Chapter 1:

1
Introduction

D. NAGA JYOTHI CSE DEPT.

Outline
2
 The Need for Databases
 Data Models
 Relational Databases
 Database Design
 Storage Manager
 Query Processing
 Transaction Manager

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 Database Management System (DBMS)
3
 DBMS contains information about a particular enterprise
 Collection of interrelated data
 Set of programs to access the data
 An environment that is both convenient and efficient to use
 Database Applications:
 Banking: transactions
 Airlines: reservations, schedules
 Universities: registration, grades
 Sales: customers, products, purchases
 Online retailers: order tracking, customized recommendations
 Manufacturing: production, inventory, orders, supply chain
 Human resources: employee records, salaries, tax deductions
 Databases can be very large.
 Databases touch all aspects of our lives
D. NAGA JYOTHI CSE DEPT.
 University Database Example
4
 Application program examples
 Add new students, instructors, and courses
 Register students for courses, and generate class rosters
 Assign grades to students, compute grade point averages
(GPA) and generate transcripts
 In the early days, database applications were built
directly on top of file systems

D. NAGA JYOTHI CSE DEPT.

 Drawbacks of using file systems to store data
5
 Data redundancy and inconsistency
 Multiple file formats, duplication of information in different files
 Difficulty in accessing data
 Need to write a new program to carry out each new task
 Data isolation
 Multiple files and formats
 Integrity problems
 Integrity constraints (e.g., account balance > 0) become “buried”
in program code rather than being stated explicitly
 Hard to add new constraints or change existing ones

D. NAGA JYOTHI CSE DEPT.

 Drawbacks of using file systems to store data (Cont.)

6
 Atomicity of updates
 Failures may leave database in an inconsistent state with partial
updates carried out
 Example: Transfer of funds from one account to another should
either complete or not happen at all
 Concurrent access by multiple users
 Concurrent access needed for performance
 Uncontrolled concurrent accesses can lead to inconsistencies
 Example: Two people reading a balance (say 100) and updating it by
withdrawing money (say 50 each) at the same time

 Security problems
 Hard to provide user access to some, but not all, data

Database systems offer solutions to all the above problems

D. NAGA JYOTHI CSE DEPT.

 Levels of Abstraction
7
 Physical level: describes how a record (e.g., instructor) is stored.
 Logical level: describes data stored in database, and the
relationships among the data.
type instructor = record
ID : string;
name : string;
dept_name : string;
salary : integer;
end;

 View level: application programs hide details of data types.

Views can also hide information (such as an employee’s salary)
for security purposes.

D. NAGA JYOTHI CSE DEPT.

 View of Data
8
An architecture for a database system

D. NAGA JYOTHI CSE DEPT.

 Instances and Schemas
9
 Similar to types and variables in programming languages
 Logical Schema – the overall logical structure of the database
 Example: The database consists of information about a set of customers
and accounts in a bank and the relationship between them
 Analogous to type information of a variable in a program

 Physical schema– the overall physical structure of the database

 Instance – the actual content of the database at a particular point
in time
 Analogous to the value of a variable
 Physical Data Independence – the ability to modify the physical
schema without changing the logical schema
 Applications depend on the logical schema
 In general, the interfaces between the various levels and components
should be well defined so that changes in some parts do not seriously
influence others.

D. NAGA JYOTHI CSE DEPT.

 Data Models
10
 A collection of tools for describing
 Data
 Data relationships
 Data semantics
 Data constraints
 Relational model
 Entity-Relationship data model (mainly for database design)
 Object-based data models (Object-oriented and Object-
relational)
 Semistructured data model (XML)
 Other older models:
 Network model
 Hierarchical model

D. NAGA JYOTHI CSE DEPT.

11A hierarchical database is a data model in which data is stored in
the form of records and organized into a tree-like structure, or
parent-child structure, in which one parent node can have many
child nodes connected through links.

The network model was created to represent complex data

relationships more effectively when compared to hierarchical
models, to improve database performance and standards. It has
entities which are organized in a graphical representation and
some entities are accessed through several paths. A User perceives
the network model as a collection of records in 1:M relationships.

Relational Model (RM) represents the database as a collection of

relations. A relation is nothing but a table of values. Every row in the
table represents a collection of related data values. These rows in
the table denote a real-world entity or relationship.

