Data modeling

Conceptual Data Modeling
Using E-R Models

PM Jat
pm_jat@daiict.ac.in

Data Model
 Data modeling is the most important task in database
development
 completeness – correctness – performance of database
depends on correct data model
 …shows up how data are structured at
Conceptual, Logical and Physical level

 More formally, it is a collection of concepts that can be
used to describe “Database Structure”
- Elmasri/Navathe

 “Database Structure”, we mean here -
data types, relationships, and constraints
Aug 14, 2008 Data Modeling Using E-R Models 2

Why Data Modeling

 It is essential step of database design, not optional

 If no, it is like making a house without a plan!

 Sometimes, in smaller applications, it may not be
explicitly done or documented still it is done


Phases of Database Design
 Requirement Analysis – Understand the business
needs and Data Components within.
 Conceptual Design
 Develop Higher/Conceptual Data Model – easily understandable by End-
Users and Managers….. what this talk is about

 Logical Design
 Develop Logical/Implementation Schema in one of the implementation
technologies like Relational, Object Relational, OO. Historically
Hierarchical/Network Models

 Physical Design
 Develop Internal Schema – how files are stored on disk –
deal with file organization, indexes, clustering etc.


Conceptual Model
 Correct and Formal representation of data items
are the objectives here
 Intuitive and Simple to understand – end users
and managers can easily understand
 Centers on “what data items and business rules
it contains” not on how they are stored or
organized in application (or structured in a
DBMS logically or physically)
 ER Diagrams and UML class diagrams are
popular Conceptual models
 Implementation neutral

E-R Diagram (Model ? )
 As said, it is a popular high level Conceptual
Data Model

 ER Diagram are simple consists of just -
 Entities – their attributes
 Relationship between entities
 Constraints on Entities and Relationship

 Let us understand what these terms mean here?


How we see data around us-
List of Students IIyr BTech(CS)

StudID Name DOB Fasther’s Address …………..
Name
060101 Aditi 12-10-88 … …… ………..

060102 Anurag 10-02-89 … …… ………..

060103 Arpit 11-07-88 … …… ………..
List of Programs Offered by CSE Department
….. …… …… … …… ………..
ProgID ProgName Intake

BCS BTech(Computer Science) 40 …

BIT BTech(Information Technology) 40 …

….. ……. ….. …

….. …… …… …


How do we describe this data
 Careful watch reveals that we have two type of “entity
sets” here-
 Students and Programs
 Let us call all students together entity set, and Program
together another entity set

 Each Student entity has certain properties (or attributes)
like Name, DOB, Fathers Name etc., and their values
drawn from a predefined “value set”,

 and similarly each program has certain attributes, and
their values


How do we describe this data
 If we attempt to describe student entity, typically it can
be like this-
 Name of Entity: Student
 Attributes: StudID, Name, DOB, FathersName, ..
 Each Attribute derives value from a value set (domain)
 StudID uniquely identifies a student entity in the set

 This is description of Student entity, and we call it
“Entity Type”

 Similarly we describe Program Entity Type


ERD notation for the Entity types

Notations used here are,
originally suggested by
Dr Peter Chen in ’76,
known as Chen’s notation


Entities and Attributes
 Entity is the basic object in ER model. It is
thing which exists
 Each entity has attributes- properties that
describe the entity, for example as we have
seen-
 Entity: Student
Attributes: StudID, Name, DOB, Father’s
Name, Address,
 Entity: Program
Attributes: ProgID, ProgName, Intake

 Attributes could be
 Atomic – can not be meaningfully decomposed:
Example DOB can not be decomposed in
Day/Month/Year – DOB is atomic data
 Student-Name may or may not be atomic
 If FirstName, LasteName have individual meaning
 Key Attributes
 Composite: Example Address (Street, City, PIN)
 Single valued and multi-valued
 Stored and Derived attributes


Entities and Attributes in ERD
Composite
Attribute

Key
Attribute

Multi-value
Attribute

Key attribute Uniquely Identifies an Entity in Entity Set


Derived
Attribute


Entity Type
 Casually, very often term “entity” is used for “Entity Type”.
 An Entity Type describes schema or intention for a set of
entities sharing the same structure.
 In OO terminology, an entity type is similar to a class, and an
entity is similar to an instance

Definition (Elmasri/Navathe):
Entity Type defines a collection (or set)
of entities that have the same attributes.
Each Entity Type in the database
is described by its name and attributes


Entity Set
 The collection of entities of a particular entity type are
grouped into an entity set, is also called extension of entity
type.
 The entity set usually referred with the same name as of
Entity Type

StudID Name DOB Fasther’s Address …………..
Name
060101 Aditi 12-10-88 … …… ………..

