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Database Design Using the
E-R Model
CMPT 354
Jian Pei
jpei@cs.sfu.ca
Outline
Overview of the Design Process
The Entity-Relationship Model
Complex Attributes
Mapping Cardinalities
Primary Key
Removing Redundant Attributes in
Entity Sets
Reducing ER Diagrams to Relational
Schemas
Extended E-R Features
Entity-Relationship Design Issues
Alternative Notations for Modeling
Data
Other Aspects of Database Design
Extended E-R Features
Entity-Relationship Design Issues
Alternative Notations for Modeling
Data
Other Aspects of Database Design
J. Pei: CMPT 354 — Database Design Using the E-R Model 2
Database Design
J. Pei: CMPT 354 — Database Design Using the E-R Model 3
Data needs (abstract) data model Implementation
Logical design Physical design
Design Phases
Initial phase – characterize fully the data needs of the prospective
database users
Second phase – choosing a data model
Applying the concepts of the chosen data model
Translating these requirements into a conceptual schema of the database
A fully developed conceptual schema indicates the functional requirements of
the enterprise
Describe the kinds of operations (or transactions) that will be performed on the data
J. Pei: CMPT 354 — Database Design Using the E-R Model 4
Design Phases
Final Phase – Moving from an abstract data model to the
implementation of the database
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
J. Pei: CMPT 354 — Database Design Using the E-R Model 5
Design Alternatives
In designing a database schema, avoid two major pitfalls
Redundancy: a bad design may result in repeat information
Redundant representation of information may lead to data inconsistency among the
various copies of information
Incompleteness: a bad design may make certain aspects of the enterprise
difficult or impossible to model
Avoiding bad designs is not enough – there may be a large number of
good designs from which we must choose
J. Pei: CMPT 354 — Database Design Using the E-R Model 6
Design Approaches
Entity Relationship Model (covered in this section)
Model an enterprise as a collection of entities and relationships
Entity: a “thing” or “object” in the enterprise that is distinguishable
from other objects
Described by a set of attributes
Relationship: an association among several entities
Represented diagrammatically by an entity-relationship diagram
Normalization Theory (to be discussed later)
Formalize what designs are bad, and test for them
J. Pei: CMPT 354 — Database Design Using the E-R Model 7
ER model – Database Modeling
The ER data model was developed to facilitate database design by
allowing specification of an enterprise schema that represents the
overall logical structure of a database
The ER data model employs three basic concepts
Entity sets
Relationship sets
Attributes
ER diagram: the diagrammatic representation associated with the ER
model, which can express the overall logical structure of a database
graphically
J. Pei: CMPT 354 — Database Design Using the E-R Model 8
Entity Sets
An entity is an object that exists and is distinguishable from other objects
Example: specific person, company, event, plant
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
An entity is represented by a set of attributes – descriptive properties possessed
by all members of an entity set
Example:
instructor = (ID, name, salary )
course= (course_id, title, credits)
A subset of the attributes form a primary key of the entity set uniquely identifying
each member of the set
J. Pei: CMPT 354 — Database Design Using the E-R Model 9
Entity Sets – Instructor and Student
J. Pei: CMPT 354 — Database Design Using the E-R Model 10
instructor
student
22222 Einstein
Katz
Kim
Crick
Srinivasan
Singh
45565
98345
76766
10101
76543
12345
98988
76653
23121
00128
76543
Shankar
Tanaka
Aoi
Chavez
Peltier
Zhang
Brown
44553
Representing Entity sets in ER Diagram
Entity sets can be represented graphically
Rectangles represent entity sets
Attributes listed inside entity rectangle
Underline indicates primary key attributes
J. Pei: CMPT 354 — Database Design Using the E-R Model 11
Relationship Sets
A relationship is an association among several entities
Example:
44553 (Peltier) advisor 22222 (Einstein)
student entity relationship set instructor entity
A relationship set is a mathematical relation among n 3 2 entities, each
taken from entity sets
{(e1, e2, … en) | e1 E1, e2 E2, …, en En}
where (e1, e2, …, en) is a relationship
Example
