function, 368 SUM function, 296
SWITCH function, 370 SYSDATE function, 364 TO_CHAR function, 363, 369 TO_DATE function, 364 TO_NUMBER function, 370 while function, 701
YEAR function, 362, 365
G
generalization, 176 general-purpose databases, 9 Google, 4, 10
governance, 595
granularity, 216, 216–217
lock, 496, 496–498 graph database
components of, 668 definition, 668 edge, 668
node, 668 properties, 668 representation, 669 traversal, 668
GROUP BY clause, 248, 297, 297–300
guided data mining, 673
H
Hadoop
data ingestion applications, 661 definition, 52
direct query applications, 661–662 ecosystem, 660–662 MapReduce, 658, 658–660
Hadoop Distributed File System (HDFS), 52, 655, 655–657
hardware
database system component, 22 independence, 62
performance, 517
query processing, 525
in SDLC, 443
hash index, 527
HAVING clause, 299, 299–300
conditional expression, 533 HAVING subqueries, 353 HBase, column-oriented NoSQL
database, 661
HDFS. See Hadoop Distributed File
System heartbeat, 657
heterogeneity transparency, 565 heterogeneous distributed database
management systems, 563 hierarchical model, 41 higher-order relationship, 135 Hive, data warehousing system, 660 homogeneous distributed database
management systems, 563 homonyms, 91
horizontal fragmentation, 575, 576 host language, 410
I
IaaS. See Infrastructure as a Service
identifiers
composite, 119–120 definition, 119 natural, 178
relational schema, 119
identifying relationship, 128
IE. See information engineering Impala, SQL-on-Hadoop
application, 662
inconsistent retrievals, 492, 492–493 incremental backup, 737
index(es)
bitmap, 527
B-tree, 527
CREATE INDEX command,
263, 263–264 definition, 103 function-based, 532 hash, 527
query optimization and, 526–528
relational database, 103–104 selectivity, 531–532, 532 SQL, 263–264
table space, 537
unique, 104
index-organized table (IOT), 538 information
versus data, 4–6 definition, 4 islands of, 20
information age, 5
information engineering (IE), 755
information resource dictionary,
751
information resource manager (IRM), 730
information system (IS) definition of, 440 department, 727 performance of, 440–441
information systems architecture (ISA), 755
Infrastructure as a Service (IaaS),
713–714
inheritance, 48, 172, 172–173
inline subquery, 356
in-memory databases, 30, 536 Inmon, Bill, 610
inner join, 89, 341
inner query, 270
input/output (I/O) accelerators, 536 input/output (I/O) request, 519 INSERT command, 248, 264,
270–271
insertion anomalies, 21
IN special operator, 279, 282
IN subqueries, 352–353
integrated data warehouse, 607–609 integrity
data, 736
entity, 78, 177–181 referential, 79
rules, 80–81
in security, 745–746
internal model definition, 62 description, 62–63
internal schema, 62
Internet database connectivity
benefits, 693 characteristics, 693 client-side extensions, 697,
697–698
description, 692
web application server, 698 web browser, 696–697
web database development,
699–702
web server interfaces, 695–696 web-to-database middleware,
693–695, 694 INTERSECT operator, 85,
373–375
IOT. See index-organized table IRM. See information resource
manager
IS. See information system ISA. See information systems
architecture
IS NULL special operator, 279, 280 isolation, 487
J
Java, 691
Java Database Connectivity (JDBC)
architecture, 691–692
definition, 691
JavaScript, 697
JavaScript Object Notation (JSON),
664
Index 787 JDBC. See Java Database
Connectivity job tracker, 659
join(s)
columns, 87
cross, 342
equijoin, 89
inner, 89, 341
left outer, 89
natural, 87, 343, 343–344 operators, 341–349
outer, 89, 341, 347–349 right outer, 89
theta, 89
JOIN ON clause, 345–346 JOIN operator, 87, 87–90 JOIN USING clause, 344–345 JSON. See JavaScript Object
Notation
K
Kelley, Chuck, 610
key(s). See also foreign keys;
primary keys attribute, 77 candidate, 78 composite, 77 definition, 76 dependencies, 76–77 foreign, 79
natural, 178
primary, 76
relational database, 77–80 secondary, 79
super, 77
surrogate, 180–181, 181 types of, 77–80
key performance indicators (KPIs),
595
key-value (KV) databases, 663,
663–664
key-value data model, 54 knowledge
characteristics, 5
in data mining, 671 explanation, 5
KPIs. See key performance indicators
L
