Chapter 2: Entity-

Entity-Relationship Model

 What’s the use of the E-R 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

Database System Concepts 2.1 ©Silberschatz, Korth and Sudarshan

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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 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

Database System Concepts 2.3 ©Silberschatz, Korth and Sudarshan

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

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 Relationship Sets with Attributes

Database System Concepts 2.5 ©Silberschatz, Korth and Sudarshan

E-R Diagram With Composite, Multivalued, and

Derived Attributes—
Attributes—try to avoid them

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 Composite 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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 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

Database System Concepts 2.9 ©Silberschatz, Korth and Sudarshan

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

Database System Concepts 2.11 ©Silberschatz, Korth and Sudarshan

One-
One-To-
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-
Many-To-
To-One Relationships
 Example of many-to-one relationships: a loan is associated with
several (including 0) customers via borrower, a customer is
associated with at most one loan via borrower

Database System Concepts 2.13 ©Silberschatz, Korth and Sudarshan

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.
 Example of 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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 Many-
Many-To-
To-Many Relationship

 Example of Many to Many Relationships:

 A customer is associated with several (possibly 0) loans via
borrower
 A loan is associated with several (possibly 0) customers via
borrower

Database System Concepts 2.15 ©Silberschatz, Korth and Sudarshan

Alternative Notation for Cardinality

Limits
 Cardinality limits can also express participation constraints

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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.

Database System Concepts 2.17 ©Silberschatz, Korth and Sudarshan

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 not as
common as binary ones.

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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 confusion we outlaw more than one arrow

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 Binary Vs. Non-
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
 E.g. works-on

Database System Concepts 2.21 ©Silberschatz, Korth and Sudarshan

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.

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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)

Database System Concepts 2.23 ©Silberschatz, Korth and Sudarshan

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

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 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

Database System Concepts 2.25 ©Silberschatz, Korth and Sudarshan

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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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.

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 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

Database System Concepts 2.29 ©Silberschatz, Korth and Sudarshan

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

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 Design Constraints on
aSpecialization/Generalization
aSpecialization/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

Database System Concepts 2.31 ©Silberschatz, Korth and Sudarshan

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

Database System Concepts 2.33 ©Silberschatz, Korth and Sudarshan

E-R Diagram With Aggregation

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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.
 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 System Concepts 2.35 ©Silberschatz, Korth and Sudarshan

E-R Diagram for a Banking Enterprise

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 Summary of Symbols Used in E-
E-R
Notation

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Summary of Symbols (Cont.)

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