Database Management

Systems

UNIT-1: INTRODUCTION TO
DATABASE SYSTEM AND ER
MODELING

RAHUL A PATIL
DEPARTMENT OF COMPUTER ENGINEERING
PIMPRI CHINCHWAD COLLEGE OF ENGINEERING, PUNE.
 Data Models
 A collection of tools for describing
 Data
 Data relationships
 Data semantics
 Data constraints

 Relational model
 Entity-Relationship data model (mainly for database design)
 Object-based data models (Object-oriented and Object-relational)
 Semistructured data model (XML)
 Other older models:
 Network model
 Hierarchical model
A Sample Relational
Database
 Object-Relational
Data Models
 Relational model: flat, ―atomic‖ values
 Object Relational Data Models
 Extend the relational data model by including object orientation
and constructs to deal with added data types.
 Allow attributes of tuples to have complex types, including non-
atomic values such as nested relations.
 Preserve relational foundations, in particular the declarative
access to data, while extending modeling power.
 Provide upward compatibility with existing relational languages.
Entity-Relationship Model
 Design Approaches
 Need to come up with a methodology to ensure that each of the
relations in the database is ―good‖
 Two ways of doing so:
 Entity Relationship Model
Models an enterprise as a collection of entities and
relationships
Represented diagrammatically by an entity-relationship
diagram:
 Normalization Theory
Formalize what designs are bad, and test for them
 Design Phases

 The initial phase of database design is to characterize fully the

data needs of the prospective database users.
 Next, the designer chooses a data model and, by applying the
concepts of the chosen data model, translates these
requirements into a conceptual schema of the database.
 A fully developed conceptual schema also indicates the
functional requirements of the enterprise. In a ―specification of
functional requirements‖, users describe the kinds of operations
(or transactions) that will be performed on the data.
 Design Phases (Cont.)

The process of moving from an abstract data model to the

implementation of the database proceeds in two final design
phases.

 Logical Design – Deciding on the database schema.

Database design requires that we find a ―good‖ collection
of relation schemas.
 Business decision – What attributes should we record
in the database?
 Computer Science decision – What relation schemas
should we have and how should the attributes be
distributed among the various relation schemas?
 Physical Design – Deciding on the physical layout of the
database
Outline of the ER Model
 Entity-Relationship Model

Entity Sets
Relationship Sets
Design Issues
Mapping Constraints
Keys
E-R Diagram
Extended E-R Features
Design of an E-R Database Schema
Reduction of an E-R Schema to Tables
 Entity Sets

A database can be modeled as:

a collection of entities,
relationship among entities.
An entity is an object that exists and is
distinguishable from other objects.
Example: specific person, company, event, plant
Entities have attributes
Example: people have names and addresses
An entity set is a set of entities of the same type
that share the same properties.
Example: set of all persons, companies, trees, holidays
 Entity Sets customer and
loan

customer-id customer- customer- customer- loan- amount

name street city number
 Attributes

An entity is represented by a set of attributes, that is

descriptive properties possessed by all members of an entity
set.
Example:
customer = (customer-id, customer-name,
customer-street, customer-city)
loan = (loan-number, amount)
Domain – the set of permitted values for each attribute
Attribute types:
Simple and composite attributes.
Single-valued and multi-valued attributes
 E.g. multivalued attribute: phone-numbers
Derived attributes
 Can be computed from other attributes
 E.g. age, given date of birth
Composite Attributes
 Relationship Sets

A relationship is an association among several entities

Example:
Hayes depositor A-102
customer entity relationship set account entity
A relationship set is a mathematical relation among n
 2 entities, each taken from entity sets
{(e1, e2, … en) | e1  E1, e2  E2, …, en  En}

where (e1, e2, …, en) is a relationship

Example:
(Hayes, A-102)  depositor
Relationship Set borrower
 Relationship Sets (Cont.)

An attribute can also be property of a relationship set.