(44553, 22222) advisor
J. Pei: CMPT 354 — Database Design Using the E-R Model 12
Relationship Sets
Example: we define the relationship set advisor to denote the
associations between students and the instructors who act as their
advisors
Pictorially, we draw a line between related entities
J. Pei: CMPT 354 — Database Design Using the E-R Model 13
instructor
student
76766 Crick
Katz
Srinivasan
Kim
Singh
Einstein
45565
10101
98345
76543
22222
98988
12345
00128
76543
76653
23121
44553
Tanaka
Shankar
Zhang
Brown
Aoi
Chavez
Peltier
Representing Relationship Sets via ER
Diagrams
Diamonds represent relationship sets
J. Pei: CMPT 354 — Database Design Using the E-R Model 14
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
Relationship Sets
An attribute can also be associated with a relationship set
For instance, the advisor relationship set between entity sets
instructor and student may have the attribute date which tracks when
the student started being associated with the advisor
J. Pei: CMPT 354 — Database Design Using the E-R Model 15
instructor
student
76766 Crick
Katz
Srinivasan
Kim
Singh
Einstein
45565
10101
98345
76543
22222
98988
12345
00128
76543
44553
Tanaka
Shankar
Zhang
Brown
Aoi
Chavez
Peltier
3 May 2008
10 June 2007
12 June 2006
6 June 2009
30 June 2007
31 May 2007
4 May 2006
76653
23121
Relationship Sets with Attributes
J. Pei: CMPT 354 — Database Design Using the E-R Model 16
ID
name
salary
ID
name
tot_cred
date
instructor student
advisor
Roles
Entity sets of a relationship need not be distinct
Each occurrence of an entity set plays a “role” in the relationship
The labels “course_id” and “prereq_id” are called roles
J. Pei: CMPT 354 — Database Design Using the E-R Model 17
course
course_id
title
credits
course_id
prereq_id prereq
Degree of a Relationship Set
Binary relationship
Involve two entity sets (or degree two)
Most relationship sets in a database system are binary
Relationships between more than two entity sets are rare – most
relationships are binary
Example: students work on research projects under the guidance
of an instructor
relationship proj_guide is a ternary relationship between
instructor, student, and project
J. Pei: CMPT 354 — Database Design Using the E-R Model 18
Non-binary Relationship Sets
Most relationship sets are binary
There are occasions when it is more convenient to represent
relationships as non-binary.
E-R Diagram with a Ternary Relationship
J. Pei: CMPT 354 — Database Design Using the E-R Model 19
instructor
ID
name
salary
student
ID
name
tot_cred
. . .
project
proj_guide
To-Do List
Using ER diagram, describe entity sets customer and product, and
relationship set purchases
Using ER diagram, describe entity sets employee and company, and
relationship set works
Can you give an example where there are only entity sets but no
relationship sets Is it a good design
Can you give an example where there are only relationship sets but
no entity sets Is it a good design
Can you give an example where a relationship set has degree 1
J. Pei: CMPT 354 — Database Design Using the E-R Model 20
Complex Attributes
Attribute types
Simple and composite attributes
Single-valued and multivalued attributes
Example: multivalued attribute: phone_numbers
Derived attributes
Can be computed from other attributes
Example: age, given date_of_birth
Domain – the set of permitted values for each attribute
J. Pei: CMPT 354 — Database Design Using the E-R Model 21
Composite Attributes
Composite attributes allow us to divide attributes into subparts (other
attributes)
J. Pei: CMPT 354 — Database Design Using the E-R Model 22
name address
first_name middle_initial last_name street city state postal_code
street_number street_name apartment_number
composite
attributes
component
attributes
Representing Complex Attributes in ER
Diagram
J. Pei: CMPT 354 — Database Design Using the E-R Model 23
instructor
ID
name
first_name
middle_initial
last_name
address
street
street_number
street_name
apt_number
city
state
zip
{ phone_number }
date_of_birth
age ( )
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
J. Pei: CMPT 354 — Database Design Using the E-R Model 24
Mapping Cardinalities
J. Pei: CMPT 354 — Database Design Using the E-R Model 25
One to one One to many
Note: Some elements in A and B may not be mapped to any elements in the other set
Many to one Many to many
Representing Cardinality Constraints in ER
Diagram
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 between an instructor and a student :