An entity–relationship model (or ER model) describes interrelated

things of interest inCSE
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specific domain of knowledge. A basic ER
model is composed of entity types (which classify the things of
12An object - based data model is a data model based on
object-oriented programming, associating methods
(procedures) with objects that can benefit from class hierarchies.
Thus, “objects” are levels of abstraction that include attributes
and behavior.

An Object relational model is a combination of a Object oriented

database model and a Relational database model. So, it
supports objects, classes, inheritance etc. just like Object
Oriented models and has support for data types, tabular
structures etc. like Relational data model.

The semi-structured model is a database model where there is no

separation between the data and the schema, and the amount
of structure used depends on the purpose. ... It provides a
flexible format for data exchange between different types of
databases.

D. NAGA JYOTHI CSE DEPT.

 Relational Model
13  All the data is stored in various tables.
 Example of tabular data in the relational model
Columns

Rows

D. NAGA JYOTHI CSE DEPT.

 A Sample Relational Database
14

D. NAGA JYOTHI CSE DEPT.

 Data Definition Language (DDL)
15
 Specification notation for defining the database schema
Example:create table instructor (
ID char(5),
name varchar(20),
dept_name varchar(20),
salary numeric(8,2))
 DDL compiler generates a set of table templates stored in a data
dictionary
 Data dictionary contains metadata (i.e., data about data)
 Database schema
 Integrity constraints
 Primary key (ID uniquely identifies instructors)
 Authorization
 Who can access what

D. NAGA JYOTHI CSE DEPT.

 Data Manipulation Language (DML)
16
 Language for accessing and manipulating the data
organized by the appropriate data model
 DML also known as query language
 Two classes of languages
 Pure – used for proving properties about computational
power and for optimization
 Relational Algebra
 Tuple relational calculus
 Domain relational calculus

 Commercial – used in commercial systems

 SQL is the most widely used commercial language

D. NAGA JYOTHI CSE DEPT.

 SQL
17
 The most widely used commercial language
 SQL is NOT a Turing machine equivalent language
 To be able to compute complex functions SQL is usually
embedded in some higher-level language
 Application programs generally access databases through
one of
 Language extensions to allow embedded SQL
 Application program interface (e.g., ODBC/JDBC) which allow
SQL queries to be sent to a database

D. NAGA JYOTHI CSE DEPT.

 Database Design
18
The process of designing the general structure of the database:

 Logical Design – Deciding on the database schema.

Database design requires that we find a “good”
collection of relation schemas.
 Business decision – What attributes should we record in
the database?
 Computer Science decision – What relation schemas
should we have and how should the attributes be
distributed among the various relation schemas?
 Physical Design – Deciding on the physical layout of
the database

D. NAGA JYOTHI CSE DEPT.

 Database Design (Cont.)
19
 Is there any problem with this relation?

D. NAGA JYOTHI CSE DEPT.

 Design Approaches
20
 Need to come up with a methodology to ensure that each
of the relations in the database is “good”
 Two ways of doing so:
 Entity Relationship Model (Chapter 7)
 Models an enterprise as a collection of entities and relationships
 Represented diagrammatically by an entity-relationship diagram:

 Normalization Theory (Chapter 8)

 Formalize what designs are bad, and test for them

D. NAGA JYOTHI CSE DEPT.

 Object-Relational Data Models
21
 Relational model: flat, “atomic” values
 Object Relational Data Models
 Extend the relational data model by including object orientation
and constructs to deal with added data types.
 Allow attributes of tuples to have complex types, including non-
atomic values such as nested relations.
 Preserve relational foundations, in particular the declarative access
to data, while extending modeling power.
 Provide upward compatibility with existing relational languages.