For instance, the depositor relationship set between entity sets
customer and account may have the attribute access-date
 Degree of a Relationship Set

Refers to number of entity sets that participate in a

relationship set.
Relationship sets that involve two entity sets are binary (or
degree two). Generally, most relationship sets in a database
system are binary.
Relationship sets may involve more than two entity sets.
E.g. Suppose employees of a bank may have jobs
(responsibilities) at multiple branches, with different jobs
at different branches. Then there is a ternary relationship
set between entity sets employee, job and branch
Relationships between more than two entity sets are rare.
Most relationships are binary. (More on this later.)
 Mapping Cardinalities

Express the number of entities to which another

entity can be associated via a relationship set.
Most useful in describing binary relationship sets.
For a binary relationship set the mapping
cardinality must be one of the following types:
One to one
One to many
Many to one
Many to many
 Mapping Cardinalities

One to one One to many

Note: Some elements in A and B may not be mapped to any
elements in the other set
 Mapping Cardinalities

Many to one Many to many

Note: Some elements in A and B may not be mapped to any
elements in the other set
 Mapping Cardinalities affect
ER Design

 Can make access-date an attribute of account, instead of a

relationship attribute, if each account can have only one customer
 I.e., the relationship from account to customer is many to one,
or equivalently, customer to account is one to many
 E-R Diagrams

 Rectangles represent entity sets.

 Diamonds represent relationship sets.
 Lines link attributes to entity sets and entity sets to relationship sets.
 Ellipses represent attributes
 Double ellipses represent multivalued attributes.
 Dashed ellipses denote derived attributes.
 Underline indicates primary key attributes (will study later)
 E-R Diagram With Composite,
Multivalued, and Derived Attributes
Relationship Sets
with Attributes
 Roles

Entity sets of a relationship need not be distinct

The labels ―manager‖ and ―worker‖ are called roles; they specify how
employee entities interact via the works-for relationship set.
Roles are indicated in E-R diagrams by labeling the lines that connect
diamonds to rectangles.
Role labels are optional, and are used to clarify semantics of the
relationship
 Cardinality Constraints

We express cardinality constraints by drawing either a

directed line (), signifying ―one,‖ or an undirected
line (—), signifying ―many,‖ between the relationship
set and the entity set.
E.g.: One-to-one relationship:
A customer is associated with at most one loan via the relationship
borrower
A loan is associated with at most one customer via borrower
 One-To-Many Relationship

In the one-to-many relationship a loan is

associated with at most one customer via
borrower, a customer is associated with several
(including 0) loans via borrower
 Many-To-One Relationships

In a many-to-one relationship a loan is associated

with several (including 0) customers via
borrower, a customer is associated with at most
one loan via borrower
 Many-To-Many Relationship

A customer is associated with several (possibly 0) loans

via borrower
A loan is associated with several (possibly 0) customers
via borrower
 Participation of an Entity Set in
a Relationship Set

 Total participation (indicated by double line): every entity in the entity

set participates in at least one relationship in the relationship set
 E.g. participation of loan in borrower is total
 every loan must have a customer associated to it via borrower
 Partial participation: some entities may not participate in any
relationship in the relationship set
 E.g. participation of customer in borrower is partial
 Alternative Notation
for Cardinality Limits

 Cardinality limits can also express participation constraints

 Keys

A super key of an entity set is a set of one or

more attributes whose values uniquely
determine each entity.
A candidate key of an entity set is a minimal
super key
Customer-id is candidate key of customer
account-number is candidate key of account
Although several candidate keys may exist, one
of the candidate keys is selected to be the
primary key.
 Keys for Relationship Sets

The combination of primary keys of the

participating entity sets forms a super key of a
relationship set.
(customer-id, account-number) is the super key of depositor
NOTE: this means a pair of entity sets can have at most one
relationship in a particular relationship set.
 E.g. if we wish to track all access-dates to each account by
each customer, we cannot assume a relationship for each
access. We can use a multivalued attribute though
E-R Diagram with a Ternary Relationship
 Design Issues