language, host, 410 LAST_DAY function, 364, 365 left outer join, 89
LENGTH function, 368
LIKE special operator, 279,
280–282 LinkedIn, 10
linking table, 99 listener, 519 listing
ordering, 290–292
unique values, 292
local mapping transparency
database supports, 567
definition, 565 location transparency
database supports, 566–567 definition, 565
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

788 Index lock(s)
binary, 498, 498–499 database-level, 496 deadlock, 500
definition, 495
exclusive, 499, 499–500 field-level, 498 granularity, 496, 496–498 manager, 495, 520 page-level, 497 pessimistic, 495 row-level, 498
shared, 499
table-level, 496
two-phase locking, 500, 500–501 types of, 498–500
logical data format, 19 logical design, 62
definition, 468
mapping conceptual model to,
468–470
steps, 468
against user requirements, 471 using integrity constraints,
470–471
using normalization, 470
logical independence, 63 logical operators, 277–279 logic, predicate, 73
logs, transaction, 489, 489–490 lost update, 490, 490–491
M
mandatory participation, 131, 131–134
manual file systems, 14
manual query optimization, 520 manual statistical generation mode,
521
many-to-many (M:N) relationship, 38, 97–100
map function, 658 mapper, 658 MapReduce, 53
batch processing, 660 conceptual illustration, 658 definition of, 658
general process, 659 implementation of, 659 simplification applications,
660–661
master data management (MDM),
595, 724
materialized view, 633, 633–636 MAX function, 295, 295–296 MDBMSs. See multidimensional
database management systems MDM. See master data
management metadata, 6
Microsoft Access
COMMIT command, 266 COUNT function, 294–295 CREATE VIEW command, 378 database creation, 251
date functions, 362–363
query by example, 271–272 relational model, 74
ROLLBACK command, 269 sequences, 382–387
time functions, 362–363
Microsoft Azure, 8
Microsoft Excel, 17
Microsoft .NET framework, 687 Microsoft SQL Server
date functions, 362–363 persistent stored module, 388 query optimization, 545 sequences, 382–387
time functions, 362–363 trigger, 394
middleware, web-to-database, 693–695
MIN function, 295, 295–296 minimal data rule, 458
mixed fragmentation, 575, 577–578 M:N relationship. See many-to-
many (M:N) relationship mobile wireless revolution, 555 model, 36
module
coupling, 466
definition, 464 monotonicity, 502 MONTH function, 362, 365 MPMD. See multiple-site
processing, multiple-site data MPSD. See multiple-site processing,
single-site data multidimensional database
management systems
(MDBMSs), 628 multidimensional online analytical
processing (MOLAP) definition, 628
relational OLAP versus, 628–629
multiple-site processing, multiple- site data (MPMD), 563, 563–564
multiple-site processing, single-site data (MPSD), 562, 562–563
multiuser databases, 8 multivalued attributes in an entity, 121 components, 123
definition, 121
implementing, 122 mutual consistency rule, 578 mutual exclusive rule, 499 MySQL
COMMIT command, 266 CREATE TABLE command, 257 date functions, 365 DEFAULT/CHECK constraints,
261
DELETE command, 270 query optimization, 545 time functions, 365 Twitter and, 10 UPDATE command, 270
N
name node, 52, 657
naming conventions, 41, 216, 233 National Institute of Standards and
Technology (NIST), 709
native database connectivity, 682–683
natural identifier, 178
natural join, 87, 343, 343–344 natural keys, 178
nested query, 270
network(s)
components, 560 latency, 574 partitioning, 574 performance, 517 query processing, 525
network model, 41, 41–42 NewSQL databases, 669, 669–670 NIST. See National Institute of
Standards and Technology node
availability, 574 client, 657
data, 657 definition, 668
in HDFS, 656–657 name, 657
non-identifying relationship, 127 nonkey attribute, 202 nonoverlapping subtypes, 174 nonprime attribute, 202 nonrepeatable read, 504 nonvolatile data warehouse,
608–609 normalization
conceptual design, 461–464 database design, 215–219,
226–229 data-modeling checklist,
232–233
definition, 202 functional dependencies,
207–208
higher-level normal forms,
220–226
logical design, 470
need for, 202–206 normal forms, 202 process of, 206–215 surrogate keys, 219–220
NoSQL. See Not only SQL NOT logical operator, 278,
