Er model

Chapter 6: Entity-Relationship
Model

Database System Concepts, 5th Ed.
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use

Chapter 6: Entity-Relationship
Model

s Modeling
s Constraints
s E-R Diagram
s Design Issues
s Weak Entity Sets
s Extended E-R Features
s Design of the Bank Database
s Reduction to Relation Schemas
s Database Design

Database System Concepts - 5 th Edition, Oct 5, 2006 6.2 ©Silberschatz, Korth and Sudarshan

Modeling

s A database can be modeled as:
q a collection of entities,
q relationship among entities.
s An entity is an object that exists and is distinguishable from other
objects.
q Example: specific person, company, event, plant
s Entities have attributes
q Example: people have names and addresses
s An entity set is a set of entities of the same type that share the same
properties.
q Example: set of all persons, companies, trees, holidays


Entity Sets customer and loan
customer_id customer_ customer_ customer_ loan_ amount
name street city number


Relationship Sets

s A relationship is an association among several entities
Example:
Hayes depositor A-102
customer entity relationship set account entity
s 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
q Example:
(Hayes, A-102) ∈ depositor


Relationship Set borrower


Relationship Sets (Cont.)

s An attribute can also be property of a relationship set.
s For instance, the depositor relationship set between entity sets customer
and account may have the attribute access-date


Degree of a Relationship Set

s Refers to number of entity sets that participate in a relationship
set.
s Relationship sets that involve two entity sets are binary (or
degree two). Generally, most relationship sets in a database
system are binary.
s 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

s Relationships between more than two entity sets are rare. Most
relationships are binary. (More on this later.)


Attributes

s 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 )

s Domain – the set of permitted values for each attribute
s Attribute types:
q Simple and composite attributes.
q Single-valued and multi-valued attributes
 Example: multivalued attribute: phone_numbers
q Derived attributes
 Can be computed from other attributes
 Example: age, given date_of_birth


Composite Attributes


Mapping Cardinality Constraints

s Express the number of entities to which another entity can be
associated via a relationship set.
s Most useful in describing binary relationship sets.
s For a binary relationship set the mapping cardinality must be one of
the following types:
q One to one
q One to many
q Many to one
q 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

Keys

s A super key of an entity set is a set of one or more attributes
whose values uniquely determine each entity.
s A candidate key of an entity set is a minimal super key
q Customer_id is candidate key of customer
q account_number is candidate key of account
s Although several candidate keys may exist, one of the candidate
keys is selected to be the primary key.


E-R Diagrams

s Rectangles represent entity sets.
s Diamonds represent relationship sets.
s Lines link attributes to entity sets and entity sets to relationship sets.
s Ellipses represent attributes
q Double ellipses represent multivalued attributes.
q Dashed ellipses denote derived attributes.
s Underline indicates primary key attributes (will study later)


E-R Diagram With Composite, Multivalued, and
Derived Attributes


Relationship Sets with Attributes


Roles

s Entity sets of a relationship need not be distinct
s The labels “manager” and “worker” are called roles; they specify how
employee entities interact via the works_for relationship set.
s Roles are indicated in E-R diagrams by labeling the lines that connect
diamonds to rectangles.
s Role labels are optional, and are used to clarify semantics of the
relationship


Cardinality Constraints

s 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.
s One-to-one relationship:
q A customer is associated with at most one loan via the relationship
borrower
q A loan is associated with at most one customer via borrower


One-To-Many Relationship

s 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

s 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

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


Participation of an Entity Set in a
Relationship Set

s Total participation (indicated by double line): every entity in the entity set
participates in at least one relationship in the relationship set
q E.g. participation of loan in borrower is total
 every loan must have a customer associated to it via borrower
s Partial participation: some entities may not participate in any relationship in
the relationship set
q Example: participation of customer in borrower is partial


Alternative Notation for Cardinality
Limits

s Cardinality limits can also express participation constraints


E-R Diagram with a Ternary
Relationship


Weak Entity Sets

s An entity set that does not have a primary key is referred to as a weak
entity set.
s The existence of a weak entity set depends on the existence of a
identifying entity set
q it must relate to the identifying entity set via a total, one-to-many
relationship set from the identifying to the weak entity set
q Identifying relationship depicted using a double diamond
s 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.
s 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.)

s We depict a weak entity set by double rectangles.
s We underline the discriminator of a weak entity set with a dashed
line.
s payment_number – discriminator of the payment entity set
s Primary key for payment – (loan_number, payment_number)


Weak Entity Sets (Cont.)

s 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.
s 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

s In a university, a course is a strong entity and a course_offering can
be modeled as a weak entity
s The discriminator of course_offering would be semester (including
year) and section_number (if there is more than one section)
s 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


Extended E-R Features:
Specialization

s Top-down design process; we designate subgroupings within an entity set
that are distinctive from other entities in the set.
s These subgroupings become lower-level entity sets that have attributes or
participate in relationships that do not apply to the higher-level entity set.
s Depicted by a triangle component labeled ISA (E.g. customer “is a”
person).
s 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


Extended ER Features:
Generalization

s A bottom-up design process – combine a number of entity
sets that share the same features into a higher-level entity set.
s Specialization and generalization are simple inversions of each
other; they are represented in an E-R diagram in the same way.
s The terms specialization and generalization are used
interchangeably.


