1. The Entity-Relationship Model2
The slides for this text are organized into chapters. This lecture covers Chapter 2, on the Entity-Relationship approach to database design.
The important issue of how to map from ER diagrams to relational tables is deferred until the relational model and the integrity constraints it supports have been introduced. ER to relational mapping, together with a discussion of the related SQL commands, is discussed in Chapter 3. 1

2. Database Design Process
Requirement collection and analysis
DB requirements and functional requirements
Conceptual DB design using a high-level model
Easier to understand and communicate with others
Logical DB design (data model mapping)
Conceptual schema is transformed from a high-level data model into implementation data model
Physical DB design
Internal data structures and file organizations for DB are specified 2

3. Overview of Database Design
Conceptual design: (ER Model is used at this stage.)
What are the entities and relationships in the enterprise?
What information about these entities and relationships should we store in the database?
What are the integrity constraints or business rules that hold?
A database `schema’ in the ER Model can be represented pictorially (ER diagrams).
An ER diagram can be mapped into a relational schema. 2

4. The Relational Model Relational Model [Properties]
Each relation (or table) in a database has a unique name
An entry at the intersection of each row and column is atomic (or single-valued);
there can be no multi-valued attributes in a relation
Each row is unique;
no two rows in a relation are identical
Each attribute (or column) within a table has a unique name

5. The Relational Model Properties Cont’d
The sequence of columns (left to right) is insignificant;
the columns of a relation can be interchanged without changing the meaning or use of the relation
The sequence of rows (top to bottom) is insignificant;
rows of a relation may be interchanged or stored in any sequence

6. The Relational Model...
The relational model of data has three major components:
Relational database objects
allows to define data structures
Relational operators
allows manipulation of stored data
Relational integrity constraints
allows to defines business rules and ensure data integrity

7. The Relational Objects
Location
Most RDBMS can have multiple
locations, all managed by the
same database engine
Accounting
Accounts Receivable
Accounts Payable
Corporate Database
Accounting
Marketing
Sales
Advertising
Marketing
Purchasing

8. The Relational Objects
Location
Database Server
Multi-user
Client Applications

9. The Relational Objects...
Database
A set of SQL objects
Database Server
Update Trigger
BEGIN
...
Client Application
Table T
UPDATE T SET
INSERT INTO T
DELETE FROM T
CALL STPROG
Insert Trigger
BEGIN
...
Delete Trigger
Stored
Procedure
Table A
BEGIN
...
BEGIN
...
Table B

10. The Relational Objects...
Database
A collection of tables and associated indexes
Index
Table
Table
Employee
Product
Table
Table
Files
Department
Customer

11. The Relational Objects...
A named, two dimensional table of data
Database
A collection of databases, tables and related objects organised in a structured fashion.
Several database vendors use schema interchangeably with database

12. Relational Objects... Data is presented to the user as tables:
Tables are comprised of rows and a fixed number of named columns.
Table
Column 1 Column 2 Column 3 Column 4
Row
Row
Row

13. Relational Objects... Data is presented to the user as tables:
Columns are attributes describing an entity. Each column must have an unique name and a data type.
Employee
Name Designation Department
Row
Row
Row
Structure of a relation (e.g. Employee)
Employee(Name, Designation, Department)

14. Relational Objects... Data is presented to the user as tables:
Rows are records that present information about a particular entity occurrence
Employee
Name Designation Department
Row
De Silva Manager Personnel
Row
Perera Secretary Personnel
Row
Dias Manager Sales

15. Relational model terminology
Row is called a ‘tuple’
Column header is called an ‘attribute’
Table is called a ‘relation’
The data type describing the type of values that can appear in each column is called a ‘domain’
Eg:-
Names : the set of names of persons
Employee_ages : value between 15 & 80 years old
The above is called ‘logical definitions of domains’.
A data type or format can also be specified for each domain.
Eg: The employee age is an integer between 15 and 80

16. Characteristics of relations
Ordering of tuples
Tuples in a realtion don’t have any particular order. How ever in a file they may be physically ordered based on a criteria, this is not there in relational model
Ordering of values within tuple
Ordering of values within a tuple are unnecessary, hence a tuple can be considered as a ‘set’.
But when relation is implemented as a file attributes may be physically ordered
Values in a tuple are atomic

17. Relational constraints
Domain constraints
specifies that the value of each attribute ‘A’ must be an atomic value. And from the specified domain
Key constraints
There is a sub set of attributes of a relational schema with the property that no two tuples should have the same combination of values for the attributes.
Any such subset of attributes is called a ‘superkey’
A ‘superkey’ can have redundant attributes. A key is a minimul superkey
If a realtion has more than one key, they are called candidate keys
One of them is chosen as the primary key

