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Chapter 6: Entity-Relationship Model
Database System Concepts, 5th Ed.
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Database System Concepts
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Chapter 1: Introduction
Part 1: Relational databases
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Chapter 2: Relational Model

Chapter 3: SQL

Chapter 4: Advanced SQL

Chapter 5: Other Relational Languages
Part 2: Database Design
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Chapter 6: Database Design and the E-R Model
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Chapter 7: Relational Database Design

Chapter 8: Application Design and Development
Part 3: Object-based databases and XML

Chapter 9: Object-Based Databases
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Chapter 10: XML
Part 4: Data storage and querying

Chapter 11: Storage and File Structure
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Chapter 12: Indexing and Hashing

Chapter 13: Query Processing

Chapter 14: Query Optimization
Part 5: Transaction management

Chapter 15: Transactions
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Chapter 16: Concurrency control
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Chapter 17: Recovery System
Database System Concepts - 5th Edition, July 11, 2005
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Part 6: Data Mining and Information Retrieval
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Chapter 18: Data Analysis and Mining
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Chapter 19: Information Retreival
Part 7: Database system architecture
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Chapter 20: Database-System Architecture
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Chapter 21: Parallel Databases
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Chapter 22: Distributed Databases
Part 8: Other topics

Chapter 23: Advanced Application Development
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Chapter 24: Advanced Data Types and New Applications
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Chapter 25: Advanced Transaction Processing
Part 9: Case studies
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Chapter 26: PostgreSQL
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Chapter 27: Oracle
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Chapter 28: IBM DB2
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Chapter 29: Microsoft SQL Server
Online Appendices
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Appendix A: Network Model
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Appendix B: Hierarchical Model
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Appendix C: Advanced Relational Database Model
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Part 2: Database Design
(Chapters 6 through 8).
 Chapter 6: Database Design and the E-R Model

provides an overview of the database-design process, with major emphasis
on database design using the entity-relationship data model. UML classdiagram notation is also covered in this chapter.
 Chapter 7: Relational Database Design

introduces the theory of relational-database design. The theory of functional
dependencies and normalization is covered, with emphasis on the motivation
and intuitive understanding of each normal form. Instructors may chose to
use only this initial coverage in Sections 7.1 through 7.3 without loss of
continuity.
 Chapter 8: Application Design and Development

emphasizes the construction of database applications with Web-based used
interfaces. In addition, the chapter covers application security.
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 ER Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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The Design Process
 Creating a database application

Design of the database schema
 Design of the programs that access and update the data

Design of a security scheme to control access to data
 The phases of the database design

Understanding the needs of users and enterprises

Conceptual design using the abstract model like ER Model
 Specification of functional requirements (operations & transactions)
 Converting the abstract model to implementation details
 Logical design phase: convert ER to Tables
 Physical design phase: file organization, storage stucture
 Design alternatives for avoiding two major pitfalls

Redundancy
 Imcompleteness
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 ER Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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Modeling
 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
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Entity Sets customer and loan
customer_id
customer_
name
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customer_
street
customer_
city
6.8
loan_
number
amount
©Silberschatz, Korth and Sudarshan
Relationship Sets
 A relationship is an association among several entities
Example:
Hayes
customer entity
depositor
relationship set
A-102
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
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Relationship Set borrower
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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
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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.
Example: 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
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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
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Single-valued and multi-valued attributes

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Example: multi-valued attribute: phone_numbers
Derived attributes

Can be computed from other attributes

Example: age (derived from the given attribute date_of_birth)
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Composite Attributes
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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Mapping Cardinality Constraints
 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
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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
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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
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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.
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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.

