1. The Entity-Relationship Model
Chapter 2
The slides for this text are organized into chapters. This lecture covers Chapter 2.
Chapter 1: Introduction to Database Systems
Chapter 2: The Entity-Relationship Model
Chapter 3: The Relational Model
Chapter 4 (Part A): Relational Algebra
Chapter 4 (Part B): Relational Calculus
Chapter 5: SQL: Queries, Programming, Triggers
Chapter 6: Query-by-Example (QBE)
Chapter 7: Storing Data: Disks and Files
Chapter 8: File Organizations and Indexing
Chapter 9: Tree-Structured Indexing
Chapter 10: Hash-Based Indexing
Chapter 11: External Sorting
Chapter 12 (Part A): Evaluation of Relational Operators
Chapter 12 (Part B): Evaluation of Relational Operators: Other Techniques
Chapter 13: Introduction to Query Optimization
Chapter 14: A Typical Relational Optimizer
Chapter 15: Schema Refinement and Normal Forms
Chapter 16 (Part A): Physical Database Design
Chapter 16 (Part B): Database Tuning
Chapter 17: Security
Chapter 18: Transaction Management Overview
Chapter 19: Concurrency Control
Chapter 20: Crash Recovery
Chapter 21: Parallel and Distributed Databases
Chapter 22: Internet Databases
Chapter 23: Decision Support
Chapter 24: Data Mining
Chapter 25: Object-Database Systems
Chapter 26: Spatial Data Management
Chapter 27: Deductive Databases
Chapter 28: Additional Topics 1

2. Objectives Overview of database design
Requirement analysis, conceptual design, logical design, normalization, and physical design
The entity-relationship (ER) model
Basic constructs:
Entities
Relationships
Attributes (of entities and relationships)
Advanced constructs:
Constraints
Weak entities
ISA hierarchies
Aggregation
Conceptual design using the ER model 2

3. Overview of Database Design
Requirements analysis: Find out what users want to do with the database
What data should be stored in the database?
What applications should be built on top of it?
What operations have critical performance requirements?
Conceptual design: Use the output of RA to develop a high-level description of the data to be stored, along with their constraints. Output of CD usually is an ER-diagram.
What are entities and relationships of the domain?
What are the integrity constraints (= business rules) that hold?
Logical design: Choose a DBMS and map the conceptual schema (ER-diagram) into the data model of the DBMS. Output of this step is the logical schema of the data. 2

4. Overview of Database Design (Cont’d)
Schema refinement: Analyze the logical schema, identify potential problems in it, and fix these by refining the logical schema using known normal forms.
Physical design: Consider expected workloads that the database will support to further refine the design in order to meet desired performance criteria. Output here is the physical schema.
Application and security design: Consider aspects of the design that go beyond the database itself. Complete design methodologies such as UML are useful here:
What are the users and processes involved in the database application?
What are the roles of the involved users?
What parts of the database are accessible to each role and what must not? 2

5. Conceptual 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?
Which integrity constraints (business rules) hold?
How to represent a database `schema’ in the ER model ? pictorially (as ER diagrams) !
How to map an ER diagram into a relational schema? 2

6. Employees
ssn
name
lot
ER Model Basics
Entity: Real-world object distinguishable from other objects. E.g., one employee.
Attribute: Construct describing an entity. E.g., the name of an employee.
Entity Set: A collection of similar entities. E.g., all employees.
All entities in an entity set have the same set of attributes. (Exception: ISA hierarchies)
Each entity set has a key, i.e. a minimal set of attributes whose values uniquely identify an entity in the entity set. Underlined !
Each attribute has a domain, i.e. a set of possible values
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
Entity set ~ rectangle; attribute ~ oval. 3

7. ER Model Basics (Cont’d.)
since
name
dname
ssn
lot
did
budget
Employees
Works_In
Departments
Relationship: Association among 2 or more entities. E.g., John works in the 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 of E1, ..., en of En.
Relationship Set ~ diamond. 4