18. Relational Objects Keys
Primary Key: An attribute (or combination of attributes) that uniquely identifies each row in a relation.
Employee(Emp_No, Emp_Name, Department)
Composite Key: A primary key that consists of more than one attribute
Salary(Emp_No, Eff_Date, Amount)

19. Relational Objects Data is presented to the user as tables:
Each table has a primary key. The primary key is a column or combination of columns that uniquely identify each row of the table.
Employee
E-No E-Name D-No
179 Silva
857 Perera
342 Dias
Salary
E-No Eff-Date Amt
/1/
/7/
/6/
342 28/1/
Primary Key
Primary Key

20. Relational Objects Data is presented to the user as tables:
The cardinality of a table refers to the number of rows in the table. The degree of a table refers to the number of columns.
Salary
E-No Eff-Date Amt
/1/
/7/
/6/
342 28/1/
Salary Table
Degree = 3
Cardinality = 4

21. Entity integrity, referential integrity/foreign keys
Entity integrity constraint specifies that no primary key can be null
The referential integrity constraint is specified between two relations and is used to maintain the consistency among tuples of the two realtions
Informally what this means is that a tuple in one relation that refers to another relation must refer to an existing tuple.
To define referential integrity we use the concept of foreign keys.

22. Relational Objects === works for ==> Relationship
Foreign Key: An attribute in a relation of a database that serves as the primary key of another relation in the same database
Employee(Emp_No, Emp_Name, Department)
Department(Dept_No, Dept_Name, M_No)
=== works for ==>

23. Relational Objects Data is presented to the user as tables:
A foreign key is a set of columns in one table that serve as the primary key in another table
Employee
E-No E-Name D-No
179 Silva
857 Perera
342 Dias
Department
D-No D-Name M-No
4 Finance
7 Sales
Primary Key
Primary Key
Primary Key
Foreign Key
Recursive foreign key: A foreign key in a relation that references the primary key values of that same relation

24. Relational Objects... Department 4 Finance 857 7 Sales 179 Employee
D-No D-Name M-No
4 Finance
7 Sales
Employee
E-No E-Name D-No
179 Silva
857 Perera
342 Dias
Foreign Key
Primary Key
Primary Key
Primary Key
Primary Key
Foreign Key
Salary
E-No Eff-Date Amt
/1/
/7/
/6/
342 28/1/
Primary Key
Foreign Key
Rows in one or more tables are associated with each other solely through data values in columns (no pointers).

25. Relational Objects Index
An ordered set of pointers to the data in the table
Employee
E-Name Pointer
De Silva
Dias
Perera
Silva
E-No E-Name D-No
Silva
Perera
Dias
De Silva

26. Index: Employee Name Employee Alwis 179 Silva 7 Bandara 857 Perera 4
E-Name Pointer
Alwis
Bandara
Costa
De Silva
Dias
Opatha
Peiris
Perera
Silva
Vaas
Wickrama
Zoysa
Employee
E-No E-Name D-No
Silva
Perera
Dias
De Silva
Alwis
Costa
Zoysa
Peiris
Vaas
Bandara
Opatha
Wickrama 1

27. Search: Employee Dias Index Improves performance. Access to data
E-Name Pointer
Alwis
Bandara
Costa
De Silva
Dias
Opatha
Peiris
Perera
Silva
Vaas
Wickrama
Zoysa
Index
Improves
performance.
Access to data
is faster

28. Search: Employee Dias Index Ensures uniqueness.
A table with unique fields
in the index cannot have
two rows with the same
values in the column or
columns that form the
index key.
Search: Employee Dias
Index
Opatha
Costa
Silva
Bandara
Dias
Perera
Wickrama

29. Search: Employee Dias . De Silva . Perera . . Bandara . . . Opatha . .
. Vaas . .
. Wickrama
. Zoysa .
. Alwis . .
. Costa . .
. Dias . .
. Peiris . .
. Silva . .