Specialization and Generalization
(Cont.)

s Can have multiple specializations of an entity set based on different
features.
s E.g. permanent_employee vs. temporary_employee, in addition to
officer vs. secretary vs. teller
s Each particular employee would be
q a member of one of permanent_employee or temporary_employee,
q and also a member of one of officer, secretary, or teller
s The ISA relationship also referred to as superclass - subclass
relationship


Design Constraints on a
Specialization/Generalization
s Constraint on which entities can be members of a given lower-level
entity set.
q condition-defined
 Example: all customers over 65 years are members of senior-
citizen entity set; senior-citizen ISA person.
q user-defined
s Constraint on whether or not entities may belong to more than one
lower-level entity set within a single generalization.
q 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
q Overlapping
 an entity can belong to more than one lower-level entity set


Design Constraints on a
Specialization/Generalization (Cont.)
s 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.
q total : an entity must belong to one of the lower-level
entity sets
q partial: an entity need not belong to one of the lower-level
entity sets


Aggregation

s Consider the ternary relationship works_on, which we saw earlier

s Suppose we want to record managers for tasks performed by an
employee at a branch


Aggregation (Cont.)

s Relationship sets works_on and manages represent overlapping information
q Every manages relationship corresponds to a works_on relationship
q However, some works_on relationships may not correspond to any
manages relationships
 So we can’t discard the works_on relationship
s Eliminate this redundancy via aggregation
q Treat relationship as an abstract entity
q Allows relationships between relationships
q Abstraction of relationship into new entity
s Without introducing redundancy, the following diagram represents:
q An employee works on a particular job at a particular branch
q An employee, branch, job combination may have an associated manager


E-R Diagram With Aggregation


E-R Diagram for a Banking
Enterprise


Summary of Symbols Used in E-R Notation


Summary of Symbols (Cont.)


Reduction to Relation Schemas

s Primary keys allow entity sets and relationship sets to be
expressed uniformly as relation schemas that represent the
contents of the database.
s A database which conforms to an E-R diagram can be
represented by a collection of schemas.
s 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.
s Each schema has a number of columns (generally
corresponding to attributes), which have unique names.


Representing Entity Sets as
Schemas

s A strong entity set reduces to a schema with the same attributes.
s A weak entity set becomes a table that includes a column for the
primary key of the identifying strong entity set
payment =
( loan_number, payment_number, payment_date, payment_amount )


Representing Relationship Sets as
Schemas
s 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.
s Example: schema for relationship set borrower
borrower = (customer_id, loan_number )


Redundancy of Schemas

s 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
s Example: Instead of creating a schema for relationship set
account_branch, add an attribute branch_name to the schema
arising from entity set account


Redundancy of Schemas (Cont.)

s For one-to-one relationship sets, either side can be chosen to act as the
“many” side
q That is, extra attribute can be added to either of the tables
corresponding to the two entity sets
s 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
s The schema corresponding to a relationship set linking a weak entity set
to its identifying strong entity set is redundant.
q Example: The payment schema already contains the attributes that
would appear in the loan_payment schema (i.e., loan_number and
payment_number).


Composite and Multivalued
Attributes
s Composite attributes are flattened out by creating a separate attribute for
each component attribute
q 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
s A multivalued attribute M of an entity E is represented by a separate
schema EM
q Schema EM has attributes corresponding to the primary key of E and
an attribute corresponding to multivalued attribute M
q Example: Multivalued attribute dependent_names of employee is
represented by a schema:
employee_dependent_names = ( employee_id, dname)
q 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)

Representing Specialization via
Schemas
s Method 1:
q Form a schema for the higher-level entity
q Form a schema for each lower-level entity set, include primary
key of higher-level entity set and local attributes

schema attributes
person name, street, city
customer name, credit_rating
employee name, salary
q 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


Representing Specialization as Schemas
(Cont.)
s Method 2:
q Form a schema for each entity set with all local and inherited attributes

schema attributes
person name, street, city
customer name, street, city, credit_rating
employee name, street, city, salary

q 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
q Drawback: street and city may be stored redundantly for people who are
both customers and employees


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