8. ER Model Basics (Cont’d.)
name
ER Model Basics (Cont’d.)
ssn
lot
Employees
since
name
dname
super-visor
subor-dinate
ssn
lot
did
budget
Reports_To
Employees
Works_In
Departments
Descriptive attributes: Attributes of relationships.
A relationship set may relate non-distinct entity sets:
Related entities play different roles; e.g., emp1 reports to a managing employee emp2. Role indicators emphasize this difference in roles.
Unique names for attributes of the relationship set are obtained by concatenating the indicator with the attributes of the entity set. 4

9. Key Constraints
since
lot
name
ssn
dname
did
budget
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; this is an example of a key constraint on Manages, also called 1-to-many relationship set. (See arrow in figure)
Manages
Employees
Departments
1-to-1
1-to Many
Many-to-1
Many-to-Many 6

10. 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 did value in Departments table must appear in a row of the Manages table with a non-null ssn value!)
Below: thick (thin) lines mean total (partial) participation.
since
since
name
name
dname
dname
ssn
lot
did
did
budget
budget
Employees
Manages
Departments
Works_In
since 8

11. Weak Entities
A weak entity can be identified uniquely only by considering the primary key of another (identifying 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.
Example below: employees holding insurance policies for their dependents. The weak entity set and its identifying relationship are indicated by drawing them with dark lines.
A weak entity set has a partial key (indicated with dashed line).
name
cost
ssn
pname
lot
age
Employees
Policy
Dependents 10

12. ISA (`is a’) Hierarchies
name
ISA (`is a’) Hierarchies
ssn
lot
Employees
As in C++, or other OO PLs, attributes are inherited.
If we declare A ISA B, then every A entity is also a B entity.
hourly_wages
hours_worked
ISA
contractid
Hourly_Emps
Contract_Emps
Overlap constraints: Are two classes allowed to overlap? E.g., can Joe be both an Hourly_Emps and a Contract_Emps entity? (Allowed/disallowed)
Covering constraints: Do entities of subclasses contain all those in the superclass? E.g., does every Employees entity also have to be an Hourly_Emps or a Contract_Emps entity? (Yes/no)
Reasons for using ISA (by specialization/generalization):
To add descriptive attributes specific to a subclass.
To identify entities that participate in a relationship (See slide 17). 12

13. name
Aggregation
ssn
lot
Employees
Used to model a relationship between entity sets and relationship sets.
Aggregation: Allows to treat a relationship set as an entity set for purposes of participation in (other) relationships.
Notation: dashed box indicating that a relationship set participates in another relationship set.
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

14. 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

15. 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).

16. Entity vs. Attribute (Contd.)
from to
name
Employees
ssn
lot
dname
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.
Accomplish this by introducing new entity set Duration.
did
budget
Works_In4
Departments
name
dname
budget
did
ssn
lot
Employees
Works_In4
Departments
Duration
from to 5

17. Entity vs. Relationship
Should an attribute be descriptive or not?
First ER diagram depicts the situation where 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

18. Binary vs. Ternary Relationships
name
Employees
ssn
lot
pname
age
Covers
Dependents
Bad design for the additional requirements
Policies
policyid
cost
This ER diagram expresses the following requirements :
An employee can own several policies.
Each policy can be owned by several employees.
Each dependent can be covered by several policies.
The ER diagram doesn’t express the following requirements:
A policy cannot be covered jointly by 2 or more employees.
Every policy must be covered by some employee.
Dependents is a weak entity set with partial key “pname” and identified by the primary key of Policies.
Which constraints would capture the additional requirements? 7

19. Binary vs. Ternary Relationships (Cont’d)
Solution: instead of Covers, introduce two binary relationships, Purchaser and Beneficiary.
Then add the following constraints:
Key constraint on Policies with respect to Purchaser
Total participation constraint on Policies with respect to Purchaser
Appropriate constraints on the weak entity set Dependents
What if we only add a key constraint on Policies wrt to Covers?
name
Employees
ssn
lot
pname
age
Dependents
Purchaser
Beneficiary
Better design
policyid
cost
Policies 7

20. 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? Impossible to represent it cleanly with these binary relations. 9

21. 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.
It is a semantic model !
Basic constructs are: entities, relationships, and attributes (of entities and relationships).
Some additional constructs are: weak entities, ISA hierarchies, and aggregation.
There are many variations of ER model in the literature. 11

22. 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

23. 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. 13