Brief About Entity Relationship Model

1. DESIGNING OF
DATABASE
ENTITY-RELATIONSHIP MODEL
1

2. HOW TO DESIGN THE DATABASE?
2

3. HOW TO DESIGN THE DATABASE?
There are two approaches
 E-R Modeling (top down approach): Identifying
entity and relations
 Normalization(bottom up approach): Refinement
of database designing
3

4. ENTITY-RELATION MODEL
4

5. E-R MODEL
 The Entity-Relationship (ER) model was
originally proposed by Peter in 1976
 The ER model is a conceptual data model that
views the real world as entities and
relationships.
 A basic component of the model is the Entity-
Relationship diagram, which is used to visually
represent data objects.
5

6. 6

7. BASIC CONSTRUCTS OF E-R
MODELING
 A database can be modeled as:
a collection of entities,
relationship among entities.
 An entity is an object that exists and is distinguishable
from other objects.
Example: specific person, company, event,
plant
 Entities have attributes
Example: people have names and addresses
 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 7

8. BASIC CONSTRUCTS OF E-R
MODELING
 Entities
 Entities are the principal data object about
which information is to be collected. Entities
are usually recognizable concepts, either
concrete or abstract, such as person, places,
things, or events, which have relevance to the
database. Some specific examples of entities
are EMPLOYEES, PROJECTS, and
INVOICES. An entity is analogous to a table
in the relational model.
8

9. Relationships
A Relationship represents an association between two or more
entities. Relationships are classified in terms of degree,
connectivity, cardinality, and existence. An example of a
relationship would be:
♦ Employees are assigned to projects
♦ Projects have subtasks
♦ Departments manage one or more projects
9

10. ENTITY SETS CUSTOMER AND
LOAN
10

11. RELATIONSHIP SET BORROWER
11

12. ATTRIBUTES
Attributes describe the properties of the entity of
which they are associated. We can classify
attributes as following:
 Simple
 Composite
 Single-valued
 Multi-valued
 Derived
12

13. 13

14. 14

15. 15

16. EXAMPLE
16

17. DEGREE OF A RELATIONSHIP
The degree of a relationship is the number of
entities associated with the relationship. The n-
ary relationship is the general form for degree n.
Special cases are the binary, and ternary, where
the degree is 2, and 3, respectively.
17

18. CONNECTIVITY AND
CARDINALITY
The connectivity of a relationship describes the
mapping of associated entity instances in the
relationship. The values of connectivity are
"one" or "many". The cardinality of a
relationship is the actual number of related
occurrences for each of the two entities.
The basic types of connectivity for relations are:
 One to One (1:1)
 One to Many (1:M)
 Many to One (M:1)
 Many to Many (M:M)
18

19. 19

20. 20

21. 21

22. 22

23. DIRECTION
 The direction of a relationship indicates the originating
entity of a relationship.
 The entity from which a relationship originates is the
parent entity; the entity where the relationship
terminates is the child entity.
 The type of the relation is determined by the direction of
line connecting relationship component and the entity.
 To distinguish different types of relation, we draw either
a directed line or an undirected line between the
relationship set and the entity set.
 Directed line is used to indicate one occurrence
and undirected line is used to indicate many
occurrences in a relation.
23

24. 24

25. 25

26. 26

27. E-R NOTATION
 Entities are represented by labeled rectangles. The label
is the name of the entity. Entity names should be
singular nouns.
 Attributes are represented by Ellipses.
 A solid line connecting two entities represents
relationships. The name of the relationship is written
above the line. Relationship names should be verbs and
diamonds sign is used to represent relationship sets.
 Attributes, when included, are listed inside the entity
rectangle. Attributes, which are identifiers, are
underlined. Attribute names should be singular nouns.
 Multi-valued attributes are represented by double
ellipses.
 Directed line is used to indicate one occurrence and
undirected line is used to indicate many occurrences in a
relation.
27

28. 28

29. E-R NOTATION
29

30. EXAMPLE
30

31. 31

32. CUSTOMER-LOAN RELATIONSHIP
32

33. EXERCISE
 Consider the following
database:
 S (S#, SSNAME, STATUS,
CITY)
 P (P#, PNAME, COLOR,
WEIGHT, CITY)
 J ( J#, JNAME, CITY)
 SPJ( S#, P#, J#, QTY)
Here, S indicates information of
suppliers, P Parts, J Projects
and SPJ indicates the supplied
quantity details. 33