Example: 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.
 Must consider the mapping cardinality of the relationship set when deciding the
what are the candidate keys
 Need to consider semantics of relationship set in selecting the primary key in
case of more than one candidate key
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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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)
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E-R Diagram With Composite, Multivalued, and
Derived Attributes
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Relationship Sets with Attributes
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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
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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.
 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
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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
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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
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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
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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

Example: participation of customer in borrower is partial
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Alternative Notation for Cardinality Limits
 Cardinality limits can also express participation constraints
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E-R Diagram with a Ternary Relationship
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Cardinality Constraints on Ternary Relationship
 We allow at most one arrow out of a ternary (or greater degree) relationship to
indicate a cardinality constraint

E.g. an arrow from works_on to job indicates each employee works on at
most one job at any branch.
 If there is more than one arrow, there are two ways of defining the meaning.

E.g a ternary relationship R between A, B and C with arrows to B and C could
mean
1. each A entity is associated with a unique entity from B and C or
2. each pair of entities from (A, B) is associated with a unique C entity,
and each pair (A, C) is associated with a unique B

Each alternative has been used in different formalisms

To avoid this confusion we outlaw more than one arrow in the ternary relation
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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Design Issues
 Use of entity sets vs. attributes
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
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
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Binary vs. Non-Binary Relationships
 Some relationships that appear to be non-binary may be better represented using
binary relationships

E.g. A ternary relationship parents, relating a child to his/her father and
mother, is best replaced by two binary relationships, father and mother


Using two binary relationships allows partial information (e.g. only mother
being know)
But there are some relationships that are naturally non-binary

Example: works_on
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Converting Non-Binary Relationships to Binary Form
 In general, any non-binary relationship can be represented using binary
relationships by creating an artificial entity set.

Replace R between entity sets A, B and C by an entity set E, and three
relationship sets:
1. RA, relating E and A
2.RB, relating E and B
3. RC, relating E and C

Create a special identifying attribute for E

Add any attributes of R to E

For each relationship (ai , bi , ci) in R, create
1. a new entity ei in the entity set E
2. add (ei , ai ) to RA
3. add (ei , bi ) to RB
4. add (ei , ci ) to RC
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Converting Non-Binary Relationships (Cont.)
 Also need to translate constraints

Translating all constraints may not be possible

There may be instances in the translated schema that cannot correspond to
any instance of R

Exercise: add constraints to the relationships RA, RB and RC to ensure
that a newly created entity corresponds to exactly one entity in each of
entity sets A, B and C
 We can avoid creating an identifying attribute by making E a weak entity set
(described shortly) identified by the three relationship sets
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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

That is, the relationship from account to customer is many to one, or equivalently,
customer to account is one to many
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How about doing an ER design
interactively on the board?
Suggest an application to be modeled.
Database System Concepts, 5th Ed.
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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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

+ the weak entity set’s discriminator.
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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)
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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 in the payment entity, 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

i.e. the loan_payment relationship may not be necessary

But if we drop the loan_payment relationship. We cannot express the
existence dependency
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More Weak Entity Set Examples
 In a university database, a course is a strong entity and a course_offering can be
modeled as a weak entity
 Course entity would have course_number attribute as a primary key
 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 of course_offering.
 Then the relationship with course would be implicit in the course_number
attribute
 Then existence dependency cannot be expressed
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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Extended E-R Features: 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.
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Specialization Example
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Extended ER Features: Generalization
 A bottom-up design process

combine a number of entity sets that share the same features into a higherlevel 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.
 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
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Design Constraints on a
Specialization/Generalization
 Constraint on which entities can be members of a given lower-level entity set.

condition-defined
 Example: all customers over 65 years are members of senior-citizen
entity set; senior-citizen ISA person.
 user-defined
 User moves the entities at his will
 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
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Design Constraints on a
Specialization/Generalization (Cont.)
 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
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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
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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
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E-R Diagram With Aggregation
High-level abstract entity set “work-on”
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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.