278–279
NOT NULL command, 247 Not only SQL (NoSQL)
in Big Data, 53 column-oriented databases,
665–667
data models in, 53–56 definition, 662 description, 11
document databases, 664,
664–665
graph database, 668, 668–669 key-value databases, 663,
663–664
NewSQL databases, 669,
669–670 null values
conditional expression, 533
definition, 78 NUMBER data type, 392
numeric data types, 254 numeric functions, 366
O
Object Linking and Embedding for Database (OLE-DB)
architecture, 688
classes and interfaces, 687 consumers, 685
data providers, 686 definition, 685
service providers, 686
object-oriented database management system (OODBMS), 48
object-oriented data model (OODM), 48–49 object/relational database
management system (O/R
DBMS), 50
ODBC. See Open Database
Connectivity
odbc_close function, 701 odbc_connect function, 701 odbc_exec function, 701 odbc_result function, 701 OLE-DB. See Object Linking and
Embedding for Database one-to-many (1:M) relationship,
38, 93–95
one-to-one (1:1) relationship
definition, 38
ERM components, 146
foreign key in, 183 implementing, 182–183 recursive relationship, 136, 151 relational database, 95–97 specialization hierarchy, 172
online analytical processing (OLAP) advanced database support, 623 architecture, 623–626 characteristics, 621
CUBE extension, 631–632 definition, 9, 621 end-user interfaces, 623 materialized view, 633,
633–636
multidimensional, 628, 628–629 multidimensional data analysis
techniques, 621–623 relational, 626, 626–627 relational versus
multidimensional, 628–629 ROLLUP extension, 630–631 SQL extensions, 629–636
online transaction processing (OLTP) database, 9
OODBMS. See object-oriented database management system
OODM. See object-oriented data model
OPEN command, 407
Open Database Connectivity
(ODBC)
to access database, 684 configuring Oracle data source,
685 definition, 683
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

operand, 533 operational cost, 443 operational data
data warehouse data versus, 609 decision support data versus,
602–605 operational database, 9 operator(s)
arithmetic, 276–277 comparison, 273–274 logical, 277–279 special, 279–283
optimistic method, concurrency control, 503, 503–504
optimizer
cost-based, 528–529 database statistics, 529–530 in DBMS, 520, 536
hints, 530, 530–531 objective, 529
rule-based, 528
optional attributes, 119 optional participation, 131 Oracle
CREATE TABLE command, 258–259
database administration tools, 757–765
date functions, 363–364 ODBC data source, 685 query optimization, 544 sequences, 382–387 time functions, 363–364 trigger, 394
O/R DBMS. See object/relational database management system
ORDER BY clause, 248, 290, 290–292
organizational rigidity, 556 OR logical operator, 277 outer join, 89, 341, 347–349 overlapping subtypes, 174,
174–175
P
PaaS. See Platform as a Service page. See diskpage
page-level lock, 497
partial completeness, 175
partial dependency, 208
partially replicated database, 580 participants, 124
participation mandatory, 131 optional, 131 relationship, 131–134
partitioned data allocation, 580 partitioning, 620
partition tolerance, 581
passive data dictionary, 750 passwords, 454
people, database system component, 23–24
perfective maintenance, 445 performance
degradation, 556
system, guidelines, 517 transparency, 565, 573–575
performance tuning
database, 516, 516–517 DBMS, 517, 517–518, 536–538 definition, 25
SQL, 517, 517–518, 531–534
periodicity, 621
persistent stored module (PSM),
388
pessimistic locking, 495 phantom read, 504 physical data format, 19 physical design
data storage organization, 472 definition, 471
integrity and security measures,
472–473
performance measurements, 473 stages, 471
physical independence, 63 physical model, 63
Pig, high-level scripting language,
661
PKs. See primary keys
Platform as a Service (PaaS), 713 plug-in, 697
policy
in DBAs, 734
security, 746
portals, 594
preaggregated data, 231
predicate logic, 73
predictive analytics, 670, 673–674 PRIMARY KEY command, 247 primary keys (PKs). See also
identifiers; keys characteristics, 179 composite, 178–180
data granularity, 216–217 definition, 76 designations, adding, 289 entity integrity, 177–178 first normal form, 208–209 guidelines, 178