D. NAGA JYOTHI CSE DEPT.

 XML: Extensible Markup Language
22
 Defined by the WWW Consortium (W3C)
 Originally intended as a document markup language not a
database language
 The ability to specify new tags, and to create nested tag
structures made XML a great way to exchange data, not just
documents
 XML has become the basis for all new generation data
interchange formats.
 A wide variety of tools is available for parsing, browsing and
querying XML documents/data

D. NAGA JYOTHI CSE DEPT.

 Database Engine
23
 Storage manager
 Query processing
 Transaction manager

D. NAGA JYOTHI CSE DEPT.

 Storage Management
24
 Storage manager is a program module that provides the
interface between the low-level data stored in the database
and the application programs and queries submitted to the
system.
 The storage manager is responsible to the following tasks:
 Interaction with the OS file manager
 Efficient storing, retrieving and updating of data
 Issues:
 Storage access
 File organization
 Indexing and hashing

D. NAGA JYOTHI CSE DEPT.

 Query Processing
25
1. Parsing and translation
2. Optimization
3. Evaluation

D. NAGA JYOTHI CSE DEPT.

 Query Processing (Cont.)
26
 Alternative ways of evaluating a given query
 Equivalent expressions
 Different algorithms for each operation
 Cost difference between a good and a bad way of
evaluating a query can be enormous
 Need to estimate the cost of operations
 Depends critically on statistical information about relations
which the database must maintain
 Need to estimate statistics for intermediate results to compute
cost of complex expressions

D. NAGA JYOTHI CSE DEPT.

 Transaction Management
27
 What if the system fails?
 What if more than one user is concurrently updating the
same data?
 A transaction is a collection of operations that performs a
single logical function in a database application
 Transaction-management component ensures that the
database remains in a consistent (correct) state despite
system failures (e.g., power failures and operating system
crashes) and transaction failures.
 Concurrency-control manager controls the interaction
among the concurrent transactions, to ensure the
consistency of the database.

D. NAGA JYOTHI CSE DEPT.

 Database Users and Administrators
28

Database

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 Database System Internals
29

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 Database Architecture
30
The architecture of a database systems is greatly influenced by
the underlying computer system on which the database is
running:
 Centralized
 Client-server
 Parallel (multi-processor)
 Distributed

D. NAGA JYOTHI CSE DEPT.

 History of Database Systems
31
 1950s and early 1960s:
 Data processing using magnetic tapes for storage
 Tapes provided only sequential access

 Punched cards for input

 Late 1960s and 1970s:
 Hard disks allowed direct access to data
 Network and hierarchical data models in widespread use
 Ted Codd defines the relational data model
 Would win the ACM Turing Award for this work
 IBM Research begins System R prototype
 UC Berkeley begins Ingres prototype

 High-performance (for the era) transaction processing

D. NAGA JYOTHI CSE DEPT.

 History (cont.)
32
 1980s:
 Research relational prototypes evolve into commercial systems
 SQL becomes industrial standard
 Parallel and distributed database systems
 Object-oriented database systems
 1990s:
 Large decision support and data-mining applications
 Large multi-terabyte data warehouses
 Emergence of Web commerce
 Early 2000s:
 XML and XQuery standards
 Automated database administration
 Later 2000s:
 Giant data storage systems
 Google BigTable, Yahoo PNuts, Amazon, ..
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Entity-Relationship Model

33

D. NAGA JYOTHI CSE DEPT.

 Chapter 7: Entity-Relationship
34Model
 Design Process
 Modeling
 Constraints
 E-R Diagram
 Design Issues
 Weak Entity Sets
 Extended E-R Features
 Design of the Bank Database
 Reduction to Relation Schemas
 Database Design
 UML

D. NAGA JYOTHI CSE DEPT.

35 Design Phases
 The initial phase of database design is to characterize fully
the data needs of the prospective database users.
 Next, the designer chooses a data model and, by
applying the concepts of the chosen data model,
translates these requirements into a conceptual schema
of the database.
 A fully developed conceptual schema also indicates the
functional requirements of the enterprise. In a
“specification of functional requirements”, users describe
the kinds of operations (or transactions) that will be
performed on the data.

D. NAGA JYOTHI CSE DEPT.

36 Design Phases (Cont.)
The process of moving from an abstract data model to the
implementation of the database proceeds in two final design
phases.

 Logical Design – Deciding on the database schema.

