Privacy-Aware Location-Aided Routing in Mobile Ad-hoc Networks
CS430/630
Database Management Systems
Spring 2018
Gabriel Ghinita
University of Massachusetts at Boston
People & Contact Information
Instructor: Gabriel Ghinita
Email: Gabriel.Ghinita AT umb DOT edu (preferred contact)
Web: http://www.cs.umb.edu/~gghinita
Phone: (617) 287-6479
Office: Science Building, 3rd Floor, Room 88 (S-3-88)
TA: Mohammad Hadianpour
Email: Mohammad.Hadian001 AT umb DOT edu
Office: S-3-124A
Course-related emails (for instructor and for TA)
Subject line MUST BEGIN with [CS430] or [CS630]
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http://www.cs.umb.edu/~gghinita
Course Info
Lecture Hours
Tue and Thu , 7:00-8:15pm
Office Hours
Tue & Thu 5:30-7:00pm
By appointment (send email)
Class URL
http://www.cs.umb.edu/~gghinita/cs430/
http://www.cs.umb.edu/~gghinita/cs630/
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http://www.cs.umb.edu/~gghinita/cs430/
http://www.cs.umb.edu/~gghinita/cs630/
Textbook & Recommended Readings
Textbook
Database Management Systems, 3rd Edition
by Ramakrishnan and Gehrke
Other recommended texts
Database System Concepts, Silberschatz, Korth and Sudarshan,
6th Edition
Database Principles, Programming, and Performance, P. E. O’Neil
and E. J. O’Neil
Other resources will be posted in the links section of the site
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Prerequisites
Data Structures and Algorithms
CS310
Programming
CS240
Discrete Math
Familiarity with UNIX OS
Exercises will be executed on Oracle 12G server running on a
Unix machine in the CS dept (DBS3 Machine)
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Grading
Final exam (40%) – open book
Midterm (30%) – open book (Thu March 22nd)
Open book does NOT include electronic devices!
6 homework assignments
5% each
Assignments for CS630 will have additional questions
Assignments are individual – submit your own work only!
No plagiarism! See student code of conduct
Lecture attendance is mandatory
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Course Materials
Class URL
http://www.cs.umb.edu/~gghinita/cs430/
http://www.cs.umb.edu/~gghinita/cs630/
Blackboard
Discussion forums
Make sure you create Unix course accounts, and that you
enroll these accounts for 630 (“apply” procedure)
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http://www.cs.umb.edu/~gghinita/cs430/
http://www.cs.umb.edu/~gghinita/cs630/
University Policies
Student Conduct: Students are required to adhere to the University
Policy on Academic Standards and Cheating, to the University Statement
on Plagiarism and the Documentation of Written Work, and to the Code
of Student Conduct as delineated in the University Catalog and Student
Handbook.The Code is available online at:
https://www.umb.edu/editor_uploads/images/life_on_campus/
Accommodations: Section 504 of the Americans with Disabilities Act
of 1990 offers guidelines for curriculum modifications and adaptations
for students with documented disabilities. If applicable, students may
obtain adaptation recommendations from the Ross Center for Disability
Services, CC-UL Room 211, (617-287-7430). The student must present
these recommendations and discuss them with each professor within a
reasonable period, preferably by the end of Drop/Add period.
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callto:+1617-287-7430
Course Overview
Relational Data Model
Relational Algebra
Structured Query Language
The most important part of the course
Conceptual design – the ER model
Database application development
Java, PL/SQL
Design Theory
Database Security
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What is a DBMS?
Specialized software that provides:
Uniform and transparent access to data
Application-independence
Application/user is oblivious to internal data organization
Data organization may change, but applications need not change
Efficient access to data
Fast search capabilities, indexing
Data consistency
E.g., cannot delete student record if grade records still in DBMS
Concurrent access to data
Persistent storage and recovery from failure
Security
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Why study databases?
Databases are ubiquitous
Behind all web service providers there is a DBMS
Most often a very large-scale one
Corporations use DBMS for business processes, HR, etc
Scientific computing relies on very large amounts of data
Humane genome data
Biochemistry data (protein sequences)
Astronomy data
High-energy physics
DBAs are very well–paid!
