The Entity-Relationship Model
Chapter 2
Database Management Systems, R. Ramakrishnan and J. Gehrke
1
Overview of Database Design
v
Requirements Analysis
– Understand what data is to be stored in DB, what
applications must be built on top of it, and what
operations are most frequent and subject to performance
requirements.
– Involve discussions with user groups, a study of current
operating environment and how it is expected to change,
analysis of any available documentation on existing
applications
v
Conceptual DB Design
– Develop high-level description of data to be stored in DB,
along with constraints that are known to hold over this
data (ER model)
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Overview of Database Design
v
Logical DB Design
– Choose a DBMS to implement our DB design, convert
conceptual DB design into a DB schema in the model of the
chosen DBMS, e.g., convert an ER schema into relational
DB schema (chap 3)
v
Schema Refinement
– Analyze collection of relations obtained in our relational
DB schema to identify potential problems
(insert/delete/update anomalies), and to refine it (chap 19)
v
Physical DB Design
– Consider typical expected workloads that our DB must
support and further refine DB design (chap 20)
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Overview of Database Design
v
Security Design
– Identify different user groups and different roles played by
various users. For each role, identify the parts of DB that
they be able to access and the parts of DB they should not
be allowed to access (chap 21)
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Conceptual Database Design
v
Conceptual design: (ER Model is used at this stage.)
–
–
–
–
–
What are the entities and relationships in the enterprise?
What information about these entities and relationships
should we store in the database?
What are the integrity constraints or business rules that hold?
A database `schema’ in the ER Model can be represented
pictorially (ER diagrams).
Can map an ER diagram into a relational schema.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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ER Model Basics
ssn
name
lot
Employees
v
Entity: Real-world object distinguishable from other
objects. An entity is described (in DB) using a
set of attributes.
v
Entity Set: A collection of similar entities. E.g., all
employees.
–
–
–
All entities in an entity set have the same set of attributes.
(Until we consider ISA hierarchies, anyway!)
Each entity set has a key (minimal set of attributes whose
values uniquely identify an entity in the set). There could be
more one candidate key; if so, we designate one of them as
primary key
Each attribute has a domain (set of possible values an
attribute may take).
Database Management Systems, R. Ramakrishnan and J. Gehrke
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name
ER Model Basics (Contd.)
name
Employees
v
v
dname
lot
budget
did
Works_In
lot
Employees
since
ssn
ssn
Departments
supervisor
subordinate
Reports_To
Relationship: Association among two or more entities.
E.g., Attishoo works in Pharmacy department.
Relationship Set: Collection of similar relationships.
–
An n-ary relationship set R relates n entity sets E1 ... En;
each relationship in R involves entities e1  E1, ..., en  En
u Same entity set could participate in different
relationship sets, or in different “roles” in same set.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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name
ER Model Basics (Contd.)
name
Employees
v
v
dname
lot
budget
did
Works_In
lot
Employees
since
ssn
ssn
Departments
supervisor
subordinate
Reports_To
A relationship can also have descriptive attributes:
Descriptive attributes are used to record information
about the relationship, e.g., since attribute associated
with Works_In relationship
Instance of a relationship: a set of relationships, e.g.,
Figure 2.3
Database Management Systems, R. Ramakrishnan and J. Gehrke
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ER Model Basics (Cont.)
v
Ternary relationship: a relationship involving
three entities
since
name
ssn
dname
lot
did
Employees
Works_In2
address
Locations
Database Management Systems, R. Ramakrishnan and J. Gehrke
budget
Departments
capacity
9
Key Constraints
since
name
ssn
v
v
Consider Works_In:
An employee can
work in many
departments; a dept
can have many
employees.
In contrast, each
dept has at most
one manager,
according to the
key constraint on
Manages.
dname
lot
Employees
1-to-1
1-to Many
Database Management Systems, R. Ramakrishnan and J. Gehrke
did
Manages
Many-to-1
budget
Departments
Many-to-Many
10
Participation Constraints
v
Does every department have a manager?
–
If so, this is a participation constraint: the participation of
Departments in Manages is said to be total (vs. partial).
u Every did value in Departments table must appear in a row of
the Manages table (with a non-null ssn value!)
since
name
ssn
dname
did
lot
Employees
Manages
budget
Departments
Works_In
since
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Weak Entities
v
A weak entity can be identified uniquely only by considering
the primary key of another (owner) entity.
–
–
Owner entity set and weak entity set must participate in a one-tomany relationship set (one owner, many weak entities).
Weak entity set must have total participation in this identifying
relationship set.
name
ssn
lot
Employees
cost
Policy
Database Management Systems, R. Ramakrishnan and J. Gehrke
pname
age
Dependents
12
name
ssn
ISA (`is a’) Hierarchies
lot
Employees
vAs
in C++, or other PLs, hourly_wages hours_worked
ISA
contractid
attributes are inherited.
vIf we declare A ISA B, every A
Contract_Emps
Hourly_Emps
entity is also considered to be a B
entity.
v Overlap constraints: Can Joe be an Hourly_Emps as well as
a Contract_Emps entity? (Allowed/disallowed)
v Covering constraints: Does every Employees entity also have
to be an Hourly_Emps or a Contract_Emps entity? (Yes/no)
v Reasons for using ISA:
– To add descriptive attributes specific to a subclass.