Database design requires that we find a “good”
collection of relation schemas.
 Business decision – What attributes should we record in
the database?
 Computer Science decision – What relation schemas
should we have and how should the attributes be
distributed among the various relation schemas?
 Physical Design – Deciding on the physical layout of
the database

D. NAGA JYOTHI CSE DEPT.

37 Design Approaches

 Entity Relationship Model (covered in this chapter)

 Models an enterprise as a collection of entities and
relationships
 Entity: a “thing” or “object” in the enterprise that is
distinguishable from other objects
 Described by a set of attributes

 Relationship: an association among several entities

 Represented diagrammatically by an entity-relationship

diagram:
 Normalization Theory (Chapter 8)
 Formalize what designs are bad, and test for them

D. NAGA JYOTHI CSE DEPT.

38

Outline of the ER Model

D. NAGA JYOTHI CSE DEPT.

39 ER model -- Database
design Modeling
 The ER data mode was developed to facilitate database
by allowing specification of an enterprise schema
that represents the overall logical structure of a database.
 The ER model is very useful in mapping the meanings and
interactions of real-world enterprises onto a conceptual
schema. Because of this usefulness, many database-design
tools draw on concepts from the ER model.
 The ER data model employs three basic concepts:
 entity sets,
 relationship sets,
 attributes.
 The ER model also has an associated diagrammatic
representation, the ER diagram, which can express the
overall logical structure of a database graphically.

D. NAGA JYOTHI CSE DEPT.

40 Entity Sets
 An entity is an object that exists and is distinguishable
from other objects.
 Example: specific person, company, event,
plant
 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
 An entity is represented by a set of attributes; i.e.,
descriptive properties possessed by all members of an
entity set.
 Example:
instructor = (ID, name, street, city, salary )
course= (course_id, title, credits)
 A subset of the attributes form a primary key of the
entity set; i.e., uniquely identifiying each member of the
set. D. NAGA JYOTHI CSE DEPT.
Entity Sets -- instructor and student
41
instructor_ID instructor_name student-ID student_name

D. NAGA JYOTHI CSE DEPT.

42 Relationship Sets
 A relationship is an association among several entities
Example:
44553 (Peltier) advisor 22222 (Einstein)
student entity relationship set instructor entity
 A relationship set is a mathematical relation among n  2 entities,
each taken from entity sets
{(e1, e2, … en) | e1  E1, e2  E2, …, en  En}

where (e1, e2, …, en) is a relationship

 Example:
(44553,22222)  advisor

D. NAGA JYOTHI CSE DEPT.

43 Relationship Set advisor

D. NAGA JYOTHI CSE DEPT.

44

Relationship Sets (Cont.)
An attribute can also be associated with a relationship set.
 For instance, the advisor relationship set between entity sets
instructor and student may have the attribute date which
tracks when the student started being associated with the
advisor

D. NAGA JYOTHI CSE DEPT.

45 Degree of a Relationship Set
 binary relationship
 involve two entity sets (or degree two).
 most relationship sets in a database system are binary.
 Relationships between more than two entity sets are rare.
Most relationships are binary. (More on this later.)
 Example: students work on research projects under the
guidance of an instructor.
 relationship proj_guide is a ternary relationship between
instructor, student, and project

D. NAGA JYOTHI CSE DEPT.

46 Mapping Cardinality
 Express the number of entities to which another entity can be
Constraints
associated via a relationship set.
 Most useful in describing binary relationship sets.
 For a binary relationship set the mapping cardinality must be
one of the following types:
 One to one
 One to many
 Many to one
 Many to many

D. NAGA JYOTHI CSE DEPT.

47 Mapping Cardinalities

One to one One to many

Note: Some elements in A and B may not be mapped to any

elements in the other set
D. NAGA JYOTHI CSE DEPT.
48 Mapping Cardinalities

Many to Many to many

one
Note: Some elements in A and B may not be mapped to any
elements in the other set
D. NAGA JYOTHI CSE DEPT.
49 Complex Attributes
 Attribute types:
 Simple and composite attributes.
 Single-valued and multivalued attributes
 Example: multivalued attribute: phone_numbers

 Derived attributes
 Can be computed from other attributes
 Example: age, given date_of_birth

 Domain – the set of permitted values for each attribute

D. NAGA JYOTHI CSE DEPT.

Composite Attributes
50

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51 Redundant Attributes
 Suppose we have entity sets:
 instructor, with attributes: ID, name, dept_name, salary
 department, with attributes: dept_name, building, budget
 We model the fact that each instructor has an
associated department using a relationship set
inst_dept
 The attribute dept_name appears in both entity sets.
Since it is the primary key for the entity set
department, it replicates information present in the
relationship and is therefore redundant in the entity set
instructor and needs to be removed.
 BUT: when converting back to tables, in some cases
the attribute gets reintroduced, as we will see later.

