module3
INFS614, GMU, Dr. Brodsky, Lecture 3 1
The ER and Relational Models
Module 3
Professor Alex Brodsky
Database Systems
INFS614, GMU, Dr. Brodsky, Lecture 3 2
ER Review
lot
dname
budgetdid
since
name
Works_In DepartmentsEmployees
ssn
Works_in: m-to-1 relationship
Key for Works_In: SSN (of employees)
INFS614, GMU, Dr. Brodsky, Lecture 3 3
ISA (`is a’) Hierarchies
Contract_Emps
name
ssn
Employees
lot
hourly_wages
ISA
Hourly_Emps
contractid
hours_worked
❖As in C++, or other PLs,
attributes are inherited.
❖If we declare A ISA B, every A
entity is also considered to be a B
entity.
❖ Overlap constraints: Can Joe be an Hourly_Emps as well as
a Contract_Emps entity? (Allowed/disallowed)
❖ Covering constraints: Does every Employees entity also have
to be an Hourly_Emps or a Contract_Emps entity? (Yes/no)
❖ Reasons for using ISA:
– To add descriptive attributes specific to a subclass.
– To identify entitities that participate in a relationship.
INFS614, GMU, Dr. Brodsky, Lecture 3 4
Aggregation
❖ Used when we have
to model a
relationship
involving (entitity
sets and) a
relationship set.
– Aggregation allows us
to treat a relationship
set as an entity set
for purposes of
participation in
(other) relationships.
☛ Aggregation vs. ternary relationship:
❖ Monitors is a distinct relationship,
with a descriptive attribute.
❖ Also, can say that each sponsorship
is monitored by at most one employee.
budgetdidpid
started_on
pbudget
dname
until
DepartmentsProjects Sponsors
Employees
Monitors
lot
name
ssn
since
INFS614, GMU, Dr. Brodsky, Lecture 3 5
Conceptual Design Using the ER Model
❖ Design choices:
– Should a concept be modeled as an entity or an attribute?
– Should a concept be modeled as an entity or a relationship?
– Identifying relationships: Binary or ternary? Aggregation?
❖ Constraints in the ER Model:
– A lot of data semantics can (and should) be captured.
– But some constraints cannot be captured in ER diagrams.
INFS614, GMU, Dr. Brodsky, Lecture 3 6
Entity vs. Attribute
❖ Should address be an attribute of Employees or an
entity (connected to Employees by a relationship)?
❖ Depends upon the use we want to make of address
information, and the semantics of the data:
◆ If we have several addresses per employee, address must
be an entity (since attributes cannot be set-valued).
◆ If the structure (city, street, etc.) is important, e.g., we
want to retrieve employees in a given city, address must
be modeled as an entity (since attribute values are
atomic).
INFS614, GMU, Dr. Brodsky, Lecture 3 7
Entity vs. Attribute (Contd.)
❖ Works_In2 does not
allow an employee to
work in a department
for two or more periods.
❖ Similar to the problem
of wanting to record
several addresses for an
employee: we want to
record several values of the
descriptive attributes for
each instance of this
relationship.
name
Employees
ssn lot
Works_In2
from to
dname
budgetdid
Departments
dname
budgetdid
name
Departments
ssn lot
Employees Works_In3
Durationfrom to
INFS614, GMU, Dr. Brodsky, Lecture 3 8
Entity vs. Relationship
❖ First ER diagram OK if
a manager gets a
separate discretionary
budget for each dept.
❖ What if a manager gets
a discretionary budget
that covers all
managed depts?
– Redundancy of dbudget,
which is stored for each
dept managed by the
manager.
Manages2
name dname
budgetdid
Employees Departments
ssn lot
dbudgetsince
Employees
since
name dname
budgetdid
Departments
ssn lot
Mgr_Appts
Manages3
dbudget
apptnumMisleading: suggests dbudget
tied to managed dept.
INFS614, GMU, Dr. Brodsky, Lecture 3 9
Binary vs. Ternary Relationships
❖ If each policy is
owned by just 1
employee:
– Key constraint
on Policies
would mean
policy can only
cover 1
dependent!
❖ What are the
additional
constraints in the
2nd diagram?
agepname
DependentsCovers
name
Employees
ssn lot
Policies
policyid cost
Beneficiary
agepname
Dependents
policyid cost
Policies
Purchaser
name
Employees
ssn lot
Bad design
Better design
INFS614, GMU, Dr. Brodsky, Lecture 3 10
Binary vs. Ternary Relationships (Contd.)