Noun  entity set

Verb  relatiionship set

Adjective  attribute
 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.
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Other Aspects of Database Design
 6.11 UML
 6.12 Summary
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E-R Diagram for a Banking Enterprise
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How about doing another ER design
interactively on the board?
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Summary of Symbols Used in E-R Notation
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Summary of Symbols (Cont.)
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Other Aspects of Database Design
 6.11 UML
 6.12 Summary
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Reduction to Relation Schemas
 Primary keys allow entity sets and relationship sets to be expressed uniformly as
relation schemas that represent the contents of the database.
 A database which conforms to an E-R diagram can be represented by a
collection of schemas.
 For each entity set and relationship set

there is a unique schema that is assigned the name of the corresponding
entity set or relationship set.
 Each schema has a number of columns (generally corresponding to attributes),
which have unique names.
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Representing Entity Sets as Schemas
 A strong entity set reduces to a schema with the same attributes.
 A weak entity set becomes a table that includes a column for the primary
key of the identifying strong entity set
 In the example of loan-payment ER diagram
payment = ( loan_number, payment_number, payment_date, payment_amount )
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Representing Relationship Sets as Schemas
 A many-to-many relationship set is represented as a schema with
attributes for the primary keys of the two participating entity sets, and
any descriptive attributes of the relationship set.
 Example: schema for relationship set borrower
borrower = (customer_id, loan_number )

customer_id from customer entity

loan_number from loan entity
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Redundancy of Schemas
 We can reduce some tables for relationship sets
 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
 Example: Instead of creating a schema for relationship set account_branch,
add an attribute branch_name to the schema arising from entity set account
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Redundancy of Schemas (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 schema by an extra attribute
in the schema corresponding to the “many” side could result in null values
 The schema corresponding to a relationship set linking a weak entity set to its
identifying strong entity set is redundant.
 Example: The payment schema already contains the attributes that would appear
in the loan_payment schema (i.e., loan_number and payment_number).
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Composite and Multivalued Attributes
 Composite attributes are flattened out by creating a separate attribute for each
component attribute

Example: given entity set customer with composite attribute name with
component attributes first_name and last_name the schema 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 schema EM

Schema EM has attributes corresponding to the primary key of E and an
attribute corresponding to multivalued attribute M

Example: Multivalued attribute dependent_names of employee is represented
by a schema:
employee_dependent_names = ( employee_id, dname)

Each value of the multivalued attribute maps to a separate tuple of the relation
on schema EM

For example, an employee entity with primary key 123-45-6789 and
dependents Jack and Jane maps to two tuples:
(123-45-6789 , Jack) and (123-45-6789 , Jane)
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Representing Specialization via Schemas
 Method 1:

Form a schema for the higher-level entity

Form a schema for each lower-level entity set, include only primary key of
higher-level entity set and local attributes
schema
person
customer
employee

attributes
name, street, city
name, credit_rating
name, salary
Drawback: getting information about an employee requires accessing two
relations, the one corresponding to the low-level schema and the one
corresponding to the high-level schema
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Representing Specialization as Schemas (Cont.)
 Method 2:

Form a schema for each entity set with all local and inherited attributes
schema
person
customer
employee


attributes
name, street, city
name, street, city, credit_rating
name, street, city, salary
If specialization is total, the schema for the generalized entity set (person) not
required to store information

Can be defined as a “view” relation containing union of specialization
relations

But explicit schema may still be needed for foreign key constraints
Drawback: street and city may be stored redundantly for people who are
both customers and employees
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Schemas Corresponding to Aggregation
•
To represent aggregation, create a relation schema containing

primary key of the aggregated relationship,
 the primary keys of the associated entity sets
 Any descriptive arributes

For example, to represent aggregation “manages” between relationship works_on and
entity set manager, create a schema
manages (employee_id, branch_name, job_title, manager_name)

Schema works_on is redundant provided we are willing to store null values for attribute
manager_name in relation on schema manages
works_on
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Other Aspects of Database Design
 6.11 UML
 6.12 Summary
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Other Aspects of Database Design
 Data Constraints and Relational Database Design
 Usage Requirements: Queries, Performance