060102 Anurag 10-02-89 … …… ………..
Definition (Elmasri/Navathe):
060103 Arpit 11-07-88 … …… ………..
The collection of all entities of a particular type
in the database….. any…… of …… is …
at point time called an entity set.
…… ………..


Key Attributes
 It is Attribute or set of Attributes, called Key Attribute
which are used to uniquely identify the entity in the set
 An important constraint on entities of an entity type is
the key constraint or uniqueness constraint.

Key StudID Name DOB Address
Attribute
060101 Aditi 12-10-88 ……

060102 Anurag 10-02-89 ……

060103 Arpit 11-07-88 ……

….. …… …… ……


Value Sets (Domains) of Attributes
 Each simple attribute of an entity type is
associated with a value set (or a domain of
values), which specifies the set of values that
may be assigned to that attribute for each
individual entity
 Also known as domain constraint on an
attribute


Relationship Type, Instance, and Set
 Let us look at following facts –
 Aditi studies in BIT
 Amit studies in BCS
 Arpit studies in BIT
 Varun studies in BEE
 Radhika studies in BIT
 Shyam studies in BCS
 This shows Relationship between Student and Program
entities (entity types to be precise) … let us give a
name to it “Studies” – obvious choice
 Relationship also has type and set, here studies is
Relationship Type,
 Each of above statement is instance of the type, and
All together form relationship set

Relationship
Set
Relationship Instance Diagram


Simple ER Diagram: Student-Program

Relationship


Relationships can also have attributes
 In our previous example, suppose we also want to have
study duration of a student in a program, then
 Let us say, we have attributes year_from, and year_to


Cardinality Constraints for
Binary Relationships
 Binary relationship, is the one in which two
entity types participate.
 Based on the number of relationships instances
in which an entity can participate, relationship
types can be one following-
 One to One (1:1)
 One to Many (1:N)
 Many to Many (M:N)
 Example:
 Customer – Bank Account as 1:1, 1:N, M:N

One to One (1:1)
 One Customer has one account and one account is
owned by one customer


One to Many (1:N)
 One Customer has many accounts, and one account is
owned by one customer


Many to Many (M:N)
 One Customer has many accounts, and one account is
owned by multiple customers jointly


Cardinality Constraints for
Binary Relationships

 Every Relationship Type has to be specified to be one
these – a constraint on relationship type

 Cardinality Constraints are also called Cardinality
Ratio (Elmasri/Navathe), though not ratio in precise
terms. Probably, because it written in ratio format
(1:N)!

 Some authors also call it Mapping Cardinality (Korth)

 Cardinality Constraint appears to be better name, as it
is an constraint on the cardinality of relationship
instances into which a entity can participate at most.

Some relationship examples?
 Identify and determine cardinality ratio?
 Student and Mentor
 Bill and Item
 Course and Professor
 Course and Text Book
 Vehicle and Owner


Participation can be
Total (mandatory) or Partial (optional)
 In our Customer-Account example-
 If customer record can exist in database without having
associated with an account – then participation of
customer is partial otherwise total.
 Can Customer exist without associating with a
account? Probably Yes.
 Can account exist without associating with a customer?
Probably No. Partial
Total
Participation
Participation


Student-Program “schema”
 Participation of Student in Studies relationship is
mandatory – student has to study in a program.
 Participation of a Program can be optional – we may
not take admission in a program.

Total
Participatio
n

Partial
Aug 14, 2008 Data Modeling Using E-R Models Participation 30

Student-Program schema

This diagram lets us ensure that we are modeling right business rule in the database
Business rules are modeled as constraints in data models
Cardinality and Participation are important constraints in Data Model
Here we see that One student can participate only once—
this means we are modeling the business rule that One Student can study in one program only
And, One Program can have many students. That is Right

We can easily note that Participation of student is Mandatory
While it is optional for Program.

Some relationship examples?
 Identify and determine cardinality ratio?
 Student and Mentor
 Bill and Item
 Course and Professor
 Course and Text Book
 Vehicle and Owner


Exercises

 Case: Simple Trading Company

 Case: My Books Database


Understand Cardinality Ratio
 Two of looking at-
 To how many entities an entity can be associated
with other side entity type

 How many times an entity participates into the
relationship


Cardinality Constraint (Ratio)
 One to One: Any entity (from any side)
is associated with at most one entity
in other side

 One to Many: one side entity can
be associated with multiple
entities from other side
1 N
 Many to Many: any entity from
any side can be associated with
multiple entities from other side

Cardinality Constraint (Ratio)
– another way of looking at
 One to One: any entity (from any side)
can participate at most once
in the relationship

 One to Many: one side entity
participates multiple times
in the relationship
1 N
 Many to Many: any entity from
any side participates multiple
times in the relationship

Structural Constraints

 Cardinality ratio and participation constraints in
together are also called structural constraints.