❖ Previous example illustrated a case when two
binary relationships were better than one ternary
relationship.
❖ An example in the other direction: a ternary
relation Contracts relates entity sets Parts,
Departments and Suppliers, and has descriptive
attribute qty. No combination of binary
relationships is an adequate substitute:
– S “can-supply” P, D “needs” P, and D “deals-with” S
does not imply that D has agreed to buy P from S.
– How do we record qty?
INFS614, GMU, Dr. Brodsky, Lecture 3 11
Summary of Conceptual Design
❖ Conceptual design follows requirements analysis,
– Yields a high-level description of data to be stored
❖ ER model popular for conceptual design
– Constructs are expressive, close to the way people think
about their applications.
❖ Basic constructs: entities, relationships, and attributes
(of entities and relationships).
❖ Some additional constructs: weak entities, ISA
hierarchies, and aggregation.
❖ Note: There are many variations on ER model.
INFS614, GMU, Dr. Brodsky, Lecture 3 12
Summary of ER (Contd.)
❖ Several kinds of integrity constraints can be expressed
in the ER model: key constraints, participation
constraints, and overlap/covering constraints for ISA
hierarchies. Some foreign key constraints are also
implicit in the definition of a relationship set.
– Some constraints (notably, functional dependencies) cannot be
expressed in the ER model.
– Constraints play an important role in determining the best
database design for an enterprise.
INFS614, GMU, Dr. Brodsky, Lecture 3 13
Summary of ER (Contd.)
❖ ER design is subjective. There are often many ways
to model a given scenario! Analyzing alternatives
can be tricky, especially for a large enterprise.
Common choices include:
– Entity vs. attribute, entity vs. relationship, binary or n-
ary relationship, whether or not to use ISA hierarchies,
and whether or not to use aggregation.
❖ Ensuring good database design: resulting
relational schema should be analyzed and refined
further. FD information and normalization
techniques are especially useful.
INFS614, GMU, Dr. Brodsky, Lecture 3 14
Relational Model
❖ Relational Model = Structure + Operations
– Structure: Relations (or Tables)
– Operations: Relational Algebra, SQL.
❖ Most widely implemented model.
– Vendors: IBM DB2, Microsoft SQL Server,
Oracle, etc.
❖ Our design+implementation approach:
Step 1: ER design (ERD)
Step 2: Translate to Relational (Relational Schema)
Step 3: Querying over the relational model
INFS614, GMU, Dr. Brodsky, Lecture 3 15
Relational Database: Definitions
❖ Relational database: a set of relations
❖ Relation: made up of 2 parts:
– Instance : a table, with rows and columns.
#Rows = cardinality, #fields = degree / arity.
– Schema : specifies name of relation, plus name and
type of each column.
◆ E.G. Students(sid: string, name: string, login: string,
age: integer, gpa: real).
❖ Can think of a relation as a set of rows or
tuples (i.e., all rows are distinct).
INFS614, GMU, Dr. Brodsky, Lecture 3 16
Example 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, all rows distinct
❖ Do all columns in a relation instance have to
be distinct?
INFS614, GMU, Dr. Brodsky, Lecture 3 17
Relational Query Languages
❖ A major strength of the relational model:
supports simple, powerful querying of data.
❖ Queries can be written intuitively, and the
DBMS is responsible for efficient evaluation.
– The key: precise semantics for relational queries.
– Allows the optimizer to extensively re-order
operations, and still ensure that the answer does
not change.
INFS614, GMU, Dr. Brodsky, Lecture 3 18
The SQL Query Language
❖ Developed by IBM (system R) in the 1970s
❖ Need for a standard since it is used by many
vendors
❖ Standards:
– SQL-86
– SQL-89 (minor revision)
– SQL-92 (major revision, current standard)
– SQL-99 (major extensions)
INFS614, GMU, Dr. Brodsky, Lecture 3 19
The SQL Query Language
❖ To find all 18 year old students, we can write:
SELECT *
FROM Students S
WHERE S.age=18
•To find just names and logins, replace the first line:
SELECT S.name, S.login
sid name login age gpa
53666 Jones jones@cs 18 3.4
53688 Smith smith@ee 18 3.2
INFS614, GMU, Dr. Brodsky, Lecture 3 20
Querying Multiple Relations
❖ What does the following query compute?