A student is associated with at most one instructor via the
relationship advisor
An instructor is associated with at most one student via the
relationship advisor
J. Pei: CMPT 354 — Database Design Using the E-R Model 26
instructor student
ID
name
salary
ID
name
tot_cred
advisor
One-to-Many Relationship
one-to-many relationship between an instructor and a student
An instructor is associated with several (including 0) students via
advisor
A student is associated with at most one instructor via advisor
J. Pei: CMPT 354 — Database Design Using the E-R Model 27
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
Many-to-One Relationships
In a many-to-one relationship between an instructor and a student,
An instructor is associated with at most one student via advisor
A student is associated with several (including 0) instructors via
advisor
J. Pei: CMPT 354 — Database Design Using the E-R Model 28
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
Many-to-Many Relationship
An instructor is associated with several (possibly 0) students via
advisor
A student is associated with several (possibly 0) instructors via advisor
J. Pei: CMPT 354 — Database Design Using the E-R Model 29
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
Total and Partial Participation
Total participation (indicated by double line): every entity in the entity set
participates in at least one relationship in the relationship set
Participation of student in advisor relation is total: every student must have an
associated instructor as the advisor
Partial participation: some entities may not participate in any relationship
in the relationship set
Example: participation of instructor in advisor is partial
J. Pei: CMPT 354 — Database Design Using the E-R Model 30
Notation for Expressing More Complex
Constraints
§ A line may have an associated minimum and maximum cardinality, shown in the
form l..h, where l is the minimum and h the maximum cardinality
A minimum value of 1 indicates total participation
A maximum value of 1 indicates that the entity participates in at most one
relationship
A maximum value of * indicates no limit
§ Example
Instructor can advise 0 or more students
A student must have 1 advisor; cannot have multiple advisors
J. Pei: CMPT 354 — Database Design Using the E-R Model 31
instructor
ID
name
salary
student
ID
name
tot_cred
advisor 1..10..*
Cardinality Constraints on Ternary
Relationship
We allow at most one arrow out of a ternary (or greater degree) relationship to indicate
a cardinality constraint
For example, an arrow from proj_guide to instructor indicates each student has at most
one guide for a project
Why only one arrow If there is more than one arrow, there are two ways of defining the
meaning
For example, 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
J. Pei: CMPT 354 — Database Design Using the E-R Model 32
A
B cR
To-Do List
Can roles also have cardinality/partition constraints
J. Pei: CMPT 354 — Database Design Using the E-R Model 33
course
course_id
title
credits
course_id
prereq_id prereq
Primary Key
Primary keys provide a way to specify how entities and relations are
distinguished
Entity sets
Relationship sets
Weak entity sets
J. Pei: CMPT 354 — Database Design Using the E-R Model 34
Primary key for Entity Sets
By definition, individual entities are distinct
From database perspective, the differences among them must be
expressed in terms of their attributes
The values of the attribute values of an entity must be such that they
can uniquely identify the entity
No two entities in an entity set are allowed to have exactly the same value for
all attributes
A key for an entity is a set of attributes that suffice to distinguish
entities from each other
J. Pei: CMPT 354 — Database Design Using the E-R Model 35
Primary Key for Relationship Sets
To distinguish among the various relationships of a relationship set we use
the individual primary keys of the entities in the relationship set
Let R be a relationship set involving entity sets E1, E2, …, En
The primary key for R is consists of the union of the primary keys of
entity sets E1, E2, …, En
If the relationship set R has attributes a1, a2, …, am associated with it,
then the primary key of R also includes the attributes a1, a2, …, am
Example: relationship set “advisor”
The primary key consists of instructor.ID and student.ID
The choice of the primary key for a relationship set depends on the
mapping cardinality of the relationship set