30. Relational Database STORE Store 1 | Colombo Store 2 | Kandy STORE
Store Name | City
INVENTORY
Store Name | Part No | Quantity
ORDERS
Store Name | Part No | Vendor No | Order No | Quantity
PART
Part No | Description
VENDOR
Vendor No | Vendor Name
INVENTORY
Store 1 | P1 | 50
Store 1 | P3 | 20
Store 2 | P2 | 100
Store 2 | P1 | 30
ORDERS
Store 1 | P3 | 3428 | 0052 | 10
Store 2 | P2 | 3428 | 0098 | 7
Store 2 | P3 | 3428 | 0098 | 15
Store 2 | P4 | 5726 | 0099 | 1
PART
P1 | Printer
P2 | Diskette
P3 | Disk Drive
P4 | Modem
VENDOR
3428 | East West
5726 | DMS

31. ER Model Basics
Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes.
Entity Set: A collection of similar entities. E.g., all employees.
All entities in an entity set have the same set of attributes. (Until we consider ISA hierarchies, anyway!)
Each entity set has a key.
Each attribute has a domain.
The slides for this text are organized into several modules. Each lecture contains about enough material for a 1.25 hour class period. (The time estimate is very approximate--it will vary with the instructor, and lectures also differ in length; so use this as a rough guideline.) This covers Lectures 1 and 2 (of 6) in Module (5).
Module (1): Introduction (DBMS, Relational Model)
Module (2): Storage and File Organizations (Disks, Buffering, Indexes)
Module (3): Database Concepts (Relational Queries, DDL/ICs, Views and Security)
Module (4): Relational Implementation (Query Evaluation, Optimization)
Module (5): Database Design (ER Model, Normalization, Physical Design, Tuning)
Module (6): Transaction Processing (Concurrency Control, Recovery)
Module (7): Advanced Topics
Employees
ssn
name
lot 3

32. ER Model Basics Key and key attributes:
Employees
ssn
name
lot
ER Model Basics
Key and key attributes:
Key: a unique value for an entity
Key attributes: a group of one or more attributes that uniquely identify an entity in the entity set
Super key, candidate key, and primary key
Super key: a set of attributes that allows to identify and entity uniquely in the entity set
Candidate key: minimal super key
There can be many candidate keys
Primary key: a candidate key chosen by the designer
Denoted by underlining in ER attributes
The slides for this text are organized into several modules. Each lecture contains about enough material for a 1.25 hour class period. (The time estimate is very approximate--it will vary with the instructor, and lectures also differ in length; so use this as a rough guideline.) This covers Lectures 1 and 2 (of 6) in Module (5).
Module (1): Introduction (DBMS, Relational Model)
Module (2): Storage and File Organizations (Disks, Buffering, Indexes)
Module (3): Database Concepts (Relational Queries, DDL/ICs, Views and Security)
Module (4): Relational Implementation (Query Evaluation, Optimization)
Module (5): Database Design (ER Model, Normalization, Physical Design, Tuning)
Module (6): Transaction Processing (Concurrency Control, Recovery)
Module (7): Advanced Topics 3

33. ER Model Basics (Contd.)
name
ER Model Basics (Contd.)
ssn
lot
Employees
since
name
dname
super-visor
subor-dinate
ssn
lot
did
budget
Reports_To
Employees
Works_In
Departments
Relationship: Association among two or more entities. e.g., Jack works in Pharmacy department.
Relationship Set: Collection of similar relationships.
An n-ary relationship set R relates n entity sets E1 ... En; each relationship in R involves entities e1 in E1, ..., en in En
Same entity set could participate in different relationship sets, or in different “roles” in same set. 4

34. Key Constraints
since
lot
name
ssn
dname
did
budget
Manages
Consider Works_In: An employee can work in many departments; a dept can have many employees.
In contrast, each dept has at most one manager, according to the key constraint on Manages.
Employees
Departments
1-to-1
1-to Many
Many-to-1
Many-to-Many 6

35. Example ER
major
Department
offers
An ER diagram represents several assertions about the real world. What are they?
When attributes are added, more assertions are made.
How can we ensure they are correct?
A DB is judged correct if it captures ER diagram correctly.
faculty
Courses
teaches
Professor
advisor
enrollment
Students 2

36. Participation Constraints
Does every department have a manager?
If so, this is a participation constraint: the participation of Departments in Manages is said to be total (vs. partial).
Every Departments entity must appear in an instance of the Manages relationship.
since
since
name
name
dname
dname
ssn
lot
did
did
budget
budget
Employees
Manages
Departments
Works_In
since 8

37. Weak Entities
A weak entity can be identified uniquely only by considering the primary key of another (owner) entity.
Owner entity set and weak entity set must participate in a one-to-many relationship set (one owner, many weak entities).
Weak entity set must have total participation in this identifying relationship set.
name
cost
ssn
pname
lot
age
Employees
Policy
Dependents 10

38. ISA (`is a’) Hierarchies
name
ISA (`is a’) Hierarchies
ssn
lot
Employees
As in C++, or other PLs, attributes are inherited.
If we declare A ISA B, every A entity is also considered to be a B entity.
hourly_wages
hours_worked
ISA
contractid
Hourly_Emps
Contract_Emps
Overlap constraints: Can Joe be an Hourly_Emps as well as a Contract_Emps entity? (default: disallowed; A overlaps B)
Covering constraints: Does every Employees entity also have to be an Hourly_Emps or a Contract_Emps entity? (default: no; A AND B COVER C)
Reasons for using ISA:
To add descriptive attributes specific to a subclass.
To identify entities that participate in a relationship. 12

