微信客服：xiaoxionga100
微信客服：ITCS521
sql代写-CMPT 354
时间：2021-11-20
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 Î E1, e2 Î E2, …, en Î En}
where (e1, e2, …, en) is a relationship
• Example
(44553, 22222) Î 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)
generalization
role indicator
total
Alternative ER Notations
J. Pei: CMPT 354 -- Database Design Using the E-R Model 73
entity set E with
simple aribute A1,
composite aribute A2,
multivalued aribute A3,
derived aribute A4,
and primary key A1
A1
A2
A3
A2.1 A2.2
A4E
generalization ISA ISA
total
generalizationweak entity set
Alternative ER Notations
J. Pei: CMPT 354 -- Database Design Using the E-R Model 74
participation
in R: total (E1)
and partial (E2)
E1 E2 E2E1R
R
R
many-to-many
relationship
one-to-one
relationship
many-to-one
relationship
R
R
*
*
*
1
1
1
R
E1
E1
E1
E2
E2
E2 E1 E2
RE1 E2
RE1 E2
UML
• UML: Unified Modeling Language
• 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
J. Pei: CMPT 354 -- Database Design Using the E-R Model 75
ER vs. UML Class Diagrams
J. Pei: CMPT 354 -- Database Design Using the E-R Model 76
* Note reversal of position in cardinality constraint depiction
–A1
+M1
E
binary
relationship
class with simple aributes
and methods (aribute
prefixes: + = public,
– = private, # = protected)
A1
M1
E entity with
aributes (simple,
composite,
multivalued, derived)
R
E2E1 role1 role2
relationship
aributes E2E1
role1 role2
A1
R
R cardinalityconstraintsE2E1
R
E2E10.. * 0..1 0..1 0.. *
ER Diagram Notation Equivalent in UML
R E2E1 role1 role2
R E2E1 role1 role2
A1
() ()
ER vs. UML Class Diagrams
J. Pei: CMPT 354 -- Database Design Using the E-R Model 77
ER Diagram Notation Equivalent in UML
* Generalization can use merged or separate arrows independent of disjoint/overlapping
E2 E3
E1
E2 E3
E1
E2 E3
overlapping
generalization
disjoint
generalization
R
E3
E1
E2
R
E3
E1
E2n-ary
relationships
E1
E2 E3
overlapping
disjoint
E1
UML Class 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
J. Pei: CMPT 354 -- Database Design Using the E-R Model 78
ER vs. UML Class Diagrams
J. Pei: CMPT 354 -- Database Design Using the E-R Model 79
Other Aspects of Database Design
• Functional Requirements
• Data Flow, Workflow
• Schema Evolution
J. Pei: CMPT 354 -- Database Design Using the E-R Model 80
Summary
• 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 81
学霸联盟
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 Î E1, e2 Î E2, …, en Î En}
where (e1, e2, …, en) is a relationship
• Example
(44553, 22222) Î 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)
generalization
role indicator
total
Alternative ER Notations
J. Pei: CMPT 354 -- Database Design Using the E-R Model 73
entity set E with
simple aribute A1,
composite aribute A2,
multivalued aribute A3,
derived aribute A4,
and primary key A1
A1
A2
A3
A2.1 A2.2
A4E
generalization ISA ISA
total
generalizationweak entity set
Alternative ER Notations
J. Pei: CMPT 354 -- Database Design Using the E-R Model 74
participation
in R: total (E1)
and partial (E2)
E1 E2 E2E1R
R
R
many-to-many
relationship
one-to-one
relationship
many-to-one
relationship
R
R
*
*
*
1
1
1
R
E1
E1
E1
E2
E2
E2 E1 E2
RE1 E2
RE1 E2
UML
• UML: Unified Modeling Language
• 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
J. Pei: CMPT 354 -- Database Design Using the E-R Model 75
ER vs. UML Class Diagrams
J. Pei: CMPT 354 -- Database Design Using the E-R Model 76
* Note reversal of position in cardinality constraint depiction
–A1
+M1
E
binary
relationship
class with simple aributes
and methods (aribute
prefixes: + = public,
– = private, # = protected)
A1
M1
E entity with
aributes (simple,
composite,
multivalued, derived)
R
E2E1 role1 role2
relationship
aributes E2E1
role1 role2
A1
R
R cardinalityconstraintsE2E1
R
E2E10.. * 0..1 0..1 0.. *
ER Diagram Notation Equivalent in UML
R E2E1 role1 role2
R E2E1 role1 role2
A1
() ()
ER vs. UML Class Diagrams
J. Pei: CMPT 354 -- Database Design Using the E-R Model 77
ER Diagram Notation Equivalent in UML
* Generalization can use merged or separate arrows independent of disjoint/overlapping
E2 E3
E1
E2 E3
E1
E2 E3
overlapping
generalization
disjoint
generalization
R
E3
E1
E2
R
E3
E1
E2n-ary
relationships
E1
E2 E3
overlapping
disjoint
E1
UML Class 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
J. Pei: CMPT 354 -- Database Design Using the E-R Model 78
ER vs. UML Class Diagrams
J. Pei: CMPT 354 -- Database Design Using the E-R Model 79
Other Aspects of Database Design
• Functional Requirements
• Data Flow, Workflow
• Schema Evolution
J. Pei: CMPT 354 -- Database Design Using the E-R Model 80
Summary
• 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 81
学霸联盟