J. Pei: CMPT 354 — Database Design Using the E-R Model 36
Choice of Primary key for Binary Relationship
Many-to-Many relationships: the preceding union of the primary keys
is a minimal superkey and is chosen as the primary key
One-to-Many relationships: the primary key of the “Many” side is a
minimal superkey and is used as the primary key
Many-to-one relationships: the primary key of the “Many” side is a
minimal superkey and is used as the primary key
One-to-one relationships: the primary key of either one of the
participating entity sets forms a minimal superkey, and either one can
be chosen as the primary key
J. Pei: CMPT 354 — Database Design Using the E-R Model 37
Examples
J. Pei: CMPT 354 — Database Design Using the E-R Model 38
instructor student
ID
name
salary
ID
name
tot_cred
advisor
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
Weak Entity Sets
Consider a section entity, which is uniquely identified by a course_id,
semester, year, and sec_id
Clearly, section entities are related to course entities
Suppose we create a relationship set sec_course between entity sets
section and course
Note that the information in sec_course is redundant, since section already has an
attribute course_id, which identifies the course with which the section is related
One option to deal with this redundancy is to get rid of the relationship
sec_course; however, by doing so the relationship between section and
course becomes implicit in an attribute, which is not desirable
J. Pei: CMPT 354 — Database Design Using the E-R Model 39
Weak Entity Sets
An alternative way to deal with this redundancy is to not store the attribute
course_id in the section entity and to only store the remaining attributes
section_id, year, and semester
However, the entity set section then does not have enough attributes to
identify a particular section entity uniquely
To deal with this problem, we treat the relationship sec_course as a special
relationship that provides extra information, in this case, the course_id,
required to identify section entities uniquely
A weak entity set is one whose existence is dependent on another entity,
called its identifying entity
Instead of associating a primary key with a weak entity, we use the
identifying entity, along with extra attributes called discriminator to
uniquely identify a weak entity
J. Pei: CMPT 354 — Database Design Using the E-R Model 40
Weak Entity Sets
An entity set that is not a weak entity set is termed a strong entity set
Every weak entity must be associated with an identifying entity; that is, the
weak entity set is said to be existence dependent on the identifying entity
set
The identifying entity set is said to own the weak entity set that it identifies
The relationship associating the weak entity set with the identifying entity
set is called the identifying relationship
Note that the relational schema we eventually create from the entity set
section does have the attribute course_id, for reasons that will become
clear later, even though we have dropped the attribute course_id from the
entity set section
J. Pei: CMPT 354 — Database Design Using the E-R Model 41
Expressing Weak Entity Sets
In E-R diagrams, a weak entity set is depicted via a double rectangle
We underline the discriminator of a weak entity set with a dashed
line
The relationship set connecting the weak entity set to the identifying
strong entity set is depicted by a double diamond
Primary key for section – (course_id, sec_id, semester, year)
J. Pei: CMPT 354 — Database Design Using the E-R Model 42
Redundant Attributes
Suppose we have entity sets:
student, with attributes: ID, name, tot_cred, dept_name
department, with attributes: dept_name, building, budget
We model the fact that each student has an associated department using a relationship set
stud_dept
The attribute dept_name in student below replicates information present in the relationship and
is therefore redundant and needs to be removed
BUT: when converting back to tables, in some cases the attribute gets reintroduced, as we will see
later
J. Pei: CMPT 354 — Database Design Using the E-R Model 43
To-Do List
Should every one-to-many relation be represented as a weak entity
set
Can a weak entity set be many-to-many with respect to the
identifying strong entity set
Can an entity set itself be made a weak entity set through roles
J. Pei: CMPT 354 — Database Design Using the E-R Model 44
course
course_id
title
credits
course_id
prereq_id prereq