39. name
Aggregation
ssn
lot
Employees
Used when we have to model a relationship involving (entitity sets and) a relationship set.
Aggregation allows us to treat a relationship set as an entity set for purposes of participation in (other) relationships.
Monitors
until
started_on
since
dname
pid
pbudget
did
budget
Projects
Sponsors
Departments
Aggregation vs. ternary relationship:
Monitors is a distinct relationship,
with a descriptive attribute.
Also, can say that each sponsorship
is monitored by at most one employee. 2

40. Conceptual Design Using the ER Model
Design choices:
Should a concept be modeled as an entity or an attribute?
Should a concept be modeled as an entity or a relationship?
Identifying relationships: Binary or ternary? Aggregation?
Constraints in the ER Model:
A lot of data semantics can (and should) be captured.
But some constraints cannot be captured in ER diagrams. 3

41. Entity vs. Attribute
Should address be an attribute of Employees or an entity (connected to Employees by a relationship)?
Depends upon the use we want to make of address information, and the semantics of the data:
If we have several addresses per employee, address must be an entity (since attributes cannot be set-valued).
If the structure (city, street, etc.) is important, e.g., we want to retrieve employees in a given city, address must be modeled as an entity (since attribute values are atomic).

42. Entity vs. Attribute (Contd.)
from to
name
Employees
ssn
lot
Works_In4 does not allow an employee to work in a department for two or more periods.
Similar to the problem of wanting to record several addresses for an employee: We want to record several values of the descriptive attributes for each instance of this relationship. Accomplished by introducing new entity set, Duration.
dname
did
budget
Works_In4
Departments
name
dname
budget
did
ssn
lot
Employees
Works_In4
Departments
Duration
from to 5

43. Entity vs. Relationship
First ER diagram OK if a manager gets a separate discretionary budget for each dept.
What if a manager gets a discretionary budget that covers all managed depts?
Redundancy: dbudget stored for each dept managed by manager.
Misleading: Suggests dbudget associated with department-mgr combination.
since
dbudget
name
dname
ssn
lot
did
budget
Employees
Manages2
Departments
name
ssn
lot
since
dname
Employees
did
budget
Manages2
Departments
ISA
This fixes the
problem!
Managers
dbudget 6

44. Binary vs. Ternary Relationships
name
Employees
ssn
lot
pname
age
If each policy is owned by just 1 employee, and each dependent is tied to the covering policy, first diagram is inaccurate.
What are the additional constraints in the 2nd diagram?
Covers
Dependents
Bad design
Policies
policyid
cost
name
Employees
ssn
lot
pname
age
Dependents
Purchaser
Beneficiary
Better design
policyid
cost
Policies 7

45. Binary vs. Ternary Relationships (Contd.)
Previous example illustrated a case when two binary relationships were better than one ternary relationship.
An example in the other direction: a ternary relation Contracts relates entity sets Parts, Departments and Suppliers, and has descriptive attribute qty. No combination of binary relationships is an adequate substitute:
S “can-supply” P, D “needs” P, and D “deals-with” S does not imply that D has agreed to buy P from S.
How do we record qty? 9

46. Summary of Conceptual Design
Conceptual design follows requirements analysis,
Yields a high-level description of data to be stored
ER model popular for conceptual design
Constructs are expressive, close to the way people think about their applications.
Basic constructs: entities, relationships, and attributes (of entities and relationships).
Some additional constructs: weak entities, ISA hierarchies, and aggregation.
Note: There are many variations on ER model. 11

47. Summary of ER (Contd.)
Several kinds of integrity constraints can be expressed in the ER model: key constraints, participation constraints, and overlap/covering constraints for ISA hierarchies. Some foreign key constraints are also implicit in the definition of a relationship set.
Some constraints (notably, functional dependencies) cannot be expressed in the ER model.
Constraints play an important role in determining the best database design for an enterprise. 12

48. Summary of ER (Contd.)
ER design is subjective. There are often many ways to model a given scenario! Analyzing alternatives can be tricky, especially for a large enterprise. Common choices include:
Entity vs. attribute, entity vs. relationship, binary or n-ary relationship, whether or not to use ISA hierarchies, and whether or not to use aggregation.
Ensuring good database design: resulting relational schema should be analyzed and refined further. FD information and normalization techniques are especially useful. 13