Database Management Systems & Programming
LIS 558 - Week 2
Entity Relationship Modeling I
Faculty of Information & Media Studies
Summer 2000
Class Outline

Database Models
Database Design Problems
Conceptual Design Methodology

Break






Purpose of Data Modeling
Entity-Relationship Design
E-R Terminology
Entity-Relationship Method Examples
Database Models



A data model is the relatively simple
representation, usually graphic, of complex
real-world data structures. It represents data
structures and their characteristics, relations,
constraints, and transformations.
The database designer usually employs data
models as communications tools to facilitate the
interaction among the designer, the applications
programmer, and the end user.
A good database is the foundation for good
applications.
Database Models

Two Categories of Database Models
• Conceptual models focus on the logical nature
of the data representation. They are
concerned with what is represented rather
than how it is represented.
• Implementation models place the emphasis
on how the data are represented in the
database or on how the data structures are
implemented.
Database Models

Three Types of Implementation
Database Models
• Hierarchical database model
• Network database model
• Relational database model
A Hierarchical Structure
Database Models

Hierarchical Database Model
• Basic Structure
– Collection of records logically organized to
conform to the upside-down tree (hierarchical)
structure.
– The top layer is perceived as the parent of the
segment directly beneath it.
– The segments below other segments are the
children of the segment above them.
– A tree structure is represented as a hierarchical
path on the computer’s storage media.
Database Models

Hierarchical Database Model
• Advantages
–
–
–
–
–
Conceptual simplicity
Database security
Data independence
Database integrity
Efficiency dealing with a large database
• Disadvantages
–
–
–
–
–
–
Complex implementation
Difficult to manage
Lacks structural independence
Applications programming and use complexity
Implementation limitations
Lack of standards
Database Models

Network Database Model
• Basic Structure
– Set -- A relationship is called a set. Each set is
composed of at least two record types: an owner
(parent) record and a member (child) record.
– A set is represents a 1:M relationship between the
owner and the member.
A Network Database Model
Database Models

Network Database Model
• Advantages
–
–
–
–
–
–
Conceptual simplicity
Handles more relationship types
Data access flexibility
Promotes database integrity
Data independence
Conformance to standards
• Disadvantages
– System complexity
– Lack of structural independencem
Evolution of Conceptual Data Models
The Evolution of Conceptual Data Models

Common characteristics required for
data models:
• A data model must show some degree of
conceptual simplicity without
compromising the semantic completeness.
• A data model must represent the real
world as closely as possible.
• The representation of the real-world
transformations (behavior) must be in
compliance with the consistency and
integrity characteristics of any data
model.
Database Models

Relational database
• Codd, E.F. (1970). A relational model for large
shared data banks. CACM, 13(6), 377-87.
–First relational prototype - IBM’s system/R
–Other variants, e.g., INGRES - UC, Berkeley
–1983 IBM released DB2

Relational databases required considerable
computing resources (not feasible until
mid- 1980s)
–low end (Access, Paradox, dBase, FoxPro, Clipper)
–high end (DB2, Oracle, Sybase, Informix, INGRES
commercial)
Database Models

Relational Database defined:
• Logical database model that treats data as if they are
stored in separate two-dimensional but related tables
• Each table consists of data elements describing a
common theme among which is one (or more) elements
that uniquely describes each record in the table
• Tables are related as long as two tables share a
common data element
• Information in these tables cam be combined on an asneeded basis to get answers to queries and generate
complex reports
Database Models

Advantages
•
•
•
•
•
•
•

But #1 problem is still
Mechanisms for minimizing data redundancy and inconsistency!
inconsistency
Logical database design is separated from physical aspects
Relatively program-data independent
Management of data for access, manipulation, and security
Flexible mechanisms for generating reports and queries
Program development and maintenance costs are reduced
Data can be accessed in a multiplicity of ways within and
amongst organizations
Disadvantages
• Ease of use - many untrained people create and use databases
without considering its design - usually incorporate many
errors
• Processing resources required
The Relational Database Model
Agents
Clients
Instruments
Entertainers
Engagements
Entertainer styles
• represented by tables (like spreadsheets)
• tables are linked with software pointers
• unlike earlier systems, all three types of relationships can be
represented
• accommodates the design of larger databases that involve
complex relationships and intricate manipulations
Linking Relational Tables
Database Models