Throughput

Response time
 Authorization Requirements

Creating views

Control user’s accessibility to views and relations
 Data Flow, Work Flow

Specification of a series of queries and updates for accomplishing tasks
 Schema updates rarely happen, but are very hectic!
 Data Conversion is necessary in interacting multiple databases
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Other Aspects of Database Design
 6.11 UML
 6.12 Summary
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UML
 UML: Unified Modeling Language
 Standard by OMG (Object Management Group)

Long history of various modeling languages
Rambaugh’s OMT-1, OMT-2
 Booch92 Method
 Jacobson OOSE Method
 Now a “de facto” standard
 UML has many components to graphically model different aspects of an entire
software system
 UML Class Diagrams correspond to E-R Diagram, but several differences
 Various Application besides Database Modeling
 Software design
 Hardware design


Computer hardware
Ship building
 Mechanical design
 …….

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Summary of UML Class Diagram Notation
UML Diagram
ER Diagram
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UML Class Diagrams (Cont.)
 Entity sets are shown as boxes, and attributes are shown within the box, rather
than as separate ellipses in E-R diagrams.
 Binary relationship sets are represented in UML by just drawing a line
connecting the entity sets. The relationship set name is written adjacent to the
line.
 The role played by an entity set in a relationship set may also be specified by
writing the role name on the line, adjacent to the entity set.
 The relationship set name may alternatively be written in a box, along with
attributes of the relationship set, and the box is connected, using a dotted line,
to the line depicting the relationship set.

Non-binary relationships drawn using diamonds, just as in ER diagrams
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UML Class Diagram Notation (Cont.)
UML Diagram
ER Diagram
overlapping
disjoint
*Note reversal of position in cardinality constraint depiction
*Generalization can use merged or separate arrows independent
of disjoint/overlapping
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UML Class Diagrams (Contd.)
 Cardinality constraints are specified in the form l..h, where l denotes the
minimum and h the maximum number of relationships an entity can participate in.
 Beware: the positioning of the constraints is exactly the reverse of the positioning
of constraints in E-R diagrams.
 The constraint 0..* on the E2 side and 0..1 on the E1 side

each E2 entity can participate in at most one relationship, whereas each E1
entity can participate in many relationships;

in other words, the relationship is many to one from E2 to E1.
 Single values, such as 1 or * may be written on edges;