34. 34

35. TOTAL PARTICIPATION
 Total participation (indicated by double line): every entity in the
entity set participates in at least one relationship in the relationship
set
 E.g. participation of loan in borrower is total
 every loan must have a customer associated to it via
borrower
 Partial participation: some entities may not participate in any
relationship in the relationship set
 Example: participation of customer in borrower is partial
35

36. 36

37. 37

38. SOME MORE EXAMPLES
38

39. REPRESENTATION OF
CARDINALITY IN THE E-R
DIAGRAM
 The cardinality of a relationship is the actual number of
related occurrences for each of the two entities
 E-R diagrams also provide a way to indicate cardinality
of the relationship.
 An edge between an entity set and a relationship set can
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 of
the entity set in the relationship set.
 A maximum value of 1 indicates that the entity
participates in at most one relationship, while a
maximum value * indicates no limit.
 The label 1..* on an edge is equivalent to a double line. 39

40.  It is easy to misinterpret the 0..* on the edge between
customer and Cust_Loan, and think that the
relationship Cust_Loan is many to one from customer
to loan, this is exactly the reverse of the correct
interpretation.
Customer Loan
C_name
Address
Phone_no
City amountLoan_No
Cust_
Loan0..* 1..1
40

41.  For example,the edge between loan and
Cust_Loan has a cardinality constraint of
1..1, meaning the minimum and the
maximum cardinality are both 1.
 That is, each loan must have exactly one
associated customer. The limit 0..* on the
edge from customer to Cust_Loan indicates
that a customer can have zero or more loans.
 Thus, the relationship Cust_Loan is one to
many from customer to loan, and further the
participation of loan in Cust_Loan is total. 41

42. Customer Loan
C_name
Address
Phone_no
City amountLoan_No
Cust_
Loan
0..* 1..1
42

43.  If both edges from a binary relationship have a
maximum value of 1, the relationship is one to
one. If we had specified a cardinality limit of 1..*
on the edge between customer and Cust_Loan, we
would be saying that each customer must have at
least one loan.
43

44. SOME MORE EXAMPLES
44

45. SOME MORE EXAMPLES
45

46. SOME MORE EXAMPLES
46

47. Consider the following database:
S (S#, SSNAME, STATUS, CITY)
P (P#, PNAME, COLOR, WEIGHT, CITY)
J ( J#, JNAME, CITY)
SPJ( S#, P#, J#, QTY)
Here S indicates information of suppliers, P Parts, J Projects
and SPJ indicates the supplied quantity details. 47

48. 48

49. Car-insurance company
•It has a set of customers, each of who owns one or more cars.
•Car may have any number of customers.
•Each car has associated with it zero to any number of recorded
accidents. System should store date and location of accident.
•Car insurance company will pay damage amount for the
accidental cars to concerned driver.
49

50. 50

51. CASE STUDY OF UNIVERSITY
MANAGEMENT SYSTEM
 Consider, a university contains many
departments. Each department can offer any
number of courses. Many teachers can work
in a department. A teacher can work only in
one department. For each department there
is a Head. A teacher can be head of only one
department. Each teacher can take any
number of courses. A course can be taken by
only one instructor. A student can enroll for
any number of courses. Each course can have
any number of students. 51

52. STEPS TO DESIGN E-R DIAGRAM
 First Step to Identify the Entities
 Second Step to find relationships among
these entities
 Step 3 to identify the key attributes
 Step 4 to identify other relevant attributes
 Step 5 to draw the complete e-r diagram
52

53. FIRST STEP TO IDENTIFY THE
ENTITIES
 In order to identify the entities collect all the
noun in the requirement sheet which has
some properties and are important for the
system.
 We can identify the following nouns:
 University, Department, Course, Teacher,
Student.
 Here, the database is of only one university.
If an entity has a single instance then that
entity is ignored. Thus, the final entities are:
 DEPARTMENT
 COURSE
 TEACHER
 STUDENT
53