SELECT S.name, E.cid
FROM Students S, Enrolled E
WHERE S.sid=E.sid AND E.grade=“A”
S.name E.cid
Smith Topology112
sid cid grade
53831 Carnatic101 C
53831 Reggae203 B
53650 Topology112 A
53666 History105 B
Given the following instance
of Enrolled (is this possible if
the DBMS ensures referential
integrity?):
we get:
INFS614, GMU, Dr. Brodsky, Lecture 3 21
Creating Relations in SQL
❖ Creates the Students
relation. Observe that the
type (domain) of each field
is specified, and enforced by
the DBMS whenever tuples
are added or modified.
❖ As another example, the
Enrolled table holds
information about courses
that students take.
CREATE TABLE Students
(sid: CHAR(20),
name: CHAR(20),
login: CHAR(10),
age: INTEGER,
gpa: REAL)
CREATE TABLE Enrolled
(sid: CHAR(20),
cid: CHAR(20),
grade: CHAR(2))
INFS614, GMU, Dr. Brodsky, Lecture 3 22
Destroying and Altering Relations
❖ Destroys the relation Students. The schema
information and the tuples are deleted.
DROP TABLE Students
❖ The schema of Students is altered by adding a
new field; every tuple in the current instance
is extended with a null value in the new field.
ALTER TABLE Students
ADD COLUMN firstYear: integer
INFS614, GMU, Dr. Brodsky, Lecture 3 23
Adding and Deleting Tuples
❖ Can insert a single tuple using:
INSERT INTO Students (sid, name, login, age, gpa)
VALUES (53688, ‘Smith’, ‘smith@ee’, 18, 3.2)
❖ Can delete all tuples satisfying some
condition (e.g., name = Smith):
DELETE
FROM Students S
WHERE S.name = ‘Smith’
☛ Powerful variants of these commands are available; more later!
INFS614, GMU, Dr. Brodsky, Lecture 3 24
Integrity Constraints (ICs)
❖ IC: condition that must be true for any instance of the
database; e.g., domain constraints.
– ICs are specified when schema is defined.
– ICs are checked when relations are modified.
❖ A legal instance of a relation is one that satisfies all
specified ICs.
– DBMS should not allow illegal instances.
❖ If the DBMS checks ICs, stored data is more faithful to
real-world meaning.
– Avoids data entry errors, too!
INFS614, GMU, Dr. Brodsky, Lecture 3 25
Primary Key Constraints
❖ A set of fields is a (candidate) key for a
relation if :
1. No two distinct tuples can have same values in all
key fields, and
2. This is not true for any subset of the key.
– Part 2 false? A superkey.
– If there’s >1 candidate keys for a relation, one of
the keys is chosen (by DBA) to be the primary key.
❖ E.g., sid is a key for Students. (What about
name?) The set {sid, gpa} is a superkey.
INFS614, GMU, Dr. Brodsky, Lecture 3 26
Primary and Candidate Keys in SQL
❖ Possibly many candidate keys (specified using
UNIQUE), one of which is chosen as the primary key.
CREATE TABLE Enrolled
(sid CHAR(20)
cid CHAR(20),
grade CHAR(2),
PRIMARY KEY (sid,cid) )
❖ “For a given student and course,
there is a single grade.” vs.
“Students can take only one
course, and receive a single grade
for that course; further, no two
students in a course receive the
same grade.”
❖ Used carelessly, an IC can prevent
the storage of database instances
that arise in practice!
CREATE TABLE Enrolled
(sid CHAR(20)
cid CHAR(20),
grade CHAR(2),
PRIMARY KEY (sid),
UNIQUE (cid, grade) )
INFS614, GMU, Dr. Brodsky, Lecture 3 27
Foreign Keys, Referential Integrity
❖ Foreign key : Set of fields in one relation that is used
to `refer’ to a tuple in another relation. (Must
correspond to primary key of the second relation.)
Like a `logical pointer’.
❖ E.g. sid is a foreign key referring to Students:
– Enrolled(sid: string, cid: string, grade: string)
– If all foreign key constraints are enforced, referential
integrity is achieved, i.e., no dangling references.
– Can you name a data model w/o referential integrity?
◆ Links in HTML!
INFS614, GMU, Dr. Brodsky, Lecture 3 28
Foreign Keys in SQL
❖ Only students listed in the Students relation should
be allowed to enroll for courses.
CREATE TABLE Enrolled
(sid CHAR(20), cid CHAR(20), grade CHAR(2),
PRIMARY KEY (sid,cid),
FOREIGN KEY (sid) REFERENCES 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
sid cid grade
53666 Carnatic101 C
53666 Reggae203 B
53650 Topology112 A
53666 History105 B
Enrolled
Students
INFS614, GMU, Dr. Brodsky, Lecture 3 29
Enforcing Referential Integrity
❖ Consider Students and Enrolled; sid in Enrolled is a
foreign key that references Students.