E-R Diagram for a University Enterprise
J. Pei: CMPT 354 — Database Design Using the E-R Model 45
Reduction to Relation Schemas
Entity sets and relationship sets can 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
J. Pei: CMPT 354 — Database Design Using the E-R Model 46
Representing Entity Sets
A strong entity set reduces to a schema with the same attributes
student(ID, name, tot_cred)
A weak entity set becomes a table that includes a column for the
primary key of the identifying strong entity set
section ( course_id, sec_id, sem, year )
Example
J. Pei: CMPT 354 — Database Design Using the E-R Model 47
Representation of Entity Sets with Composite
Attributes
§ Composite attributes are flattened out by creating a separate attribute for
each component attribute
Example: given entity set instructor 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
§ Prefix omitted if there is no ambiguity (name_first_name could be
first_name)
§ Ignoring multivalued attributes, extended instructor schema is
instructor(ID,
first_name, middle_initial, last_name,
street_number, street_name,
apt_number, city, state, zip_code,
date_of_birth)
J. Pei: CMPT 354 — Database Design Using the E-R Model 48
instructor
ID
name
first_name
middle_initial
last_name
address
street
street_number
street_name
apt_number
city
state
zip
{ phone_number }
date_of_birth
age ( )
Representation of Entity Sets with
Multivalued Attributes
§ 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 phone_number of instructor is represented
by a schema:
inst_phone= ( ID, phone_number)
§ Each value of the multivalued attribute maps to a separate tuple of the
relation on schema EM
For example, an instructor entity with primary key 22222 and phone
numbers 456-7890 and 123-4567 maps to two tuples:
(22222, 456-7890) and (22222, 123-4567)
J. Pei: CMPT 354 — Database Design Using the E-R Model 49
Representing Relationship Sets
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 advisor
advisor = (s_id, i_id)
J. Pei: CMPT 354 — Database Design Using the E-R Model 50
instructor
ID
name
salary
student
ID
name
tot_cred
advisor
Redundancy of Schemas
§ 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 inst_dept,
add an attribute dept_name to the schema arising from entity set
instructor
J. Pei: CMPT 354 — Database Design Using the E-R Model 51
student
ID
name
salary
ID
name
tot_cred
advisor
inst_dept stud_dept
instructor
department
dept_name
building
budget
t
Redundancy of Schemas
For one-to-one relationship sets, either side can be chosen to act as
the “many” side
That is, an 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 section schema already contains the attributes that
would appear in the sec_course schema
J. Pei: CMPT 354 — Database Design Using the E-R Model 52
Specialization
Top-down design process; we designate sub-groupings within an
entity set that are distinctive from other entities in the set
These sub-groupings 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., instructor “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
J. Pei: CMPT 354 — Database Design Using the E-R Model 53
Specialization Example
Overlapping – employee and student
Disjoint – instructor and secretary
Total and partial
J. Pei: CMPT 354 — Database Design Using the E-R Model 54
Representing Specialization via Schemas
Method 1:
Form a schema for the higher-level entity
Form a schema for each lower-level entity set, include primary key of higher-
level entity set and local attributes
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
J. Pei: CMPT 354 — Database Design Using the E-R Model 55
Representing Specialization as Schemas
Method 2:
Form a schema for each entity set with all local and inherited attributes
Drawback: name, street and city may be stored redundantly for people who
are both students and employees
J. Pei: CMPT 354 — Database Design Using the E-R Model 56
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
J. Pei: CMPT 354 — Database Design Using the E-R Model 57
Completeness Constraint
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
Partial generalization is the default
We can specify total generalization in an ER diagram by adding the keyword total in the
diagram and drawing a dashed line from the keyword to the corresponding hollow
arrow-head to which it applies (for a total generalization), or to the set of hollow arrow-
heads to which it applies (for an overlapping generalization)
The student generalization is total: All student entities must be either graduate or