Entity-Relationship Data Model
• It is one of the most widely accepted
graphical data modeling tools.
• It graphically represents data as entities
and their relationships in a database
structure.
• It complements the relational data model
concepts.
Database Models

E-R model is commonly used to:
• Translate different views of data among
managers, users, and programmers to fit
into a common framework.
• Define data processing and constraint
requirements to help us meet the
different views.
• Help implement the database.
Database Models

Entity Relationship Data Model
• Basic Structure
– E-R models are normally represented in an
entity relationship diagram (ERD).
– An entity is represented by a rectangle.
– Each entity is described by a set of attributes.
An attribute describes a particular
characteristics of the entity.
– A relationship is represented by a diamond
connected to the related entities.
Database Models

Three Types of Relationships
• One-to-many relationships (1:M)
– A painter paints many different paintings, but each one
of them is painted by only that painter.
• PAINTER (1) paints PAINTING (M)
• Many-to-many relationships (M:N)
– An employee might learn many job skills, and each job
skill might be learned by many employees.
• EMPLOYEE (M) learns SKILL (N)
• One-to-one relationships (1:1)
– Each store is managed by a single employee and each
store manager (employee) only manages a single store.
• EMPLOYEE (1) manages STORE (1)
Relationship Depiction: The ERD
Database Models

Entity-Relationship Data Model
• Advantages
– Exceptional conceptual simplicity
– Visual representation
– Effective communication tool
– Integrated with the relational database model
• Disadvantages
– Limited constraint representation
– Limited relationship representation
– No data manipulation language
– Loss of information content
Introduction to Database Design
Database schema defines database’s structure,
tables, relationships, domains, and constraint rules

Tables
• BOOK (ISBN, Title, AuthID, PubID, Price)
• PUBLISHER (PubID, PubName, PubPhone)
• AUTHOR (AuthID, AuthName, AuthPhone)

Relationships
• Each book is published by one and only one publisher
• Each publisher publishes one or more books

Domains (set of values in a column)
• Physical description (e.g., set of integers 0 < x < 99999)

Constraints (business rules)
• Price cannot be less than zero; Author phone field
cannot be left blank
Database Terminology

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Tables hold the data
Database design is the process of separating
information into multiple tables that are
related to each other
Single table designs work only for the
simplest of situations
Anomalies often arise in single table designs
as a result of inserting, deleting, or updating
records
Table
Users view their data in two-dimensional tables.
table
=
file
=
relation
Field
The fields within records contain data. Data within a field
must be of the same data type. Each field within a table
must have a unique name. Order of fields is unimportant.
column =
field
=
attribute
Record
A record is a group of related fields of information about a
single instance of one object or event in a database.
Tables consist of zero, one, or more records.
Order of rows is unimportant.
row
=
record =
tuple
Data Dictionary and the System Catalog


Data dictionary contains metadata to provide
detailed accounting of all tables within the
database.
System catalog is a very detailed system data
dictionary that describes all objects within
the database.
• System catalog is a system-created database whose
tables store the database characteristics and
contents.
• System catalog tables can be queried just like any
other tables.
• System catalog automatically produces database
documentation.
Sample Data Dictionary
Keys


A key helps define entity relationships.
The key’s role is based on a concept known as
determination, which is used in the definition of
functional dependence.
– The attribute B is functionally dependent on A if A
determines B.
– An attribute that is part of a key is known as a key
attribute.
– A multi-attribute key is known as a composite key.
– If the attribute (B) is functionally dependent on a
composite key (A) but not on any subset of that
composite key, the attribute (B) is fully functionally
dependent on (A).
Keys



