This presentation gives you a brief introduction about ER-Model, Entities, Attributes and other Database concepts.

1. ER Diagrams

2. To be discussed:-
 Entity
 Attribute
 Types of Attribute
 Relationship
 ER Diagram Representation
 Generalization
 Specialization
 Inheritance
 Codd’s 12 Rule
 Relational Data Model
 ER Model to Relational Model

3. Entity
 An entity can be a real-world object, either
animate or inanimate, that can be easily
identifiable. Example : School Database
 Teachers
 Students
 Classes
 Courses offered
Entity set
 An entity set is a collection of similar types of
entities. An entity set may contain entities
with attribute sharing similar values.

4. Attributes
Entities are represented by means of their properties,
called attributes. All attributes have
values. Example : Student :- Name, Class, Age
Types of Attributes
 Simple attribute − Simple attributes are atomic
values, which cannot be divided further. For
example, a student's phone number is an atomic
value of 10 digits.
 Composite attribute − example, a student's
complete name may have first_name and
last_name.

5. Types of Attributes Continue…..
 Derived attribute − example, average_salary in a
department should not be saved directly in the database,
instead it can be derived.
 For another example, age can be derived from
data_of_birth.
 Single-value attribute − Single-value attributes contain
single value. example −: Social_Security_Number.
 Multi-value attribute − Multi-value attributes may contain
more than one values.
 Example: a person can have more than one phone
number, email_address, etc.

6. Relationship
 The association among entities is called
relationship.
 Ex: Employee works_at department
 Ex: Student enrolls in a course
 Relationship Set
A set of relationships of similar type is called a
relationship set. Like entities, a relationship too
can have attributes. These attributes are
called descriptive attributes.

7. Mapping cardinalities
 Cardinality defines the number of entities in
one entity set, which can be associated with
the number of entities of other set via
relationship set.
 One to one
One to many
Many to one
Many to many

8. One to One
Entity set A Entity
set B

9. One to many
Entity set A Entity
set B

10. Many to one
Entity set A Entity
set B

11. Many to many
Entity set A Entity
set B

12. ER Diagram Representation
 Entity
 Attributes
Teacher Student Classes
Studen
t
Nam
e
Roll
Birth_Dat
e

13. ER Diagram Representation Continue…
 Composite
Studen
t
Nam
e
Roll
Birth_Dat
e
Last
First

14. ER Diagram Representation Continue…
 Multivalued
Studen
t
Nam
e
Roll
Birth_Dat
e
Last
First
Phone_No

15. ER Diagram Representation Continue…
 Derived
Studen
t
Nam
e
Roll
Birth_Dat
e
Last
First
Phone_No
Age

16. ER Diagram Representation Continue…
 Relationship
 Binary Relationship and Cardinality
When two entities participate in a relationship then it
is called Binary Relationship.
Cardinality is the number of instance of an entity from
a relation that can be associated with the relation.

17. ER Diagram Representation Continue…
 One to One (1:1)
1 1
Example : Person – Passport
Student – Roll-No
Relationshi
p EntityEntity

18. ER Diagram Representation Continue…
 One to Many (1:N)
1 N
Example : Mother- Children
Student - Address
Relationshi
p EntityEntity

19. ER Diagram Representation Continue…
 Many to One (N:1)
N 1
Example : Students - Teacher
Relationshi
p EntityEntity

20. ER Diagram Representation Continue…
 Many to One (M:N)
M N
Example : - Books - Authors
Relationshi
p EntityEntity

21. ER Diagram Representation Continue…
 Participation Constraint
Total participation Partial participation
Relationshi
p EntityEntity

22.  The ER Model has the power of expressing
database entities in a conceptual hierarchical
manner. As the hierarchy goes up, it generalizes
the view of entities, and as we go deep in the
hierarchy, it gives us the detail of every entity
included.
Going up in this structure is called Generalization
Reverse is called Specialization
Generalization and Specialization

23. Generalization
Pigeon
Birds
Peacoc
kSparrow

24. Specialization
Person
Teache
r
Student
IS A

25. Inheritance
Person
Nam
e
Gende
r
Birth_Dat
e
Teache
r
Student
EMP_IDRoll
IS A

26. Relational Data Model
 Relational data model is the primary data model,
which is used widely around the world for data
storage and processing.
 Concepts
 Touple
 Relation Instance
 Relation Schema
 Relation key
 Attribute domain

27. Key Constraints
 Key constraints
Primary Key
Foreign Key
Candidate Key
Alternate Key

28. Key Constraints ….
 Key constraints force that −
 in a relation with a key attribute, no two tuples can
have identical values for key attributes.
 a key attribute can not have NULL values.
 Domain constraints
 Every attribute is bound to have a specific range of values.
 Example : Age can not be negative
 Referential integrity constraints

29. Key Constraints ….
 Referential integrity constraints
Every relation has some conditions that must hold for
it to be a valid relation. These conditions are
called Relational Integrity Constraints.
I If a relation refers to a key attribute of a different or
same relation, then that key element must exist.

30. ER model to Relational Model
 ER model
 Entity
 Attributes
 Mapping Entity
 Create table for each entity.
 Entity's attributes should become fields of tables with their
respective data types.
 Declare primary key.

31. ER model to Relational Model
 Mapping Relationship
 Create table for a relationship.
 Add the primary keys of all participating Entities as
fields of table with their respective data types.
 If relationship has any attribute, add each attribute
as field of table.
 Declare a primary key composing all the primary
keys of participating entities.
 Declare all foreign key constraints.

32. ER model to Relational Model
 Mapping Weak entity set
 Create table for weak entity set.
 Add all its attributes to table as field.
 Add the primary key of identifying entity set.
 Declare all foreign key constraints.