❖ What should be done if an Enrolled tuple with a non-
existent student id is inserted? (Reject it!)
❖ What should be done if a Students tuple is deleted?
– Also delete all Enrolled tuples that refer to it.
– Disallow deletion of a Students tuple that is referred to.
– Set sid in Enrolled tuples that refer to it to a default sid.
– (In SQL, also: Set sid in Enrolled tuples that refer to it to a
special value null, denoting `unknown’ or `inapplicable’.)
❖ Similar if primary key of Students tuple is updated.
INFS614, GMU, Dr. Brodsky, Lecture 3 30
Referential Integrity in SQL/92
❖ SQL/92 supports all 4
options on deletes and
updates.
– Default is NO ACTION
(delete/update is rejected)
– CASCADE (also delete
all tuples that refer to
deleted tuple)
– SET NULL / SET DEFAULT
(sets foreign key value
of referencing tuple)
CREATE TABLE Enrolled
(sid CHAR(20),
cid CHAR(20),
grade CHAR(2),
PRIMARY KEY (sid,cid),
FOREIGN KEY (sid)
REFERENCES Students
ON DELETE CASCADE
ON UPDATE SET DEFAULT )
INFS614, GMU, Dr. Brodsky, Lecture 3 31
Where do ICs Come From?
❖ ICs are based upon the semantics of the real-world
enterprise that is being described in the database
relations.
❖ We can check a database instance to see if an IC is
violated, but we can NEVER infer that an IC is true
by looking at an instance.
– An IC is a statement about all possible instances!
– From example, we know name is not a key, but the assertion
that sid is a key is given to us.
❖ Key and foreign key ICs are the most common; more
general ICs supported too.
INFS614, GMU, Dr. Brodsky, Lecture 3 32
Logical DB Design: ER to Relational
❖ Entity sets to tables.
CREATE TABLE Employees
(ssn CHAR(11),
name CHAR(20),
lot INTEGER,
PRIMARY KEY (ssn))
Employees
ssn
name
lot
INFS614, GMU, Dr. Brodsky, Lecture 3 33
Relationship Sets to Tables
❖ In translating a relationship
set to a relation, attributes of
the relation must include:
– Keys for each
participating entity set
(as foreign keys).
◆ This set of attributes
forms a superkey for
the relation.
– All descriptive attributes.
CREATE TABLE Works_In(
ssn CHAR(11),
did INTEGER,
since DATE,
PRIMARY KEY (ssn, did),
FOREIGN KEY (ssn)
REFERENCES Employees,
FOREIGN KEY (did)
REFERENCES Departments)
INFS614, GMU, Dr. Brodsky, Lecture 3 34
Review: Key Constraints
❖ Each dept has at
most one manager,
according to the
key constraint on
Manages.
Translation to
relational model?
Many-to-Many1-to-1 1-to Many Many-to-1
dname
budgetdid
since
lot
name
ssn
ManagesEmployees Departments
INFS614, GMU, Dr. Brodsky, Lecture 3 35
Translating ER Diagrams with Key Constraints
❖ Map relationship to a
table:
– Note that did is
the key now!
– Separate tables for
Employees and
Departments.
❖ Since each
department has a
unique manager, we
could instead
combine Manages
and Departments.
CREATE TABLE Manages(
ssn CHAR(11),
did INTEGER,
since DATE,
PRIMARY KEY (did),
FOREIGN KEY (ssn) REFERENCES Employees,
FOREIGN KEY (did) REFERENCES Departments)
CREATE TABLE Dept_Mgr(
did INTEGER,
dname CHAR(20),
budget REAL,
ssn CHAR(11),
since DATE,
PRIMARY KEY (did),
FOREIGN KEY (ssn) REFERENCES Employees)
INFS614, GMU, Dr. Brodsky, Lecture 3 36
Review: Participation Constraints
❖ Does every department have a manager?
– If so, this is a participation constraint: the participation of
Departments in Manages is said to be total (vs. partial).
◆ Every did value in Departments table must appear in a
row of the Manages table (with a non-null ssn value!)
lot
name dname
budgetdid
since
name dname
budgetdid
since
Manages
since
DepartmentsEmployees
ssn
Works_In
INFS614, GMU, Dr. Brodsky, Lecture 3 37
Participation Constraints in SQL
❖ We can capture participation constraints involving
one entity set in a binary relationship, but little else
(without resorting to CHECK constraints).