Use of entity sets vs. attributes

Employee (e_name, Telephone_no)
Choice mainly depends on the structure of the enterprise being modeled, and on the semantics
associated with the attribute in question.
Use of entity sets vs. relationship sets
Loan (no, amt) as relation ship between customer and branch
Several customers holds one loan??
Loan is an entity set
Possible guideline is to designate a relationship set to describe an action that occurs between
entities
Binary versus n-ary relationship sets
Although it is possible to replace any nonbinary (n-ary, for n > 2) relationship set by a number
of distinct binary relationship sets, a n-ary relationship set shows more clearly that several
entities participate in a single relationship.
Placement of relationship attributes
Customer – Depositor – Account
One to many – keep at many side or relationship same meaning
One to one – either of entities
May to many -relationship
 Weak Entity Sets
An entity set that does not have a primary key is
referred to as a weak entity set.
The existence of a weak entity set depends on the
existence of a identifying entity set
it must relate to the identifying entity set via a total, one-to-many
relationship set from the identifying to the weak entity set
Identifying relationship depicted using a double diamond
The discriminator (or partial key) of a weak entity set
is the set of attributes that distinguishes among all the
entities of a weak entity set.
The primary key of a weak entity set is formed by the
primary key of the strong entity set on which the weak
entity set is existence dependent, plus the weak entity
set’s discriminator.
 Weak Entity Sets (Cont.)

We depict a weak entity set by double rectangles.

We underline the discriminator of a weak entity set with a dashed
line.
payment-number – discriminator of the payment entity set
Primary key for payment – (loan-number, payment-number)
 Weak Entity Sets (Cont.)

Note: the primary key of the strong entity set is not

explicitly stored with the weak entity set, since it is
implicit in the identifying relationship.
If loan-number were explicitly stored, payment could
be made a strong entity, but then the relationship
between payment and loan would be duplicated by an
implicit relationship defined by the attribute loan-
number common to payment and loan
 More Weak Entity Set Examples

In a university, a course is a strong entity and a course-

offering can be modeled as a weak entity
The discriminator of course-offering would be semester
(including year) and section-number (if there is more than
one section)
If we model course-offering as a strong entity we would
model course-number as an attribute.
Then the relationship with course would be implicit in the
course-number attribute
 Specialization

Top-down design process; we designate subgroupings

within an entity set that are distinctive from other
entities in the set.
These subgroupings become lower-level entity sets that
have attributes or participate in relationships that do
not apply to the higher-level entity set.
Depicted by a triangle component labeled ISA (E.g.
customer ―is a‖ person).
Attribute inheritance – a lower-level entity set
inherits all the attributes and relationship participation
of the higher-level entity set to which it is linked.
Specialization Example
 Generalization

A bottom-up design process – combine a number

of entity sets that share the same features into a
higher-level entity set.
Specialization and generalization are simple
inversions of each other; they are represented in
an E-R diagram in the same way.
The terms specialization and generalization are
used interchangeably.
 Specialization and
Generalization (Contd.)

Can have multiple specializations of an entity set based

on different features.
E.g. permanent-employee vs. temporary-employee, in
addition to officer vs. secretary vs. teller
Each particular employee would be
a member of one of permanent-employee or temporary-employee,
and also a member of one of officer, secretary, or teller
The ISA relationship also referred to as superclass -
subclass relationship
 Design Constraints on a
Specialization/Generalization

Constraint on which entities can be members of a

given lower-level entity set.
condition-defined
 E.g. all customers over 65 years are members of senior-
citizen entity set; senior-citizen ISA person.
user-defined
Constraint on whether or not entities may belong to
more than one lower-level entity set within a single
generalization.
Disjoint
 an entity can belong to only one lower-level entity set
 Noted in E-R diagram by writing disjoint next to the ISA
triangle
Overlapping
 an entity can belong to more than one lower-level entity set
 Design Constraints on a
Specialization/Generalization
(Contd.)