54. SECOND STEP TO FIND
RELATIONSHIPS AMONG
THESE ENTITIES
 Each department can offer any number of courses and
we can assume that each course belongs to only one
department. Then the connectivity of the relation ship
among DEPARTMENT and COURSE is One to Many. If
a course can run in more than one department then it is
Many to Many.
 Many teachers can work in a department and a teacher
can work only in one department. Thus the connectivity
among DEPARTMENT and TEACHER is one to many.
 For each department there is a Head and a teacher can
be head of only one department. Hence, the connectivity
is one to one.
54

55.  Each teacher can take any number of courses and
a course can be taken by only one instructor.
Thus, the connectivity between TEACHER and
COURSE is one to many.
 A student can enroll for any number of courses
and each course can have any number of
students. Thus, the connectivity between
STUDENT and COURSE is many to many.
55

56. STEP 3 TO IDENTIFY THE KEY
ATTRIBUTES
 Following are the primary key attributes for
each entity set:
 Dno (Department number) is the key
attribute for the Entity DEPARTMENT.
 C_code (Course number) is the key attribute
for COURSE Entity.
 Roll_no (Roll number) is the key attribute for
STUDENT Entity.
 T_code (Teacher code) is the key attribute for
TEACHER Entity.
56

57. STEP 4 TO IDENTIFY OTHER
RELEVANT ATTRIBUTES
 Following are the other relevant attributes for
each entity set:
 DEPARTMENT entity will have other relevant
attributes as dname, loc.
 For COURSE entity, c_name, credits.
 For TEACHER entity, name, mob_no
 For STUDENT entity, name, address
57

58. STEP 5 TO DRAW THE
COMPLETE E-R DIAGRAM
DEPARTMENT
TEACHER STUDENT
COURSEoffers
has enroll
teach
heads
dno dname loc c_code credits
roll_no addressnamet_code mob_noname
c_name
58

59. Strong and Weak Entity Sets
The entity set which does not has sufficient attributes to form a primary key is
called as weak entity set.
An entity set that has a primary key is called as Strong entity set. Consider an
entity set Payment which has three attributes: payment_number, payment_date
and payment_amount.
Although each payment entity is distinct but payment for different loans may
share the same payment number. Thus, this entity set does not have a primary
key and it is a weak entity set.
Each weak set must be a part of one-to-many relationship set.
59

60. A member of a strong entity set is called dominant entity and member
of weak entity set is called as subordinate entity.
A weak entity set does not have a primary key but we need a means of
distinguishing among all those entries in the entity set that depend on
one particular strong entity set.
The discriminator of a weak entity set is a set of attributes that allows
this distinction to be made.
For example, payment_number is acts as discriminator for payment
entity set. It is also called as the Partial key of the entity set.
60

61. The primary key of a weak entity set is formed by the primary
key of the strong entity set on which the weak entity set is
existence dependent, plus the weak entity set’s discriminator. In
the above example {loan_number, payment_number} acts as
primary key for payment entity set.
61

62. The relationship between weak entity and strong entity set is
called as Identifying Relationship. In example, loan-payment is
the identifying relationship for payment entity. A weak entity
set is represented by doubly outlined box and corresponding
identifying relation by a doubly outlined diamond
62

63. 63

64. 64

65. CASE STUDY
Represent each of the following requirements with an ER diagram:
 A regional council requires the design of a database system that
can provide information on all schools in the region. The
requirements collection and analysis phase of the database design
process has provided the following data requirements for the
schools database system.
(a) Every school has many pupils and many teachers. Each pupil is
assigned to one school and each teacher works for one school only.
(b) Each teacher teaches more than one subject but a subject may
be taught by more than one teacher. The database should store the
number of hours a teacher spent teaching a subject. Data held on
each teacher includes his/her national Insurance Number (NIN),
name (first and last), sex, and qualifications. The data held on
each subject includes subject title and type.
(c) Each pupil can study more than one subject and a subject may
be studied by more than one pupil. Data held on each pupil
includes the pupil's code, name (first and last), sex, and date of
birth.
(d) Each school is managed by one of its teachers. The database
should keep track of the date he/she started managing the school.
Data stored on each school includes the school's code, name,
address (town, street, and post code) and phone.
65