natural keys and, 178
surrogate, 180–181, 181 prime attribute, 202
privacy, 725
private cloud, 712
Procedural Language SQL (PL/
SQL)
data types, 392 definition, 388 description of, 387–391 processing with cursors,
407–409
stored functions, 409
stored procedure, 401, 401–406 triggers, 392–401, 393
procedure
cache, 519
data administration strategy, 735 database system component, 24
production database, 9 profile, 762
PROJECT operator, 83, 83–84 PROJECT table, 212
PSM. See persistent stored module public cloud, 712
pull replication, 579 push replication, 578
Q
query
ad hoc, 7
definition, 7 formulation, 534–535 inner, 270
language, 27
nested, 270 optimizer, 523 recursive, 303
result set, 7 subquery, 270
query optimization algorithms, 520 automatic, 520
in DDBMS, 559 dynamic, 520 examples, 538–545 indexes and, 526–528 manual, 520 operation modes, 520 principles, 520
static, 520
techniques, 520–521 query processing
bottleneck, 525, 525–526 DBMS process, 522
I/O operations, 524
SQL execution phase, 524 SQL fetching phase, 525 SQL parsing phase, 523–524
R
RAD. See Rapid Application Development
RAID. See redundant array of independent disks
RAM. See random access memory random access memory (RAM)
performance, 517
query processing, 525
rapid ad hoc data access, 555 Rapid Application Development
(RAD), 441
RDBMS. See relational database
management system
RDO. See remote data objects
Read Committed isolation level, 504 Read Uncommitted isolation level,
504
record, 15
recursive joins, 303–304 recursive query, 303
recursive relationship, 136–138 reduce function, 658
reducer, 658
redundant array of independent
disks (RAID), 537 redundant data, 231
redundant transaction logs, 506 referential constraint actions,
261–262 referential integrity, 79 regular entity, 126 relation, 43
Index 789 formal definitions, 82
operators, 83–91
terminology, 82 relational database
Codd rules, 104–105
data redundancy revisited,
101–103
indexes, 103–104 many-to-many (M:N)
relationship, 97–100 one-to-many (1:M) relationship,
93–95
one-to-one (1:1) relationship,
95–97
relational database management
system (RDBMS)
definition, 43 multidimensional data schema
support, 626 SQL usage, 44
relational diagram, 44, 483 relational model
characteristics, 74 definition, 43
description of, 43–44, 73–76 end-user perspectives, 45 Microsoft Access in, 74 relational diagram, 44–45
relational online analytical processing (ROLAP)
data access language, 626–627 definition, 626 multidimensional data schema
support, 626 multidimensional OLAP versus,
628–629
query performance optimized
for, 626–627
very large databases, 627
relational schema, 119 relational set operators
DIFFERENCE operator, 85, 85–86
DIVIDE operator, 90, 90–91 EXCEPT operator, 375–376 INTERSECT operator, 85,
373–375
JOIN operator, 87, 87–90 PROJECT operator, 83, 83–84 RESTRICT operator, 83 SELECT operator, 83
syntax alternatives, 377 UNION operator, 84, 84–85,
371–373
UNION ALL operator, 373
relational view, 379 relationship(s)
binary, 134, 134–135 database design case, 187–188 definition, 38
degree, 134–136 higher-order, 135
identifying, 128 non-identifying, 127 participants, 124 participation, 131–134
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
relational algebra definition, 82

790 Index recursive, 136–138
strength, 126–129 strong, 128
ternary, 134, 134–135 unary, 134, 134–135 weak, 127
reliability, 556
relvar, 82
remote data objects (RDO), 683,
683–685
remote request, 568
remote transaction, 568, 568–569 Repeatable Read isolation level, 504 repeating group, 208
replicated data allocation, 580 replication, 620
replica transparency, 574
required attributes, 119
reserved words, 258
RESTRICT operator, 83
right outer join, 89
roles, 762
ROLLBACK command, 488,
507–508
rollback segment table space, 537 roll up, 602
ROUND function, 366 row-centric storage, 665 row-level lock, 498
rule-based optimizer, 528 rule-based query optimization
algorithm, 521 rules of precedence, 277
S
SaaS. See Software as a Service scalability, 556
scaling out, 651
scaling up, 651
scheduler, 493–495, 494, 520 schema
conceptual, 61 definition, 42, 251 external, 60 internal, 62