Completeness constraint -- specifies whether or not an

entity in the higher-level entity set must belong to at
least one of the lower-level entity sets within a
generalization.
total : an entity must belong to one of the lower-level entity sets
partial: an entity need not belong to one of the lower-level entity
sets
 Aggregation

 Consider the ternary relationship works-on, which we saw earlier

 Suppose we want to record managers for tasks performed by an

employee at a branch
 Aggregation (Cont.)

Relationship sets works-on and manages represent

overlapping information
Every manages relationship corresponds to a works-on relationship
However, some works-on relationships may not correspond to any
manages relationships
 So we can’t discard the works-on relationship
Eliminate this redundancy via aggregation
Treat relationship as an abstract entity
Allows relationships between relationships
Abstraction of relationship into new entity
Without introducing redundancy, the following diagram
represents:
An employee works on a particular job at a particular branch
An employee, branch, job combination may have an associated manager
E-R Diagram With Aggregation
 E-R Design Decisions

The use of an attribute or entity set to represent an

object.
Whether a real-world concept is best expressed by an
entity set or a relationship set.
The use of a ternary relationship versus a pair of binary
relationships.
The use of a strong or weak entity set.
The use of specialization/generalization – contributes to
modularity in the design.
The use of aggregation – can treat the aggregate entity
set as a single unit without concern for the details of its
internal structure.
 Database Design Banking
Enterprise

 The initial specification of user requirements may

be based on interviews with the database users,
and on the designer’s own analysis of the
enterprise. The description that arises from this
design phase serves as the basis for specifying the
conceptual structure of the database. Here are the
major characteristics of the banking enterprise.
 Database Design Banking
Enterprise

 The bank is organized into branches. Each branch is located in

a particular city and is identified by a unique name. The bank
monitors the assets of each branch.
 Bank customers are identified by their customer-id values. The
bank stores each customer’s name, and the street and city
where the customer lives. Customers may have accounts and
can take out loans. A customer may be associated with a
particular banker, who may act as a loan officer or personal
banker for that customer.
 Bank employees are identified by their employee-id values. The
bank administration stores the name and telephone number of
each employee, the names of the employee’s dependents, and
the employee-id number of the employee’s manager. The bank
also keeps track of the employee’s start date and, thus, length
of employment.
 Database Design Banking
Enterprise

 The bank offers two types of accounts—savings and checking

accounts. Accounts can be held by more than one customer, and a
customer can have more than one account. Each account is
assigned a unique account number. The bank maintains a record
of each account’s balance, and the most recent date on which the
account was accessed by each customer holding the account. In
addition, each savings account has an interest rate, and overdrafts
are recorded for each checking account.
 A loan originates at a particular branch and can be held by one or
more customers. A loan is identified by a unique loan number. For
each loan, the bank keeps track of the loan amount and the loan
payments. Although a loanpayment number does not uniquely
identify a particular payment among those for all the bank’s loans,
a payment number does identify a particular payment for a
specific loan. The date and amount are recorded for each payment.
 Database Design Banking
Enterprise

 In a real banking enterprise, the bank would keep track

of deposits and withdrawals from savings and checking
accounts, just as it keeps track of payments to loan
accounts. Since the modeling requirements for that
tracking are similar, and we would like to keep our
example application small, we do not keep track of such
deposits and withdrawals in our model.
 E-R Diagram for a
Banking Enterprise
How about doing another ER design
interactively on the board?
Summary of Symbols Used
in E-R Notation
Summary of Symbols (Cont.)
Alternative E-R Notations
Notation to Express Entity with Complex
Attributes
E-R Diagram for a University Enterprise
Reduction of an E-R Schema to Tables

Primary keys allow entity sets and relationship

sets to be expressed uniformly as tables which
represent the contents of the database.
A database which conforms to an E-R diagram can be
represented by a collection of tables.
For each entity set and relationship set there is
a unique table which is assigned the name of the
corresponding entity set or relationship set.
Each table has a number of columns (generally
corresponding to attributes), which have unique
names.
Converting an E-R diagram to a table format is the
basis for deriving a relational database design from an
E-R diagram.
 Representing Entity Sets as Tables

A strong entity set reduces to a table with the same attributes.