 Second approach of looking at cardinality
constraints evolves another way of depicting
structural constraints in ER Diagram


Another way of showing structural
constraint
 Min possibility a customer (entity) can participate in holds
relationship is 0, and Max possibility it can participate in
holds relationship is N, and same way for Account entity
type it is one and only one

(0,n) (1,1)

Another way of showing structural
constraint – Many to Many
 Min possibility an Account (entity) can participate in
holds relationship is 1, and Max possibility it can can
participate in holds relationship is N

(0,n) (1,N)

Structural constraints
 Note that this notation takes care of
participation constraints as well.

 Having min participation zero is partial
participation while, and having > 0 is total
participation


Recursive Relationship
 Entity having relationship with itself
 There can be many situations like this
 Employee supervises another employee
 One citizen is spouse of another citizen
 One course has pre-requisite of another course
 One category is parent of another category


Recursive Relationship

Employee
Employee Supervises
Supervisor
r1: e5 is supervisor of e1
e1 r1
e2 r2
r3: e1 is supervisor of e2 e3
r3
e4
e5 r4
r5: e4 is supervisor of e6 e6
e7 r5
r6

Roles: Supervisor (Green), Subordinate (Red)

What about Cardinality and participation
constraints in Supervises relationship..
 ?


Weak Entity Types
 Entity that do not have key attributes of their own are
called weak entity types
 These are attached to some regular/strong entity type,
we call that entity as identifying or owner entity type,
and we call the relationship between weak entity type
and its owner entity type, identifying relationship.
 A weak entity type always has total participation in its
identifying relationship.
 Examples:
 Experience History of an Employee
 Dependents of an Employee


Weak Entity Types
Identifying
Relationship

Owner
Entity

Weak
Entity


 In some cases, database designer may choose to
express weak entity as multi-value composite
attribute of the owner entity type

 However, this approach could only be possible
if week entity does not participate in any other
relationship than identifying relationships.


ER Creating Guidelines
 As a thumb rule Nouns and Objects are
candidate for Entities and Verbs for
relationships in a problem description.
 Below are some rules suggested in
[Entity-Relationship Modeling, A Practical how-to-guide, Yeol Song and
Kristin Froehlich, IEEE Potentials, Dec-94/Jan-95, pg#29-34]

3. Every entity type should be important in its
own right within the problem domain
 Address can not be an entity
 City, can be Yes or No


1. IF an object type (noun) has only one property
to store THEN it is an attribute of another
entity type ELSE it is an entity type-
 Suppose naukari.com wants to store skills of
job seekers, can it be attribute or entity?
 Can be Yes or No
 If we want to store more meaning to skills, For
example “C++”: which platform, which
functional Area etc., then it becomes entity,
otherwise just attribute

1. IF an object type has only one data instance THEN do
not model as an entity type – example Director,
DAIICT
2. IF a relationship needs to have a unique identifier
THEN model it as an entity type
 Consider “products are billed to customers”, appear like
relationship, but bills are numbered, and uniquely identified,
candidate for entity type.
 Similarly Pays, Orders look like relationships but are
possibly entities – depends on business context
3. IF any verb refers to nouns which are not selected as
entity types THEN do not model it as a relationship
type
 Students Fills Enrollment Form. Here neither Enrollment
form is entity nor FillsIn is a relationship


 Below are rules related to cardinality and
participation. Suppose Entity A relates to entity
B, then
2. For each A, what is the maximum number of
Bs that may be related to it?
3. IF A can exist without being associated with a
B THEN A has partial (optional) participation
ELSE A has total (mandatory) participation.


Example - Student Election scenario
 Consider
 There are some positions for which voting needs to
be done
 For each position there can be N candidates
 When voting takes place we want to make sure that
no body votes twice.


Example - Student Election scenario
extended
 How our ERD will change if we have some
requirements/constraints-
 Suppose we want to keep track of who has voted to
whom

 Suppose there is constraints that for some positions
students from certain program can only vote

 Suppose there is another constraint that for some
position students from certain program can only be
candidates

Home Exercises

 Elmasri/Navathe 4th ed

 Unsolved Exercise: 3.21, 3.22


Ternary Relationship

 Three entities are participating in a relationship
 Most case binary suffices, but ternary/n-ary comes in
some cases


An example of ternary Relationship


Enhanced Features in ER modeling
 Primarily addresses issues related to
generalization/specialization, aggregation,
which were missing in Chen’s original ER
model
 Features suggested by Elmasri/Navathe appears
to be complete covering most aspects of
generalization/specialization, aggregation


Aggregation
 Let us take an Example Company-Interview-Applicant-
Offer situation


Aggregation


Thanks


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