Controlled redundancy (shared common
attributes) makes the relational database work.
The primary key of one table appears again as
the link (foreign key) in another table.
If the foreign key contains either matching
values or nulls, the table(s) that make use of
such a foreign key are said to exhibit
referential integrity.
Indexes

An index is composed of an index key and a set
of pointers.
Relational Database Keys
Integrity Rules
Database Components

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Tables
Queries
Forms
Reports
Modules
Major Components of a Database Application
1. Form- data entry
3. Query- asks questions
2. Report- summarizes & prints
4. Menu - organizes components
5. Program - used to automate a database
Levels of Database Representation

Three levels of representation
• external
• conceptual
• internal

- user views of data
- abstract description of data
- physical implementation
access methods, index
construction, data structures
Starting point for design?
• Conceptual general description which is then
represented in terms of the data contained in the
database

Conceptual level is primary focus of this
course
A Logical View of Data




Relational database model’s structural and
data independence enables us to view data
logically rather than physically.
The logical view allows a simpler file concept
of data storage.
The use of logically independent tables is
easier to understand.
Logical simplicity yields simpler and more
effective database design methodologies.
What is Relational Database Design?

Relational Database Summary
• logical database model that treats data as if
they are stored in separate but related tables
• Tables are related as long as two tables share
common data element
• Information in these tables can be combined
on an as-needed basis to retrieve answers to
queries and to generate complex reports
What is Relational Database Design?
Why use a Relational Data Model Design?




Small databases can easily be maintained as a
single flat file (like a spreadsheet)
For design of larger databases that involve
complex relationships and intricate
manipulations, a relational model is essential
Originally the major limitation of relational
model was memory and processing speed but
this is no longer the case
Relational design model resolves a number of
potential problems
Database Design Problems

Numerous anomalies can arise during the
design of databases
•
•
•
•
•
Redundancy
Multi-valued problems
Update anomalies
Insertion anomalies
Deletion anomalies
Database Design Problems

Redundancy
• unnecessary repetition of data
Database Design Problems

Multi-valued problems
• e.g., 1 - Add multiple rows, one for each value
•Data about a book must be repeated for as many times
as there are authors of a book (also creates redundancy)



Database Design Problems

Multi-valued problems
• e.g., 2 - Add multiple columns, one for each value
•How many columns for authors must be included in the
design (empty fields waste space too)?



Database Design Problems

Multi-valued problems
• e.g., 3 - Include all author’s names in a single field
•How do you search for a single author’s name or create an
alphabetical list of authors
Database Design Problems

Update Anomalies
• To update an author’s telephone, each
instance must be changed
• if we miss an item or enter it incorrectly
we create an unreliable table
• sometimes previous errors propagate
errors further
Database Design Problems

Update Anomalies
• e.g., Consider the author Austen in the following table.
What happens if we change her telephone number?
Propagation
of Error
Database Design Problems

Insertion anomalies
• What happens if we want to enter information
regarding a publisher for whom we do not have
book information?
• Do we add null values for the other fields?
Database Design Problems

Deletion anomalies
• What happens if we delete all the book
entries for a given publisher?
Database Design Problems




Use of the relational database model removes
some database anomalies
Further removal of database anomalies relies
on a structured technique called normalization
Proper use of foreign keys is crucial to
exercising data redundancy control
Presence of some of these anomalies is
sometimes justified in order to enhance
performance
Class Outline

Database Models
Database Design Problems
Design Exercise

Break
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

Database Design Methodology
Purpose of Data Modeling
Entity-Relationship Design
E-R Terminology
Entity-Relationship Method Examples
Characteristics of a Database designer

Knowledge of the problem you are trying to solve

Communication skills - extensive discussions with users

Analytical aptitude - keep in mind the broad goals even
while poring over the smallest details

Impertinence - question everything!