And even in other IT areas, DBMS skills are a must
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A bit of history …
First data stores were file systems
Does not conform to transparency and uniformity desiderata
Search (within file) most often linear
Not portable
Doesn’t handle concurrency properly
Sequential access only
Early DBMS appeared in the 60s
Driven by banking and airline industry
Relatively small record size, and many concurrent accesses
Two prominent models: hierarchical model (tree) and network
model (graph)
Lack of support for high-level query languages
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A bit of history (contd.)
Relational Databases
Major breakthrough, paper written by Codd (1970)
Relations (tables) with rows (records) and columns (fields)
Relationships and constraints among tables
Structured Query Language (SQL): high-level, declarative
Data definition/ manipulation language
Fast search – use of index structures
Data access language independent from internal organization
Newer paradigms
Object-oriented and multimedia DB
Data Stream Management Systems (DSMS)
MapReduce
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Levels of Abstraction
Data
Physical Schema
Conceptual Schema
View 1 View 2 View 3
Database Management Systems 3rd ed, Ramakrishnan and Gehrke
Describes files
and indexes used
Defines logical
data structure
Views define how
users see data
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The Relational Model
Data Model
Structure of data
Relational model uses tables
Programming languages deal with arrays, collections, etc
Operations on the data
Queries: operations that retrieve information
Modifications: operations that change data
Constraints
Domain constraints (the simplest): e.g., age must be numeric
Other constraints: each student has unique matriculation #
Prominent Data Models
Relational model
Object-relational model, semi-structured model (XML), E-R
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Relational Model
Relational database: a set of relations
Relation:
two-dimensional table, with rows and columns
#Rows = cardinality
#Columns= degree (or arity)
Each row represents an entity
A student, a course, a movie
Each column represents a property of the entity
Student age, student matriculation #, student gpa
Column values are atomic (e.g., integer or string) within
given domain
Rows are also called tuples or records; columns are also
called fields or attributes
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“Students” Relation or Table
sid name login age gpa
53666 Jones jones@cs 18 3.4
53688 Smith smith@eecs 18 3.2
53650 Smith smith@math 19 3.8
Cardinality = 3, Degree = 5
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Relational Schema
Schema: specifies name of relation, plus name and domain
of each column
Students ( sid: integer,
name: string,
login: string,
age: integer,
gpa: real )
Each relation must have a schema
Similar to a data type in programming languages
Relational database schema = collection of relations’
schemas
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More about Relations
Relations are sets of tuples
Sets are NOT ordered
Do NOT retrieve by order number, but by content!
Relation Instance
Contents of a relation may change over time
Tuples are added/deleted/modified
E.g., Students join or leave the university
Instance represents set of tuples at a certain point in time
Schemas may change too
Although this is not frequent in practice
Changing schema is very expensive
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Instance of “Students” Relation
sid name login age gpa
53666 Jones jones@cs 18 3.4
53688 Smith smith@eecs 18 3.2
53650 Smith smith@math 19 3.8
Cardinality = 3, Degree = 5
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Another Instance of “Students”
sid name login age gpa
53666 Jones jones@cs 18 3.4
53688 Smith smith@eecs 18 3.2
53650 Smith smith@math 19 3.8
53660 Korth korth@math 22 3.6
Cardinality = 4, Degree = 5
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Keys
A key of a relation is a set of fields K such that:
1. No two distinct tuples in ANY relation instance have same
values in all key fields, and
2. No subset of K is a key (otherwise K is a superkey)
Key may not be unique
Multiple candidate keys may exist
One of the keys is chosen (by DBA) to be the primary key
Keys are shown underlined in schema
In the relational model, duplicate tuples do not exist!
But most DBMS implementations do allow duplicates
Keys constraints must be set by DBA to avoid duplicates
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Example of Keys
Students(sid: string, name: string, login: string, age: integer, gpa: real)
sid is a key; {sid, name} is a superkey
In practice, it is not easy to know when there exists a
unique attribute combination in the data (e.g., names)
artificial keys are created: student ID, customer ID, etc.
SSN is also often used for keys
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sid name login age gpa
53666 Jones jones@cs 18 3.4
53688 Smith smith@eecs 18 3.2
53650 Smith smith@math 19 3.8