D. NAGA JYOTHI CSE DEPT.

52 Weak Entity Sets
 Consider a section entity, which is uniquely identified by a
course_id, semester, year, and sec_id.
 Clearly, section entities are related to course entities. Suppose
we create a relationship set sec_course between entity sets
section and course.
 Note that the information in sec_course is redundant, since
section already has an attribute course_id, which identifies the
course with which the section is related.
 One option to deal with this redundancy is to get rid of the
relationship sec_course; however, by doing so the relationship
between section and course becomes implicit in an attribute,
which is not desirable.

D. NAGA JYOTHI CSE DEPT.

53 Weak Entity Sets (Cont.)
 An alternative way to deal with this redundancy is to not store
the attribute course_id in the section entity and to only store
the remaining attributes section_id, year, and semester.
However, the entity set section then does not have enough
attributes to identify a particular section entity uniquely;
although each section entity is distinct, sections for different
courses may share the same section_id, year, and semester.
 To deal with this problem, we treat the relationship sec_course
as a special relationship that provides extra information, in this
case, the course_id, required to identify section entities
uniquely.
 The notion of weak entity set formalizes the above intuition. A
weak entity set is one whose existence is dependent on
another entity, called its identifying entity; instead of
associating a primary key with a weak entity, we use the
identifying entity, along with extra attributes called
discriminator to uniquely identify a weak entity. An entity set
that is not aJYOTHI
D. NAGA weak entity set is termed a strong entity set.
CSE DEPT.
54 Weak Entity Sets (Cont.)
 Every weak entity must be associated with an
identifying entity; that is, the weak entity set is said to be
existence dependent on the identifying entity set. The
identifying entity set is said to own the weak entity set
that it identifies. The relationship associating the weak
entity set with the identifying entity set is called the
identifying relationship.
 Note that the relational schema we eventually create
from the entity set section does have the attribute
course_id, for reasons that will become clear later, even
though we have dropped the attribute course_id from
the entity set section.

D. NAGA JYOTHI CSE DEPT.

55

E-R Diagrams

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Entity Sets
56
Entities can be represented graphically as follows:
• Rectangles represent entity sets.
• Attributes listed inside entity rectangle
• Underline indicates primary key attributes

D. NAGA JYOTHI CSE DEPT.

Relationship Sets
57

Diamonds represent relationship sets.

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58 Relationship Sets with
Attributes

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59 Roles
 Entity sets of a relationship need not be distinct
 Each occurrence of an entity set plays a “role” in the relationship
 The labels “course_id” and “prereq_id” are called roles.

D. NAGA JYOTHI CSE DEPT.

60 Cardinality Constraints
 We express cardinality constraints by drawing either a
directed line (→), signifying “one,” or an undirected line (—),
signifying “many,” between the relationship set and the entity
set.

 One-to-one relationship between an instructor and a student :

 A student is associated with at most one instructor via the
relationship advisor
 A student is associated with at most one department via
stud_dept

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 One-to-Many Relationship
61
 one-to-many relationship between an instructor and a student
 an instructor is associated with several (including 0) students via
advisor
 a student is associated with at most one instructor via advisor,

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Many-to-One Relationships
62
 In a many-to-one relationship between an instructor and a
student,
 an instructor is associated with at most one student via advisor,
 and a student is associated with several (including 0) instructors
via advisor

D. NAGA JYOTHI CSE DEPT.

63 Many-to-Many Relationship
 An instructor is associated with several (possibly 0) students
via advisor
 A student is associated with several (possibly 0) instructors
via advisor

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 Total and Partial Participation
64 Total participation (indicated by double line): every entity in the
entity set participates in at least one relationship in the relationship
set

participation of student in advisor relation is total

 every student must have an associated instructor
Partial participation: some entities may not participate in any
relationship in the relationship set
Example: participation of instructor in advisor is partial

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Notation for Expressing More Complex Constraints

65
A line may 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.
A maximum value of 1 indicates that the entity participates in
at most one relationship
A maximum value of * indicates no limit.