– To identify entitities that participate in a relationship.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Overlap constraints:
suppose there is another Senior_Emps subclass, and
an employee can be both a Contract_Emps entity and a
Senior_Emps entity; we denote this by writing
‘Contract_Emps OVERLAPS Senior_Emps’
Covering constraints:
If every Motor_Vehicles entity have to be either a
Motorboats entity or a Cars entity, then we write
‘Motorboats AND Cars COVER Motor_Vehicles’
Database Management Systems, R. Ramakrishnan and J. Gehrke
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name
ssn
Aggregation
v
Used when we have
to model a
relationship
involving (entitity
sets and) a
relationship set.
–
Aggregation allows us
to treat a relationship
set as an entity set
for purposes of
participation in
(other) relationships.
lot
Employees
Monitors
since
started_on
pid
pbudget
Projects
until
did
Sponsors
dname
budget
Departments
* Aggregation vs. ternary relationship:
v Monitors is a distinct relationship,
with a descriptive attribute.
v Also, can say that each sponsorship
is monitored by at most one employee.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Conceptual Design Using the ER Model
v
Design choices:
–
–
–
v
Should a concept be modeled as an entity or an attribute?
Should a concept be modeled as an entity or a relationship?
Identifying relationships: Binary or ternary? Aggregation?
Constraints in the ER Model:
–
–
A lot of data semantics can (and should) be captured.
But some constraints cannot be captured in ER diagrams.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Entity vs. Attribute
v
v
Should address be an attribute of Employees or an
entity (connected to Employees by a relationship)?
Depends upon the use we want to make of address
information, and the semantics of the data:
u
u
If we have several addresses per employee, address must
be an entity (since attributes cannot be set-valued).
If the structure (city, street, etc.) is important, e.g., we
want to retrieve employees in a given city, address must
be modeled as an entity (since attribute values are
atomic).
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Entity vs. Attribute (Contd.)
name
v
v
from
to
dname
Works_In2 does not ssn
lot
did
budget
allow an employee to
Departments
Works_In2
Employees
work in a department
for two or more periods.
Similar to the problem
of wanting to record
several addresses for an
name
dname
employee: we want to
ssn
lot
did
budget
record several values of the
Works_In3
Departments
Employees
descriptive attributes for
each instance of this
Duration
to
from
relationship.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Entity vs. Relationship
v
v
First ER diagram OK if
since dbudget
name
a manager gets a
ssn
lot
did
separate discretionary
budget for each dept.
Employees
Manages2
What if a manager gets
a discretionary budget
that covers all
name
ssn
managed depts?
lot
did
–
Redundancy of dbudget,
which is stored for each
dept managed by the
manager.
Employees
Misleading: suggests dbudget
tied to managed dept.
Manages3
dname
budget
Departments
dname
budget
Departments
since
apptnum
Database Management Systems, R. Ramakrishnan and J. Gehrke
Mgr_Appts
dbudget
19
Binary vs. Ternary Relationships
name
ssn
v
If each policy is
owned by just 1
employee:
–
v
Key constraint
on Policies
would mean
policy can only
cover 1
dependent!
What are the
additional
constraints in the
2nd diagram?
pname
lot
Employees
Dependents
Covers
Bad design
age
Policies
policyid
cost
name
pname
ssn
lot
age
Dependents
Employees
Purchaser
Beneficiary
Better design
Database Management Systems, R. Ramakrishnan and J. Gehrke
policyid
Policies
cost
20
Binary vs. Ternary Relationships (Contd.)
v
v
Previous example illustrated a case when two
binary relationships were better than one ternary
relationship.
An example in the other direction: a ternary
relation Contracts relates entity sets Parts,
Departments and Suppliers, and has descriptive
attribute qty. No combination of binary
relationships is an adequate substitute:
–
–
S “can-supply” P, D “needs” P, and D “deals-with” S
does not imply that D has agreed to buy P from S.
How do we record qty?
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Summary of Conceptual Design
v
Conceptual design follows requirements analysis,
–
v
ER model popular for conceptual design
–
v
v
v
Yields a high-level description of data to be stored
Constructs are expressive, close to the way people think
about their applications.
Basic constructs: entities, relationships, and attributes
(of entities and relationships).
Some additional constructs: weak entities, ISA
hierarchies, and aggregation.
Note: There are many variations on ER model.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Summary of ER (Contd.)
v
Several kinds of integrity constraints can be expressed
in the ER model: key constraints, participation
constraints, and overlap/covering constraints for ISA
hierarchies. Some foreign key constraints are also
implicit in the definition of a relationship set.
–
–
Some constraints (notably, functional dependencies) cannot be
expressed in the ER model.
Constraints play an important role in determining the best
database design for an enterprise.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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Summary of ER (Contd.)
v
ER design is subjective. There are often many ways
to model a given scenario! Analyzing alternatives
can be tricky, especially for a large enterprise.
Common choices include:
–
v
Entity vs. attribute, entity vs. relationship, binary or nary relationship, whether or not to use ISA hierarchies,
and whether or not to use aggregation.
Ensuring good database design: resulting
relational schema should be analyzed and refined
further. FD information and normalization
techniques are especially useful.
Database Management Systems, R. Ramakrishnan and J. Gehrke
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