Impartiality - find best solution

Relax constraints - assume anything is possible

Pay attention to details and definitions

Reframing - iteratively analyze in new way - be creative!
A good designer combines the art of design with the science of design.
Conceptual Design Methodology
1. Define the problem and define database
objectives
2. Analyze current database, assess user
requirements, and create data model
3. Design data structures (tables, fields, field
specifications, establish keys)
4. Establish table relationships
5. Clarify business rules critical to database
design (e.g., required fields, validation rules)
6. Determine and establish user views of data
7. Review data integrity and reiterate design
methodology
Statement of Purpose
1. Declare a specific purpose for the database to
focus and guide its development
e.g., “The purpose of the All-Star Talent database is to
maintain the data we use in support of the entertainment
services we provide to our clientele.”
2. Articulate goals & objectives that define
specific tasks
“We need to maintain complete entertainer information.”
“We need to maintain complete customer information.”
“We need to track all customer-entertainer bookings.”
“We need to maintain financial records of both payments
from customers and payments to entertainers.”
Assessment of User Requirements:
What is analyzed?

interview transcripts

meeting minutes

observational notes
• reports
business mission and
strategy statements
• presentations


questionnaire results

document analyses
• business forms
• flow charts
• computer-generated
output
• training manuals
• consultant reports
• job descriptions
Assessment of User Requirements:
Specific requirements
Goals of analysis of user requirements: collect a list of business goals,
entities to track, a database schema, and sample report outputs.

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

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What are subjects/objects for the business?
What characteristics describe each object?
What unique characteristic distinguishes each object
from other objects of the same type?
How do you use this data (e.g, summary reports)?
Over what period of time are you interested in this data?
Are all instances of each object the same?
What events occur that imply associations between
various objects?
Is each activity or event always handled the same way or
are there special circumstances?
Rules for Conducting User Interviews

Create a quiet, stress-free environment; set a limit of six people

Have an agenda - provide it to participants ahead of time

Focus on the problem at hand; maintain control of the interview

Conduct separate interviews for users and management

Identify the decision maker

Avoid technical jargon

Show concern for user needs

Give everyone equal and undivided attention

Write down everything where it can be seen by participants

Encourage ‘blue sky’ thinking

Arbitrate disputes

Keep the pace of the interview moving

Don’t foreclose your options too soon
Data Modeling





A model is a simplified representation (usually a graphic)
of a complex object in reality to make it understandable
If the elements in the model are correctly associated with
elements in reality, the model can be used to solve
problems in reality (e.g., engineer’s model to determine a
bridge’s weight tolerance; if the model is incorrect...)
an ER model is integrated set of concepts that describes
data, relationships between data, and the constraints on
the data as they are used within a specific organization; a
data model renders organization’s (users’) view of objects
and/or events and their associations
ER model is a blueprint from which a well-structured
database is created
ER models are independent of details of implementation
Purpose of Data Modeling

High level description
• integrated set of concepts that describes data,
relationships between data, and the constraints on the
data as they are used within a specific organization

View of objects or events
• data model renders organization’s (users’) view of
objects and/or events and their associations


Representation of data in a simplified
fashion that makes it understandable
Once established, database design is
relatively straightforward
E-R Modeling Concepts
1 : 1
Connectivity
Entities
M : N
Relationship
Type
Participation
Objects
Cardinality
Relationships
Degree
Recursive
Binary
Domains
Attributes
Values
1 : N
Ternary
N-ary
Mandatory
Optional
Entities

Entity
• Something that can be identified in the users’
environment about which we want to store data;
typically is a noun
• Entities or objects must have occurrences that can
be uniquely identified
• Identified by an organization or its users
• Consists of tangible or intangible objects or events