The single value 1 on an edge is treated as equivalent to 1..1, while * is
equivalent to 0..*.
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Other Aspects of Database Design
 6.11 UML
 6.12 Summary
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Ch 6: Summary (1)
 The entity-relationship (E-R) data model is based on a perception of a real
world that consists of a set of basic objects called entities, and of relationships
among these objects.
 The model is intended primarily for the database-design process.
It was developed to facilitate database design by allowing the specification of an
enterprise schema.
Such a schema represents the overall logical structure of the database.
This overall structure can be expressed graphically by an E-R diagram.
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Ch 6: Summary (2)
 An entity is an object that exists in the real world and is distinguishable from
other objects.
We express the distinction by associating with each entity a set of attributes that
describes the object.
 A relationship is an association among several entities.
The collection of all entities of the same type is an entity set, and the collection
of all relationships of the same type is a relationship set.
 Mapping cardinalities express the number of entities to which another entity
can be associated via a relationship set.
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Ch 2: Summary (3)
 A superkey of an entity set is a set of one or more attributes that, taken collectively,
allows us to identify uniquely an entity in the entity set.
We choose a minimal superkey for each entity set from among its superkeys; the
minimal superkey is termed the entity set’s primary key.
Similarly, a relationship set is a set of one or more attributes that, taken collectively,
allows us to identify uniquely a relationship in the relationship set.
Likewise, we choose a minimal superkey for each relationship set from among its
superkeys; this is the relationship set’s primary key.
 An entity set that does not have sufficient attributes to form a primary key is termed
a weak entity set.
An entity set that has a primary key is termed a strong entity set.
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Ch 6: Summary (4)
 Specialization and generalization define a containment relationship between a
higher-level entity set and one or more lower-level entity set to form a lower level
entity set.
 Aggregation is an abstraction in which relationship sets (along with their
associated entity sets) are treated as higher-level entity sets, and can participate
in relationships.
 The various features of the E-R model offer the database designer numerous
choices in how to best represent the enterprise being modeled.
Concepts and objects may, in certain cases, be represented by entities,
relationships, or attributes.
Aspects of the overall structure of the enterprise may be best described by using
weak entity sets, generalization, specialization, or aggregation.
Often, the designer must weigh the merits of a simple, compact model versus
those of a more precise, but more complex, one.
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Ch 6: Summary (5)
 A database that conforms to an E-R diagram can be represented by a collection
of tables.
For each entity set and for each relationship set in the database, there is a
unique table that is assigned the name of the corresponding entity set of
relationship set.
Each table has a number of columns, each of which has a unique name.
Converting database representation from an E-R diagram to a table format is the
basis for deriving a relational-database design from an E-R diagram.
 The unified model language (UML) provides a graphical means of modeling
various components of a software system.
The class diagram components of UML is based on E-R diagrams. However,
there are some differences between the two that one must beware of.
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Ch 6: Bibliographical Notes (1)
 The E-R data model was introduced by Chen[1976].
 A logical design methodology for relational database using the extended E-R
model is presented by Teorey et al.[1986].
 Mapping from extended E-R models to the relational model is discussed by
Lyngbaek and Vianu [1987] and Markowitz and Shoshani [1992].
 Various data-manipulation languages for the E-R model have been proposed:
GERM (Benneworth et al. [1981]), GORDAS (Elmasri and Wiederhole[1981]),
and ERROL (Markowitz and Raz[1983]).
 A graphical query language for the E-R database was proposed by Zhang and
Mendelzon [1983] and Elmasri and Larson[1985].
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Ch 6: Bibliographical Notes (2)
 Smith and Simth [1977] introduced the concepts of generalization,
specialization, and aggregation and Hammer and McLeod [1980] expanded
them.
 Lenzerini and Santucci [1983] used the concepts in defining cardinality
constraints in the E-R model.
 Thalheim [2000] provides a detailed textbook coverate of research in E-R
modeling.
 Basic textbook discussions are offered by Batini et al. [1992] and Elmasri and
Navathe [2000].
 Davis et al. [1983] provide a collection of papers on the E-R model.
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Ch 6: Tools
 Many database systems provide tools for database design that support E-R
diagrams. These tools help a designer create E-R diagrams, and they can
automatically create corresponding tables in a database.
 See bibliographic notes of Chapter 1 for references to database system
vendor’s Web sites.
 There are also some database independent data modeling tools that support E-
R diagram and UML class diagrams. These include Rational Rose
(www.rational.com/products/rose), Visio Enterprise (see www.visio.com), and
ERwin (search for Erwin at the site www.cai.com/products)
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Chapter 6: Entity-Relationship Model
 6.1 Design Process
 6.2 Modeling
 6.3 Constraints
 6.4 E-R Diagram
 6.5 Design Issues
 6.6 Weak Entity Sets
 6.7 Extended E-R Features
 6.8 Design of the Bank Database
 6.9 Reduction to Relation Schemas
 6.10 Database Design
 6.11 UML
 6.12 Summary
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End of Chapter 6
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E-R Diagram for Exercise 2.10
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E-R Diagram for Exercise 2.15
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E-R Diagram for Exercise 2.22
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E-R Diagram for Exercise 2.15
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Existence Dependencies
 If the existence of entity x depends on the existence of entity y,
then x is said to be existence dependent on y.

y is a dominant entity (in example below, loan)

x is a subordinate entity (in example below, payment)
loan
loan-payment
payment
If a loan entity is deleted, then all its associated payment entities
must be deleted also.
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Figure 6.8
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Figure 6.15
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Figure 6.16
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Figure 6.26
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Figure 6.27
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Figure 6.28
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Figure 6.29
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Figure 6.30
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Figure 6.31
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Alternative E-R Notations
Figure 6.24
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