Instructor can advise 0 or more students. A student must have

1 advisor; cannot have multiple advisors

D. NAGA JYOTHI CSE DEPT.

 Notation to Express Entity with Complex Attributes

66

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Expressing Weak Entity Sets
67
 In E-R diagrams, a weak entity set is depicted via a double
rectangle.
 We underline the discriminator of a weak entity set with a
dashed line.
 The relationship set connecting the weak entity set to the
identifying strong entity set is depicted by a double diamond.
 Primary key for section – (course_id, sec_id, semester, year)

D. NAGA JYOTHI CSE DEPT.

 E-R Diagram for a University
Enterprise
68

D. NAGA JYOTHI CSE DEPT.

69

Reduction to Relation Schemas

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 Reduction to Relation Schemas
70
 Entity sets and relationship sets can be expressed uniformly as
relation schemas that represent the contents of the database.
 A database which conforms to an E-R diagram can be
represented by a collection of schemas.
 For each entity set and relationship set there is a unique schema
that is assigned the name of the corresponding entity set or
relationship set.
 Each schema has a number of columns (generally
corresponding to attributes), which have unique names.

D. NAGA JYOTHI CSE DEPT.

71 Representing Entity Sets
 A strong entity set reduces to a schema with the same
attributes

student(ID, name, tot_cred)

 A weak entity set becomes a table that includes a column

for the primary key of the identifying strong entity set

section ( course_id, sec_id, sem, year )

D. NAGA JYOTHI CSE DEPT.

Representing Relationship Sets
72
 A many-to-many relationship set is represented as a schema
with attributes for the primary keys of the two participating
entity sets, and any descriptive attributes of the relationship
set.
 Example: schema for relationship set advisor

advisor = (s_id, i_id)

D. NAGA JYOTHI CSE DEPT.

Representation of Entity Sets with Composite Attributes

73
 Composite attributes are flattened out by
creating a separate attribute for each
component attribute
 Example: given entity set instructor with composite
attribute name with component attributes
first_name and last_name the schema
corresponding to the entity set has two attributes
name_first_name and name_last_name
 Prefix omitted if there is no ambiguity
(name_first_name could be first_name)

 Ignoring multivalued attributes, extended

instructor schema is
 instructor(ID,
first_name, middle_initial, last_name,
street_number, street_name,
apt_number, city, state, zip_code,
date_of_birth)

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Representation of Entity Sets with Multivalued Attributes

74
 A multivalued attribute M of an entity E is represented by a
separate schema EM
 Schema EM has attributes corresponding to the primary key
of E and an attribute corresponding to multivalued attribute
M
 Example: Multivalued attribute phone_number of instructor is
represented by a schema:
inst_phone= ( ID, phone_number)
 Each value of the multivalued attribute maps to a separate
tuple of the relation on schema EM
 For example, an instructor entity with primary key 22222 and
phone numbers 456-7890 and 123-4567 maps to two tuples:
(22222, 456-7890) and (22222, 123-4567)

D. NAGA JYOTHI CSE DEPT.

75 Redundancy of Schemas
Many-to-one and one-to-many relationship sets that are total on the
many-side can be represented by adding an extra attribute to the
“many” side, containing the primary key of the “one” side
Example: Instead of creating a schema for relationship set inst_dept,
add an attribute dept_name to the schema arising from entity set
instructor

D. NAGA JYOTHI CSE DEPT.

76 Redundancy of Schemas

chosen(Cont.)
For one-to-one relationship sets, either side can be
to act as the “many” side
 That is, an extra attribute can be added to either of the
tables corresponding to the two entity sets
 If participation is partial on the “many” side, replacing
a schema by an extra attribute in the schema
corresponding to the “many” side could result in null
values

D. NAGA JYOTHI CSE DEPT.

77 Redundancy of Schemas

(Cont.)
The schema corresponding to a relationship set linking a
weak entity set to its identifying strong entity set is redundant.