Entity Instance
• A single entity occurrence or instance within a
collection of entities
e.g., STUDENT is an entity; Annie Abel is an entity
instance as are Bob Brown and Cathy Chen.
STUDENT
Attributes

properties that describe characteristics of an
entity - assumed all instances of a given entity
have the same attributes
•
•
•
•
use atomic attributes, those that cannot be divided
further (e.g., not composite attributes (e.g., use last name
& first name, not name)
do not use derived attributes (attributes that can be
calculated using other attributes; e.g., age)
use single value attributes not multi-valued (e.g.,
medication1, medication2, etc.)
multi-valued attributes, if they have their own important
attributes should be elevated to entities
e.g., attributes of the
entity STUDENT might
include name, address,
etc.
phone #
first name
last name
STUDENT
photo
birth date
Identifiers




Each entity occurrence has a unique identifier
The identifier is an attribute (or group of
attributes) that describes or identifies each
entity occurrence
An identifier should be unique to each
occurrence
Referred to as a ‘primary key’ in relational
models
e.g., in the list of potential attributes
of the entity STUDENT, the identifier
could be Student Number.
STUDENT
StudentID, ...
Relationships

Association or connection between two or
more entities
•
•
•
Usually a verb
HAS-A is also a common relationship
(EMPLOYEE-has a-DEPENDENT)
E-R model also contains relationship classes
STUDENT
StudentID, ...
takes
COURSE
CourseID, ...
Degree of Relationship: Binary
In a binary relationship, two entities are associated.
This is the most common degree of relationship.
VACATIONER
EMPLOYEE
takes
works
for
TRIP
DEPARTMENT
Degree of Relationship: Ternary
In a ternary relationship, three entities are
associated.
WRITER
ILLUSTRATOR
CUSTOMER
WAREHOUSE
create
order
DESIGNER
ITEM
Degree of Relationship: Unary (Recursive)
In a recursive relationship, one entity is associated
with itself.
TEAM
COURSE
plays
requires
Recursive Relationships

A relationship can exist between
occurrences of the same entity set
Implementation of the M:N Recursive
“PART Contains PART” Relationship
Implementation of the M:N “COURSE Requires
COURSE” Recursive Relationship
Implementation of the 1:M “EMPLOYEE Manages
EMPLOYEE” Recursive Relationship
Connectivity


Connectivity describes constraints on relationship
(also referred to as “maximum cardinality”)
Number of instances of entity B that can (or must) be
associated with each instance of entity A
One-to-Many
Child
One-to-One
Employee
Many-to-Many
Musician
1
rents
M
1
has
1
M
sings
N
Toy
Office
Song
Representing M:N binary relationships



M:N relationships are represented by two 1:M
relationships.
the relationship is itself an entity, called a composite
entity (rectangle around the diamond)
The composite entity often has its own attributes
STUDENT
STUDENT
M
1 M
Date
N
enrolls in
enrolls in
M
CLASS
1
Mark
CLASS
Cardinality


Cardinality is the specific number of entity
occurrences associated with one occurrence of the
related entity
often referred to as ‘business rules’ because
cardinality is usually determined by organizational
policy
e.g., at a toy lending library, a given child may not borrow any toys
at all or they may borrow more than one (up to 3) toys. A toy may
not be borrowed by anyone, or it may be borrowed by one child.
Child
1
(0,3)
borrows
M
(0,1)
Toy
Occurrences Diagram
Pictorial mapping of the occurrences between two entities
assists in understanding connectivity and cardinality
C1
C2
C3
C4
C5
C6
T1
T2
T3
T4
T5
T6
A child may rent between 0 and 3 toys; a toy may only be rented by 0
or 1 child. One child may rent many toys (1:M)
Relationship Participation
Also referred to as “minimum cardinality”
 Mandatory Participation
• An instance of a given entity must definitely match an
instance of a second entity
• e.g., each student must enroll in exactly one course
 Optional Participation
• An instance of a given entity does not necessarily
participate in the relationship
• lower bound of cardinality is zero
• e.g., a faculty member teaches zero, one, or two
courses
1
N
DONATION
MEMBER
makes