CREATE TABLE Dept_Mgr(
did INTEGER,
dname CHAR(20),
budget REAL,
ssn CHAR(11) NOT NULL,
since DATE,
PRIMARY KEY (did),
FOREIGN KEY (ssn) REFERENCES Employees,
ON DELETE NO ACTION)
INFS614, GMU, Dr. Brodsky, Lecture 3 38
Review: Weak Entities
❖ A weak entity can be identified uniquely only by
considering the primary key of another (owner) entity.
– Owner entity set and weak entity set must participate in a
one-to-many relationship set (1 owner, many weak entities).
– Weak entity set must have total participation in this
identifying relationship set.
lot
name
agepname
DependentsEmployees
ssn
Policy
cost
INFS614, GMU, Dr. Brodsky, Lecture 3 39
Translating Weak Entity Sets
❖ Weak entity set and identifying relationship
set are translated into a single table.
– When the owner entity is deleted, all owned weak
entities must also be deleted.
CREATE TABLE Dep_Policy (
pname CHAR(20),
age INTEGER,
cost REAL,
ssn CHAR(11) NOT NULL,
PRIMARY KEY (pname, ssn),
FOREIGN KEY (ssn) REFERENCES Employees,
ON DELETE CASCADE)
INFS614, GMU, Dr. Brodsky, Lecture 3 40
Review: ISA Hierarchies
Contract_Emps
name
ssn
Employees
lot
hourly_wages
ISA
Hourly_Emps
contractid
hours_worked
❖As in C++, or other PLs,
attributes are inherited.
❖If we declare A ISA B, every A
entity is also considered to be a B
entity.
❖ Overlap constraints: Can Joe be an Hourly_Emps as well as
a Contract_Emps entity? (Allowed/disallowed)
❖ Covering constraints: Does every Employees entity also have
to be an Hourly_Emps or a Contract_Emps entity? (Yes/no)
INFS614, GMU, Dr. Brodsky, Lecture 3 41
Translating ISA Hierarchies to Relations
❖ General approach:
– 3 relations: Employees, Hourly_Emps and Contract_Emps.
◆ Hourly_Emps: Every employee is recorded in
Employees. For hourly emps, extra info recorded in
Hourly_Emps (hourly_wages, hours_worked, ssn); must
delete Hourly_Emps tuple if referenced Employees
tuple is deleted).
◆ Queries involving all employees easy, those involving
just Hourly_Emps require a join to get some attributes.
❖ Alternative: Just Hourly_Emps and Contract_Emps.
– Hourly_Emps: ssn, name, lot, hourly_wages, hours_worked.
– Each employee must be in one of these two subclasses.
INFS614, GMU, Dr. Brodsky, Lecture 3 42
Review: Binary vs. Ternary
Relationships
❖ If each policy is
owned by just 1
employee:
– Key constraint
on Policies
would mean
policy can only
cover 1
dependent!
❖ What are the
additional
constraints in the
2nd diagram?
agepname
DependentsCovers
name
Employees
ssn lot
Policies
policyid cost
Beneficiary
agepname
Dependents
policyid cost
Policies
Purchaser
name
Employees
ssn lot
Bad design
Better design
INFS614, GMU, Dr. Brodsky, Lecture 3 43
Binary vs. Ternary Relationships (Contd.)
❖ The key
constraints allow
us to combine
Purchaser with
Policies and
Beneficiary with
Dependents.
❖ Participation
constraints lead to
NOT NULL
constraints.
❖ What if Policies is
a weak entity set?
CREATE TABLE Policies (
policyid INTEGER,
cost REAL,
ssn CHAR(11) NOT NULL,
PRIMARY KEY (policyid).
FOREIGN KEY (ssn) REFERENCES Employees,
ON DELETE CASCADE)
CREATE TABLE Dependents (
pname CHAR(20),
age INTEGER,
policyid INTEGER,
PRIMARY KEY (pname, policyid).
FOREIGN KEY (policyid) REFERENCES Policies,
ON DELETE CASCADE)
INFS614, GMU, Dr. Brodsky, Lecture 3 44
Relational Model: Summary
❖ A tabular representation of data.
❖ Simple and intuitive, currently the most widely used.
❖ Integrity constraints can be specified by the DBA,
based on application semantics. DBMS checks for
violations.
– Two important ICs: primary and foreign keys
– In addition, we always have domain constraints.
❖ Powerful and natural query languages exist.
❖ Rules to translate ER to relational model