 Example: The section schema already contains the attributes

that would appear in the sec_course schema

D. NAGA JYOTHI CSE DEPT.

78

Advanced Topics

D. NAGA JYOTHI CSE DEPT.

Non-binary Relationship Sets
79
 Most relationship sets are binary
 There are occasions when it is more convenient to
represent relationships as non-binary.
 E-R Diagram with a Ternary Relationship

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Cardinality Constraints on Ternary Relationship
80
 We allow at most one arrow out of a ternary (or greater
degree) relationship to indicate a cardinality constraint
 For exampe, an arrow from proj_guide to instructor indicates
each student has at most one guide for a project
 If there is more than one arrow, there are two ways of
defining the meaning.
 For example, a ternary relationship R between A, B and C with
arrows to B and C could mean
1. Each A entity is associated with a unique entity from B and
C or
2. Each pair of entities from (A, B) is associated with a unique
C entity, and each pair (A, C) is associated with a unique B

 Each alternative has been used in different formalisms

 To avoid confusion we outlaw more than one arrow

D. NAGA JYOTHI CSE DEPT.

81 Specialization
 Top-down design process; we designate sub-groupings
within an entity set that are distinctive from other entities
in the set.
 These sub-groupings become lower-level entity sets that
have attributes or participate in relationships that do not
apply to the higher-level entity set.
 Depicted by a triangle component labeled ISA (e.g.,
instructor “is a” person).
 Attribute inheritance – a lower-level entity set inherits all
the attributes and relationship participation of the higher-
level entity set to which it is linked.

D. NAGA JYOTHI CSE DEPT.

82 Specialization Example
 Overlapping – employee and student
 Disjoint – instructor and secretary
 Total and partial

D. NAGA JYOTHI CSE DEPT.

 Representing Specialization via Schemas
83
 Method 1:
 Form a schema for the higher-level entity
 Form a schema for each lower-level entity set, include primary
key of higher-level entity set and local attributes

schema attributes
person ID, name, street, city
student ID, tot_cred
employee ID, salary

 Drawback: getting information about, an employee requires

accessing two relations, the one corresponding to the low-level
schema and the one corresponding to the high-level schema

D. NAGA JYOTHI CSE DEPT.

Representing Specialization as Schemas (Cont.)
84
 Method 2:
 Form a schema for each entity set with all local and inherited
attributes

schema attributes
person ID, name, street, city
student ID, name, street, city, tot_cred
employee ID, name, street, city, salary

 Drawback: name, street and city may be stored redundantly

for people who are both students and employees

D. NAGA JYOTHI CSE DEPT.

85 Generalization
 A bottom-up design process – combine a number of
entity sets that share the same features into a higher-level
entity set.
 Specialization and generalization are simple inversions of
each other; they are represented in an E-R diagram in the
same way.
 The terms specialization and generalization are used
interchangeably.

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 Design Constraints on a Specialization/Generalization
86
 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
 Partial generalization is the default. We can specify total
generalization in an ER diagram by adding the keyword total in the
diagram and drawing a dashed line from the keyword to the
corresponding hollow arrow-head to which it applies (for a total
generalization), or to the set of hollow arrow-heads to which it
applies (for an overlapping generalization).
 The student generalization is total: All student entities must be either
graduate or undergraduate. 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

D. NAGA JYOTHI CSE DEPT.

 Aggregation
87
Consider the ternary relationship proj_guide, which we saw earlier
Suppose we want to record evaluations of a student by a guide
on a project

D. NAGA JYOTHI CSE DEPT.

88 Aggregation (Cont.)
 Relationship sets eval_for and proj_guide represent
overlapping information
 Every eval_for relationship corresponds to a proj_guide
relationship
 However, some proj_guide relationships may not correspond to
any eval_for relationships
 So we can’t discard the proj_guide relationship

 Eliminate this redundancy via aggregation

 Treat relationship as an abstract entity
 Allows relationships between relationships
 Abstraction of relationship into new entity

D. NAGA JYOTHI CSE DEPT.

89 Aggregation (Cont.)
 Eliminate this redundancy via aggregation without introducing
redundancy, the following diagram represents:
 A student is guided by a particular instructor on a particular project
 A student, instructor, project combination may have an associated
evaluation

D. NAGA JYOTHI CSE DEPT.

 Representing Aggregation via Schemas

90
To represent aggregation, create a schema containing
Primary key of the aggregated relationship,
The primary key of the associated entity set
Any descriptive attributes
In our example:
The schema eval_for is:
eval_for (s_ID, project_id, i_ID, evaluation_id)
The schema proj_guide is redundant.