MANDATORY
(0,N)
(1,1)
OPTIONAL
a member may or may not make a donation but a donation must be
associated with a member
Example: Customers & Orders

From the CUSTOMER perspective:
– a customer may make many orders (M orders of 1:M
connectivity)
– a customer does not necessarily make orders (optional
participation of orders, cardinality is (0,N))
1
CUSTOMER
makes
(0,N)

M
ORDER
(1,1)
From the ORDER perspective:
– an order is made by (associated with) one and only one
customer (1 customer of 1:M connectivity)
– an order must be made by (associated with) a customer
(mandatory participation, cardinality is (1,1))
Example: Customers & Orders
common field
parent table
related
table
Entity-Relationship Design




First step - develop a list of entities and
attributes that are of interest to the
enterprise
Entities or objects must have occurrences
that can be uniquely identified
E-R Design consists of determining entities,
attributes, relationships, relationship types,
level of participation in relationships,
identifiers, and then drawing an E-R Diagram
E-R Diagram (model) is a blueprint from which
a well-structured database is created
Steps in Entity-Relationship Modeling
1. Identify entities
2. Identify relationships
3. Determine relationship type
4. Determine level of participation
5. Assign an identifier for each entity
6. Draw completed E-R diagram
7. Deduce a set of preliminary skeleton tables along with
a proposed primary key for each table (using rules
provided)
8. Develop a list of all attributes of interest (not already
listed and systematically assign each to a table in
such a way to achieve a 3NF design (i.e., no repeating
groups, no partial dependencies, and no transitive
dependencies)
E-R Method Example: Library Database

Step 1. Identify entity types
AUTHOR

BOOK
PUBLISHER
Step 2. Identify relationships
AUTHOR
writes
BOOK
PUBLISHER
publishe
s
BOOK
Library Database

(cont’d)
Step 3. Determine relationship type. Ask:
• Each book is written by how many authors? Each author writes
how many books?
• Each book may be authored by zero (anonymous), one, or more
than one author and each author may write zero, one, or more
than one book. The relationship type is many-to-many or:
AUTHOR
•
M
writes
N
BOOK
For PUBLISHER-publishes-BOOK, each publisher publishes
zero, one, or more books and each book is published by
exactly one publisher. The relationship type is one-to-many
where BOOKS is on the many side and PUBLISHER is one
the one side.
PUBLISHER
1
publishe
s
M
BOOK
Library Database

(cont’d)
Step 4. Determine level of participation
• Since each book does not have to be authored (anonymous)
and since each author does not have to write a book (may
make CD) the level of participation is optional for both sides
of the relationship of AUTHOR-writes-BOOK combination
AUTHOR
M
writes
(0, N)
•
N
BOOK
(0, N)
For the PUBLISHER-publishes-BOOK combination, the level
of participation for PUBLISHER is optional (publishers do
not necessarily have to publish a book, perhaps newsletters)
and the level of participation for the BOOK side is
mandatory (each book must have a publisher)
PUBLISHER
1
(0, N)
publishe
s
N
BOOK
(1,1)
Library Database


(cont’d)
Step 5. Assign an identifier for each entity
• AuthorID, ISBN, PublisherID
Step 6. Draw completed E-R diagram
AUTHOR
M
AuthorID, ... (0,N)
writes
ISBN, ...
BOOK
N
(0,N)
N
(1,1)
publishes
1
PUBLISHER
PublisherID, ...
(0,N)
Library Database

(cont’d)
Step 6. Draw completed E-R diagram - resolve
M:N relationships
AUTHOR
1
M
writes
M
1
BOOK
(0,N)
(0,N) (1,1)
(1,1)
AuthorID, ...
AuthorID,ISBN, ... ISBN, ...
M (1,1)
publishes
1 (0,N)
PUBLISHER
PublisherID, ...
E-R Modeling: University Example