XML, 705
scope, 449
script, 687
SDLC. See Systems Development
Life Cycle secondary key, 79
second normal form (2NF) characteristics, 207
conversion to, 211–213 definition, 212
dependent attributes, 212 eliminate partial dependencies,
211 security
breach, 746
database, 748, 748–749 definition, 725
goals, 745–746 passwords, 454 physical, 454
policy, 746
threat, 746 vulnerability, 746, 747
segment, 41 SELECT command
definition, 248, 266
GROUP BY clause, 248, 297,
297–300
HAVING clause, 299,
299–300
joining database table(s),
300–304
SELECT operator, 83
SELECT queries, 271–283 semantic data model, 48 semistructured data, 10 sequences, 382, 382–387 serializability, 487
serializable isolation level, 505 serializable schedule, 494 server-side extension, 693 set-oriented commands, 371
set theory, 73
shards, 669
shared lock, 499
simple attributes, 121
simple file systems, 17 single-site processing, single-site
data (SPSD), 561, 561–562 single-user databases, 8 single-valued attributes, 121 snowflake schema, 618
social media, 10 software
database system component, 22–23
data-profiling, 724 independence, 62 MDM, 724 performance, 517 in SDLC, 443 types of, 22–23
Software as a Service (SaaS), 713 sort cache, 536
SPARC. See Standards Planning
and Requirements Committee sparse data, 55
sparsity, 628
specialization, 176 specialization hierarchy, 171,
171–172
special operators, 279–283 spreadsheets, databases and,
comparison of, 27 Sprint, 4
SPSD. See single-site processing, single-site data
SQL. See Structured Query Language
SQLCODE variable, 412
SQL data services, 716 SQLSTATE variable, 412 Sqoop, Hadoop ecosystem, 661 standards, 734–735
Standards Planning and Requirements Committee (SPARC), 59
star schema
attribute hierarchy, 614, 614–616 attributes, 612–614
definition, 610
dimensions, 611
facts, 611 performance-improving
techniques, 617–621 representation, 616–617
stateless system, 697
static query optimization, 520 static SQL, 414
statistically based query
optimization algorithm, 520,
520–521
stored functions, 409
stored procedure, 401, 401–406 stream processing, 652
string functions, 366–368
strong entity, 126
strong relationship, 128
structural dependence, 19 structural independence, 19 structured data, 9, 653
Structured Query Language (SQL)
cache, 519, 536
constraints, 259–262
database model, 249–251
data definition commands, 247,
249–264
data manipulation commands,
248, 264–271
data types, 252–255 definition, 27
dynamic, 414
embedded, 410–414
functions, 292–297, 361–370 indexes, 263–264
introduction to, 247–249
join operators, 341–349 performance tuning, 517, 517–
518, 531–534
relational set operators, 371–377 schema, 251–252
static, 414
table structures, 255–259 transaction management,
488–489
subject-oriented data warehouse,
608–609 subordinates, 572
subqueries
FROM, 355–356
IN, 352–353
ALL multirow operator, 353–355 ANY multirow operator,
353–355
attribute list, 356–358 characteristics, 350 correlated, 358, 358–361 definition, 270 examples, 350 HAVING, 353
inline, 356
overview of, 349–351 WHERE, 351–352
subschema, 42 SUBSTRING function, 368 subtype discriminator, 174 SUM function, 296
super column, 667 superkey, 77
surrogate keys, 180–181, 181, 219–220
SWITCH function, 370 synonym, 93
syntax alternatives, 377 SYSDATE function, 364 system administrators, 23 system analog
data dictionary and, 91–93
definition, 91
system analysts, 24
system cost, 443
system performance, guidelines,
517
system programmers, 24 systems administrator, 730 systems analysis, 440
systems development, 440 Systems Development Life Cycle
(SDLC)
analysis, 443–444 definition, 442
detailed systems design, 444 implementation, 444–445 maintenance, 445
planning, 442–443
system table space, 537
T
table(s) base, 377
clustered, 472
copying parts of, 287–289 CREATE TABLE command, 255 creating structures, 255–259 definition, 43, 73
deleting from database, 290 partitioning, 620
replicating, 620
SQL, 255–259
table-level lock, 497 table row(s)
adding, 264–266 deleting, 269–270 inserting, 270–271 listing, 266–268
null attributes, 265 optional attributes, 265 selecting, 271–276 updating, 268–269
table space
definition, 519, 760 index, 537