D. NAGA JYOTHI CSE DEPT.

91
Design Issues

D. NAGA JYOTHI CSE DEPT.

 Entities vs. Attributes
92
 Use of entity sets vs. attributes

 Use of phone as an entity allows extra information about phone

numbers (plus multiple phone numbers)

D. NAGA JYOTHI CSE DEPT.

93 Entities vs. Relationship sets
 Use of entity sets vs. relationship sets
Possible guideline is to designate a relationship set to
describe an action that occurs between entities

 Placement of relationship attributes

For example, attribute date as attribute of advisor
or as attribute of student

D. NAGA JYOTHI CSE DEPT.

 Binary Vs. Non-Binary Relationships
94
 Although it is possible to replace any non-binary (n-ary, for n
> 2) relationship set by a number of distinct binary
relationship sets, a n-ary relationship set shows more clearly
that several entities participate in a single relationship.
 Some relationships that appear to be non-binary may be
better represented using binary relationships
 For example, a ternary relationship parents, relating a child to
his/her father and mother, is best replaced by two binary
relationships, father and mother
 Using two binary relationships allows partial information (e.g., only
mother being known)

 But there are some relationships that are naturally non-binary

 Example: proj_guide

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 Converting Non-Binary Relationships to Binary Form

95
 In general, any non-binary relationship can be represented using
binary relationships by creating an artificial entity set.
 Replace R between entity sets A, B and C by an entity set E, and
three relationship sets:
1. RA, relating E and A 2. RB, relating E and B
3. RC, relating E and C
 Create an identifying attribute for E and add any attributes of R to E
 For each relationship (ai , bi , ci) in R, create
1. a new entity ei in the entity set E 2. add (ei , ai ) to RA
3. add (ei , bi ) to RB 4. add (ei , ci ) to RC

D. NAGA JYOTHI CSE DEPT.

 Converting Non-Binary Relationships (Cont.)
96
 Also need to translate constraints
 Translating all constraints may not be possible
 There may be instances in the translated schema that
cannot correspond to any instance of R
 Exercise: add constraints to the relationships RA, RB and RC to ensure
that a newly created entity corresponds to exactly one entity in
each of entity sets A, B and C

 We can avoid creating an identifying attribute by making E a

weak entity set (described shortly) identified by the three
relationship sets

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97 E-R Design Decisions
 The use of an attribute or entity set to represent an object.
 Whether a real-world concept is best expressed by an entity
set or a relationship set.
 The use of a ternary relationship versus a pair of binary
relationships.
 The use of a strong or weak entity set.
 The use of specialization/generalization – contributes to
modularity in the design.
 The use of aggregation – can treat the aggregate entity set
as a single unit without concern for the details of its internal
structure.

D. NAGA JYOTHI CSE DEPT.

Summary of Symbols Used in E-R Notation
98

D. NAGA JYOTHI CSE DEPT.

99 Symbols Used in E-R Notation
(Cont.)

D. NAGA JYOTHI CSE DEPT.

10
0 Alternative ER Notations
 Chen, IDE1FX, …

D. NAGA JYOTHI CSE DEPT.

10
1 Alternative ER Notations
Chen IDE1FX (Crows feet notation)

D. NAGA JYOTHI CSE DEPT.

10
2 UML
 UML: Unified Modeling Language
 UML has many components to graphically model different
aspects of an entire software system
 UML Class Diagrams correspond to E-R Diagram, but several
differences.

D. NAGA JYOTHI CSE DEPT.

 ER vs. UML Class Diagrams
10
3

*Note reversal of position in cardinality constraint depiction

D. NAGA JYOTHI CSE DEPT.
10
4 ER vs. UML ClassEquivalent
ER Diagram Notation
Diagrams in UML

*Generalization can use merged or separate arrows independent

of disjoint/overlapping
D. NAGA JYOTHI CSE DEPT.
10
5 UML Class Diagrams (Cont.)
 Binary relationship sets are represented in UML by just drawing
a line connecting the entity sets. The relationship set name is
written adjacent to the line.
 The role played by an entity set in a relationship set may also
be specified by writing the role name on the line, adjacent to
the entity set.
 The relationship set name may alternatively be written in a
box, along with attributes of the relationship set, and the box
is connected, using a dotted line, to the line depicting the
relationship set.

D. NAGA JYOTHI CSE DEPT.

 End of Chapter 7
10
6

D. NAGA JYOTHI CSE DEPT.