A database is to be set up to record information about
faculty, the courses they teach, and the students who
take courses. Some courses are taught by teams of
faculty members.
• Step 1. Identify entity types
FACULTY
COURSE
STUDENT
• Step 2. Identify relationships
FACULTY
teaches
COURSE
STUDENT
takes
COURSE
University Example

Step 3. Determine relationship type. Ask:
–
–
–
–
F1
F2
F3
F4
F5
F6
(cont’d)
Each faculty member teaches how many courses?
Each course is taught by how many faculty?
Each student takes how many courses?
Each course is taken by how many students?
• Use occurrences diagram to visualize relationship
between entities
C1
C2
C3
C4
C5
C6
S1
S2
S3
S4
S5
S6
C1
C2
C3
C4
C5
C6
University Example

(cont’d)
Step 3. Determine Relationship type (cont’d)
• For FACULTY-teaches-COURSE we are told each faculty
member teaches zero, one, or two courses. We are told some
courses are taught by zero, one, two, or three faculty. This is a
many-to-many relationship.
FACULTY
M
teaches
N
COURSE
• For STUDENT-takes-COURSE each student enrols in one to six
courses and each course is taken by zero or up to 30 students.
This too is a many-to-many relationship.
STUDENT
M
takes
N
COURSE
University Example

(cont’d)
Step 4. Determine level of participation
• FACULTY-teaches-COURSE - level of participation is optional,
since sometimes Faculty do not have to teach (e.g., sabbatical);
similarly, a course may not have anyone interested in teaching it
FACULTY
M
teaches
(0,2)
N
COURSE
(0,3)
• STUDENT-takes-COURSE - level of participation is mandatory
since students must take at least one course; a course,
however, may or may not have students taking it
STUDENT
M
(1,6)
takes
N
(0,30)
COURSE
University Example

(cont’d)
Step 5. Assign an identifier for each entity
• FacultyID, CourseID, StudentID

Step 6. Draw completed E-R diagram
N
M
COURSE
(0,3)
CourseID, ...
M
taught by
(0,2)
N
FACULTY
FacultyID, ...
taken by
(0,30)
STUDENT
(1,6)
StudentID, ...
University Example




(cont’d)
You are now told that in addition to the relationships given,
each student is assigned a faculty advisor who gives direction in
choosing courses.
Use occurrences diagram to visualize relationship between
entities
We are told each student is advised by exactly one faculty
advisor. We can assume that each faculty member advises
zero, one, or more students. This means the additional
relationship is of type one-to-many or 1:M.
The STUDENT is on the many side of the relationship and must
be advised therefore, faculty is mandatory to student;
FACULTY on the one side of the relationship may or may not
have a student, therefore student is optional to faculty.
1
FACULTY
(0,N)
advises
M
STUDENT
(1,1)
University Example (cont’d)

Step 6. Draw completed E-R diagram
1 M
STUDENT
M
(1,1)
(1,6)
takes
(1,1)
M1
COURSE
(1,1)
(0,30)
CourseID, ...
1 (0,3)
M (1,1)
StudentID, ...
taught
by
M (1,1)
1 (0,2)
(0,N)
1
FACULTY
FacultyID, ...
Evaluation of the E-R Model




Using data models to conceptualize the design of a
database saves time and money because a completed E-R
diagram is the actual blueprint of the database. Its
composition must reflect an organization's operations
accurately if the database is to meet that organization's
data requirements.
The completed E-R diagram also lets the designer
communicate more precisely with those who commissioned
the database design. It’s easier to correct design flaws at
the data modeling stage.
Do not confuse entities and relationships with actual
tables. The transformation or decomposition of E-R
models will be discussed within the next few weeks.
E-R modeling is an iterative process. Even when complete,
ER models generally do not provide a complete picture
(e.g., business rules cannot always be shown), therefore,
much additional documentation is necessary.
Week 2 - Exercises 1-2
Week After Next

More entity-relationship modeling
exercises

Transformation of E-R Models

Read Rob Chapter 4

Complete Adamski’s tutorial 3