undergraduate. Because the higher-level entity set arrived at through generalization is
generally composed of only those entities in the lower-level entity sets, the
completeness constraint for a generalized higher-level entity set is usually total
J. Pei: CMPT 354 — Database Design Using the E-R Model 58
Aggregation
Consider the ternary relationship proj_guide, which we saw earlier
Suppose we want to record evaluations of a student by a guide on a
project
J. Pei: CMPT 354 — Database Design Using the E-R Model 59
project
evaluation
instructor student
eval_ for
proj_ guide
Aggregation
Relationship sets eval_for and proj_guide represent overlapping
information
Every eval_for relationship corresponds to a proj_guide relationship
However, some proj_guide relationships may not correspond to any
eval_for relationships
So we can’t discard the proj_guide relationship
Eliminate this redundancy via aggregation
Treat relationship as an abstract entity
Allows relationships between relationships
Abstraction of relationship into new entity
J. Pei: CMPT 354 — Database Design Using the E-R Model 60
Aggregation
Eliminate this redundancy via aggregation without introducing
redundancy, the following diagram represents:
A student is guided by a particular instructor on a particular project
A student, instructor, project combination may have an associated evaluation
J. Pei: CMPT 354 — Database Design Using the E-R Model 61
evaluation
proj_ guide
instructor student
eval_ for
project
Reduction to Relational Schemas
To represent aggregation, create a schema containing
Primary key of the aggregated relationship
The primary key of the associated entity set
Any descriptive attributes
In our example:
The schema eval_for is:
eval_for (s_ID, project_id, i_ID, evaluation_id)
The schema proj_guide is redundant
J. Pei: CMPT 354 — Database Design Using the E-R Model 62
Common Mistakes in E-R Diagrams
J. Pei: CMPT 354 — Database Design Using the E-R Model 63
Common Mistakes in E-R Diagrams
J. Pei: CMPT 354 — Database Design Using the E-R Model 64
Entities vs. Attributes
Use of entity sets vs. attributes
Use of phone as an entity allows extra information about phone
numbers (plus multiple phone numbers
J. Pei: CMPT 354 — Database Design Using the E-R Model 65
instructor
ID
name
salary
phone
phone_number
location
instructor
ID
name
salary
phone_number
inst_phone
Entities vs. Relationship sets
Use of entity sets vs. relationship sets: possible guideline is to
designate a relationship set to describe an action that occurs between
entities
Placement of relationship attributes: for example, attribute date as
attribute of advisor or as attribute of student
J. Pei: CMPT 354 — Database Design Using the E-R Model 66
registration
…
…
…
section
sec_id
semester
year
student
ID
name
tot_cred
section_reg student_reg
Binary Vs. Non-Binary Relationships
Although it is possible to replace any non-binary (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.
Some relationships that appear to be non-binary may be better
represented using binary relationships
For example, 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 known)
But there are some relationships that are naturally non-binary
Example: proj_guide
J. Pei: CMPT 354 — Database Design Using the E-R Model 67
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 an identifying attribute for E and 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
J. Pei: CMPT 354 — Database Design Using the E-R Model 68
Converting Non-Binary Relationships
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
J. Pei: CMPT 354 — Database Design Using the E-R Model 69
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
J. Pei: CMPT 354 — Database Design Using the E-R Model 70
Summary of Symbols Used in E-R Notation
J. Pei: CMPT 354 — Database Design Using the E-R Model 71
E
R
R
A1
A2
A2.1
A2.2
{A3}
A4
E
entity set
relationship set
identifying
relationship set
for weak entity set primary key
discriminating
aribute of
weak entity set
total participation
of entity set in
relationship
aributes:
simple (A1),
composite (A2) and
multivalued (A3)
derived (A4)

A1
E
A1
E
R E
()
Symbols Used in E-R Notation
J. Pei: CMPT 354 — Database Design Using the E-R Model 72
R
R
R
role-
name
R
E
R
l..h E
E1
E2 E3
E1
E2 E3
E1
E2 E3

many-to-many
relationship
many-to-one
relationship
one-to-one
relationship
cardinality
limits

ISA: generalization
or specialization
disjoint
generalization
total (disjoint)
generaliza