66. DESIGN ISSUES
USE OF ENTITY SETS VERSUS
ATTRIBUTES
Consider the entity set employee with attributes
employee-name and telephone-number.
 It can easily be argued that a telephone is an
entity in its own right with attributes telephone-
number and location (the office where the
telephone is located). If we take this point of
view, we must redefine the employee entity set
as:
• The employee entity set with attribute
employee-name
• The telephone entity set with attributes
telephone-number and location
• The relationship set emp-telephone, which
denotes the association between employees and
the telephones that they have
66

67. WHAT, THEN, IS THE MAIN
DIFFERENCE BETWEEN THESE TWO
DEFINITIONS OF AN EMPLOYEE?
 Treating a telephone as an attribute
telephone-number implies that employees
have precisely one telephone number each.
 Treating a telephone as an entity telephone
permits employees to have several telephone
numbers (including zero) associated with
them
 However, we could instead easily define
telephone-number as a multivalued attribute
to allow multiple telephones per employee.
67

68.  The main difference then is that treating a
telephone as an entity better models a
situation where one may want to keep extra
information about a telephone, such asits
location, or its type (mobile, video phone, or
plain old telephone), or who all share the
telephone.
 Thus, treating telephone as an entity is
more general than treating it as an
attribute and is appropriate when the
generality may be useful. 68

69. 69

70.  In contrast, it would not be appropriate to treat
the attribute employee-name as an entity;
 it is difficult to argue that employee-name is an
entity in its own right (in contrast to the
telephone).
 Thus, it is appropriate to have employee-name as
an attribute of the employee entity set.
70

71.  Two natural questions thus arise: What
constitutes an attribute, and what
constitutes an entity set?
 Unfortunately, there are no simple answers.
 The distinctions mainly depend on the structure
of the real-world enterprise being modeled, and
on the semantics associated with the attribute in
question.
71

72. USE OF ENTITY SETS VERSUS
RELATIONSHIP SETS
 It is not always clear whether an object is
best expressed by an entity set or a
relationship set.
 We assumed that a bank loan is modeled as
an entity.
 An alternative is to model a loan not as an
entity, but rather as a relationship betwee
customers and branches, with loan-number
and amount as descriptive attributes.
 Each loan is represented by a relationship
between a customer and a branch. 72

73.  If every loan is held by exactly one customer and
is associated with exactly one branch, we may
find satisfactory the design where a loan is
represented as a relationship.
73

74. Generalization: A bottom-up design process
A generalization hierarchy is a form of abstraction that specifies
that two or more entities that share common attributes can be
generalized into a higher-level entity type called a supertype or
generic entity.
The lower level of entities becomes the subtype, or categories, to
the super type. Subtypes are dependent entities.
Generalization is used to emphasize the similarities among
lower-level entity sets and to hide differences.
It makes ER diagram simpler because shared attributes are not
repeated. Generalization is denoted through a triangle
component labeled ‘IS A”, 74

75. 75

76. Specialization: Top-down design process
Specialization is the process of taking subsets of a higher-level
entity set to form lower level entity sets. It is a process of
defining a set of subclasses of an entity type, which is called as
superclas of the specialization. The process of defining subclass
is based on the basis of some distinguish characteristics of the
entities in the super class.
For example, specialization of the Employee entity type may
yield the set of subclasses namely Salaried_Employee and
Hourly_Employee on the method of pay
76

77. 77

78. 78

79. Difference between Specialization and Generalization
Specialization is the process of taking subsets of a higher-level
entity set to form lower level entity sets. Specialization
emphasizes differences among entities within the set by creating
distinct lower-level entity sets. These lower-level entity sets may
have attributes, or may participate in relationships, that do not
apply to all entities in the higher-level entity set.
Generalization proceeds from the recognition that a number of
entities set share some common features, which are described by
the same, attributes and participate in the same relationship sets.
Generalization is used to emphasize the similarities among
lower-level entity sets and to hide differences.
79

80. DESIGN CONSTRAINTS ON A
SPECIALIZATION/GENERALIZAT
ION
 Constraint on which entities can be members of a given
lower-level entity set.
 condition-defined OR Attribute Defined
 Example: all customers over 65 years are members of
senior-citizen entity set; senior-citizen ISA person.
 All account entities are evaluated on the defining
account-type attribute. Only those entities that
satisfy the condition account-type = “savings account”
are allowed to belong to the lower-level entity set
person. All entities that satisfy the condition account-
type = “checking account” are included in checking
account. Since all the lower-level entities are
evaluated on the basis of the same attribute (in this
case, on account-type), this type of generalization is
said to be attribute-defined.
80