 Composite and Multivalued
Attributes

Composite attributes are flattened out by creating a

separate attribute for each component attribute
E.g. given entity set customer with composite attribute name with
component attributes first-name and last-name the table corresponding
to the entity set has two attributes
name.first-name and name.last-name
A multivalued attribute M of an entity E is represented by
a separate table EM
Table EM has attributes corresponding to the primary key of E and an
attribute corresponding to multivalued attribute M
E.g. Multivalued attribute dependent-names of employee is represented
by a table
employee-dependent-names( employee-id, dname)
Each value of the multivalued attribute maps to a separate row of the
table EM
 E.g., an employee entity with primary key John and
dependents Johnson and Johndotir maps to two rows:
(John, Johnson) and (John, Johndotir)
 Representing Weak Entity Sets

 A weak entity set becomes a table that includes a column for

the primary key of the identifying strong entity set
 Representing Relationship Sets as Tables

A many-to-many relationship set is represented as a table with

columns for the primary keys of the two participating entity sets,
and any descriptive attributes of the relationship set.
E.g.: table for relationship set borrower
 Redundancy of Tables

 Many-to-one and one-to-many relationship sets that are total

on the many-side can be represented by adding an extra
attribute to the many side, containing the primary key of the
one side
 E.g.: Instead of creating a table for relationship account-
branch, add an attribute branch to the entity set account
 Redundancy of Tables (Cont.)

For one-to-one relationship sets, either side can be

chosen to act as the ―many‖ side
That is, extra attribute can be added to either of the tables
corresponding to the two entity sets
If participation is partial on the many side,
replacing a table by an extra attribute in the
relation corresponding to the ―many‖ side could
result in null values
The table corresponding to a relationship set
linking a weak entity set to its identifying strong
entity set is redundant.
E.g. The payment table already contains the information that
would appear in the loan-payment table (i.e., the columns
loan-number and payment-number).
 Representing Specialization as Tables

Method 1:
Form a table for the higher level entity
Form a table for each lower level entity set, include primary key of higher
level entity set and local attributes

table table attributes

person name, street, city
customer name, credit-rating
employee name, salary
Drawback: getting information about, e.g., employee requires accessing
two tables
 Representing Specialization as Tables
(Cont.)

Method 2:
Form a table for each entity set with all local and inherited attributes
table table attributes
person name, street, city
customer name, street, city, credit-rating
employee name, street, city, salary

If specialization is total, table for generalized entity (person) not required

to store information
 Can be defined as a ―view‖ relation containing union of
specialization tables
 But explicit table may still be needed for foreign key constraints
Drawback: street and city may be stored redundantly for persons who
are both customers and employees
 Relations Corresponding to Aggregation

 To represent aggregation, create a table containing

 primary key of the aggregated relationship,
 the primary key of the associated entity set
 Any descriptive attributes
 Relations Corresponding to Aggregation (Cont.)

 E.g. to represent aggregation manages between relationship

works-on and entity set manager, create a table
manages(employee-id, branch-name, title, manager-name)
 Table works-on is redundant provided we are willing to
store null values for attribute manager-name in table
manages
E-R Diagram for Exercise 2.10
End of Unit – I
 Thank You

RAHUL A PATIL
DEPARTMENT OF COMPUTER ENGINEERING
PIMPRI CHINCHWAD COLLEGE OF ENGINEERING, PUNE.

PATIL.RAHUL3068@GMAIL.COM