rollback segment, 537 system, 537 temporary, 537
user data, 537
tags, 702
task tracker, 659
temporary table space, 537 ternary relationship, 134, 134–135 theta join, 89
third normal form (3NF)
characteristics, 207 conversion to, 213–215 definition, 214
dependent attributes, 213–214 transitive dependencies, 213
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

time
Microsoft Access, 362–363 MySQL, 365
Oracle, 363–364
time stamping, 502, 502–503 time-variant data, 183, 183–186 time-variant data warehouse,
608–609
TM. See transaction manager TO_CHAR function, 363, 369 TO_DATE function, 364 TO_NUMBER function, 370 top-down design, 473, 473–474 total completeness, 175
TP. See transaction processor transaction(s)
atomic transaction property,
506
definition, 484
evaluating results, 484–487 isolation, 504–505 management, 559 properties, 487–488 relational diagram, 483 SQL, 488–489 understanding, 484
transactional database, 9 transaction logs
backup, 455 definition, 489 description, 489–490 redundant, 506
transaction manager (TM), 560 transaction processor (TP), 560 transaction recovery management
buffers, 506
checkpoints, 507
deferred update method, 508 deferred-write technique, 507 log characteristics, 508–509 redundant transaction logs, 506 ROLLBACK command
operation, 507–508 write-ahead-log protocol, 506 write-through technique, 507
transaction transparency
definition, 565
distributed concurrency control,
571
distributed request, 570, 570–571 distributed transaction, 569,
569–570
remote request, 568 remote transaction, 568,
568–569
two-phase commit protocol,
571, 571–573 transitive dependency, 208 transparency
distribution, 564 failure, 565 fragmentation, 565 heterogeneity, 565 local mapping, 565 location, 565 performance, 565 transaction, 565
traversal, 668 trigger(s)
action, conditional DML predicates, 401
definition, 393
in PL/SQL, 392–401 row-level, 394 statement-level, 394
tuple, 43
Twitter, 10
two-phase commit protocol (2PC),
571, 571–573
two-phase locking (2PL), 500,
500–501
%TYPE data type, 392
U
UDA. See Universal Data Access UML. See Unified Modeling
Language
unary relationship, 134, 134–135 uncommitted data, 491, 491–492 Unified Modeling Language (UML),
48, 118, 441
UNION operator, 84, 84–85, 371–373
UNION ALL operator, 373 union-compatible operator, 84, 371 UNIQUE command, 247
unique fragment, 566
unique index, 104
uniqueness, 502
Universal Data Access (UDA), 682 unreplicated database, 580 unstructured data, 9, 653
update anomalies, 21
UPDATE command, 248, 268 user(s). See also end users
data table space, 537 in DBMS, 520 definition, 762
V
value, 654
VARCHAR2 data type, 392 variability, 654
variety, 649, 653–654 VBScript, 698
velocity, 649, 652–653 vertical fragmentation, 575,
576–577
very large databases (VLDBs), 29
decision support databases, 607
definition, 607
relational online analytical
processing, 627 viability, 654
view
definition, 377 relational, 379 updatable, 379–382, 380
virtualization, 451 virtual tables
overview of, 377–382
updatable views, 379–382 visualization, 654
VLDBs. See very large
databases
VLDBs (very Large Databases), 29 volume, 649, 651–652
volume, velocity, and variety, 51
Index 791 W3C. See World Wide Web
Consortium
wait/die scheme, 502–503, 503 weak entities, 129–131
weak relationship, 127
web application server, 698 web browser, 696–697
web database development,
699–702
web server interfaces, 695–696 web-to-database middleware
definition, 694 interaction components,
694–695
server-side extension, 693
WHERE clause
conditional expression, 533 conditional restrictions,
271–276 definition, 271
while function, 701 wildcard character, 266,
266–267
workgroup databases, 8 workstations, diskless, 454 World Wide Web Consortium
(W3C), 702
wound/wait scheme, 502–503,
503
write-ahead-log protocol, 506 write-ahead protocol, 572 write-through technique, 507
X
XML. See Extensible Markup Language
XML schema definition (XSD),
706
XSD. See XML schema definition
XSLT. See Extensible Style Language Transformations
Y
YEAR function, 362, 365
Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
W

Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

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