81. user-defined
User-defined lower-level entity sets are not constrained by
a membership condition; rather, the database user
assigns entities to a given entity set.
For instance, let us assume that, after 3 months of
employment, bank employees are assigned to one of four
work teams.
We therefore represent the teams as four lower-level
entity sets of the higher-level employee entity set.
A given employee is not assigned to a specific team entity
automatically on the basis of an explicit defining
condition.
Instead, the user in charge of this decision makes the
team assignment on an individual basis. The
assignment is implemented by an operation that adds
an entity to an entity set.
81

82.  Constraint on whether or not entities may belong to
more than one lower-level entity set within a single
generalization.
 Disjoint
 A disjointness constraint requires that an entity
belong to no more than one lower-level entity set. In
our example, an account entity can satisfy only one
condition for the account-type attribute; an entity can
be either a savings account or a checking account, but
cannot be both.
82

83. Overlapping
an entity can belong to more than one lower-level
entity set
83

84.  Overlapping. In overlapping generalizations, the same
entity may belong to more than one lower-level entity
set within a single generalization.
 For an illustration, consider the employee work team
example, and assume that certain managers participate
in more than one work team.
 A given employee may therefore appear in more than
one of the team entity sets that are lower-level entity
sets of employee. Thus, the generalization is
overlapping.
84

85.  As another example, suppose generalization
applied to entity sets customer and employee
leads to a higher-level entity set person. The
generalization is overlapping if an employee can
also be a customer.
85

86. DESIGN CONSTRAINTS ON A
SPECIALIZATION/GENERALIZAT
ION (CONT.)
 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
86

87.  Partial generalization is the default.
 We can specify total generalization in an E-R diagram by
using a double line to connect the box representing the
higher-level entity set to the triangle symbol.
 (This notation is similar to the notation for total
participation in a relationship.)
 The account generalization is total: All account entities
must be either a savings account or a checking account
87

88.  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.
 When the generalization is partial, a higher-level
entity is not constrained to appear in a lower-
level entity set.
 The work team entity sets illustrate a partial
specialization.
88

89.  Since employees are assigned to a team only
after 3 months on the job, some employee entities
may not be members of any of the lower-level
team entity sets. We may characterize the team
entity sets more fully as a partial, overlapping
specialization of employee.
89

90.  The generalization of checking-account and savings-
account into account is a total, disjoint generalization.
The completeness and disjointness constraints, however,
do not depend on each other.
Constraint patterns may also be partial-disjoint and total-
overlapping.
We can see that certain insertion and deletion
requirements follow from the constraints that apply to a
given generalization or specialization.
90

91. For instance, when a total completeness constraint
is in place, an entity inserted into a higher-level
entity set must also be inserted into at least one
of the lower-level entity sets.
With a condition-defined constraint, all higher-
level entities that satisfy the condition must be
inserted into that lower-level entity set.
Finally, an entity that is deleted from a higher-
level entity set also is deleted from all the
associated lower-level entity sets to which it
belongs. 91

92. Aggregation
One limitation of the E-R model is that it cannot express
relationships among relationships.
The best way to model a situation like this is by the use of
aggregation. Thus, the relationship set work_on relating the
entity sets Employee, Branch and Job is a higher-level entity set.
Such an entity set is treated in the same manner, as is any other
entity set. We can then create a binary relationship Manages
between work_on and Manager to represent who manages what
tasks.
92

93. 93

94. 94

95. Suppose a customer loan pair may have a bank emp Who is a
loan officer for that particular pair.
CUST LOANBORROWER
LOAN-
OFFICER
EMP
95

96. The best way to model the situation is to use aggregation.
Aggregation is asn abstraction through which relationship are treated
as higher level entities
CUST LOANBORROWER
LOAN-OFFICER
EMP 96

97. E-R DIAGRAM OF
BAKING SYSTEM
97

98. CASE STUDY: DATABASE
DESIGN FOR BANKING
ENTERPRISE
 Here are the major characteristics of the banking
enterprise.
 • The bank is organized into branches. Each
branch is located in a particular city and is
identified by a unique name. The bank monitors
the assets of each branch.
98

99. 99

100. 100