Fundamentals of Database IT403

College of Computing and Informatics

Assignment 2
Deadline: 27/11/2024 @ 23:59
[Total Mark for this Assignment is 8]
Student Details:
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ID: ###

CRN: ###
Instructions:

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• Use Times New Roman font for all your answers.
Restricted – ‫مقيد‬

Question One

Pg. 01
Learning
Outcome(s):

Question One

CLO 3

Consider the following EMPLOYEE relation from a company database. Write

Create EntityRelationship
model, Relational
model, and write
SQL queries

2 Marks

SQL queries to answer the following questions:
EmpID

Name

Salary

Gender

Address

123

Sarah

50000

Female

Jeddah

234

Mohamed

45000

Male

Riyadh

376

Ahmed

70000

Male

Jeddah

498

Maryam

10000

Female

Jeddah

555

Muna

30000

Female

Riyadh

1- Retrieve the count of Female Employees.
2- Retrieve the average Salary of all Employees.
3- Delete the record of the Employee named “Muna”.
4- Insert a new employee into the table.

Restricted – ‫مقيد‬

Question Two

Pg. 02
Learning
Outcome(s):

Question Two

2 Marks

Answer the following questions by referring to the tables below:
CLO 3: Create
EntityRelationship
model, Relational
model, and write
SQL queries.

COURSE
Course_name

Course_number

Credit_hours

Department

Intro to Computer Science

CS310

4

CS

System Analysis and
Design

IT353

4

IT

Discrete Mathematics

MATH410

3

MATH

IT403

3

IT

Fundamentals of
Database

SECTION
Section_ID
80

Course_no
MATH410

Semester
Fall

Year
23

Instructor
Sara

95
103

CS310
IT353

Fall
Fall

23
24

Ali
Khaled

115

MATH410

Fall

24

Ahmed

120
140

CS310
IT403

Spring
Fall

24
24

Ali
Eman

a) Create a virtual table called (COURSE_ON_SEC) that summarize courses
information along with its section and instructor, listed alphabetically by
course name.
b) Retrieve the section ID, course name, course number, department, and
instructor of the courses taught by instructor “Ali” from
COURSE_ON_SEC table.

Restricted – ‫مقيد‬

Question Three

Pg. 03
Learning
Outcome(s):
CLO4 – Design a
database starting

Question Three

2 Marks

You are given a table with unnormalized design. You are required to transform the
provided table into First Normal Form (1NF), and Second Normal Form (2NF). For
each step, provide the new table(s) and explain the changes made.

from the
conceptual design

Order_ID

Supplier_Name

Products_Info

Order_Date

001

Amazon

P1, product101,
product102

01-11-2024

002

Noon

P2, product201,
product202,
product203

04-11-2024

003

Alim

P3, product301

10-11-2024

to the
implementation of
database
schemas

Restricted – ‫مقيد‬

Question Four

Pg. 04
Learning
Outcome(s):
CLO3:

Question Four

2 Marks

Using join:
–

write an SQL query to retrieve the names and addresses of all employees who work in
Research departments.

Create EntityRelationship
model, Relational
model, and write
SQL queries.

Restricted – ‫مقيد‬

–

Write an SQL query to retrieve the names of all employees who have a higher salary
than their manager. Use a self-join to compare the salaries.

FUNDAMENTALS OF

Database
Systems
SEVENTH EDITION

This page intentionally left blank

FUNDAMENTALS OF

Database
Systems
SEVENTH EDITION

Ramez Elmasri
Department of Computer Science and Engineering
The University of Texas at Arlington

Shamkant B. Navathe
College of Computing
Georgia Institute of Technology

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Library of Congress Cataloging-in-Publication Data on File

10 9 8 7 6 5 4 3 2 1
ISBN-10:
0-13-397077-9
ISBN-13: 978-0-13-397077-7

To Amalia
and
to Ramy, Riyad, Katrina, and Thomas
R. E.
To my wife Aruna for her love, support, and understanding
and
to Rohan, Maya, and Ayush for bringing so much joy into our lives
S.B.N.

This page intentionally left blank

Preface

T

his book introduces the fundamental concepts
necessary for designing, using, and implementing
database systems and database applications. Our presentation stresses the fundamentals of database modeling and design, the languages and models provided by the
database management systems, and database system implementation techniques.
The book is meant to be used as a textbook for a one- or two-semester course in
database systems at the junior, senior, or graduate level, and as a reference book. Our
goal is to provide an in-depth and up-to-date presentation of the most important
aspects of database systems and applications, and related technologies. We assume
that readers are familiar with elementary programming and data-structuring concepts and that they have had some exposure to the basics of computer organization.

New to This Edition
The following key features have been added in the seventh edition:
■

A reorganization of the chapter ordering (this was based on a survey of the
instructors who use the textbook); however, the book is still organized so
that the individual instructor can choose to follow the new chapter ordering
or choose a different ordering of chapters (for example, follow the chapter
order from the sixth edition) when presenting the materials.
■ There are two new chapters on recent advances in database systems and big
data processing; one new chapter (Chapter 24) covers an introduction to the
newer class of database systems known as NOSQL databases, and the other
new chapter (Chapter 25) covers technologies for processing big data,
including MapReduce and Hadoop.
■ The chapter on query processing and optimization has been expanded and
reorganized into two chapters; Chapter 18 focuses on strategies and algorithms for query processing whereas Chapter 19 focuses on query optimization techniques.
■ A second UNIVERSITY database example has been added to the early chapters (Chapters 3 through 8) in addition to our COMPANY database example
from the previous editions.
■ Many of the individual chapters have been updated to varying degrees to include
newer techniques and methods; rather than discuss these enhancements here,
vii

viii

Preface

we will describe them later in the preface when we discuss the organization of
the seventh edition.
The following are key features of the book:
■

A self-contained, flexible organization that can be tailored to individual
needs; in particular, the chapters can be used in different orders depending
on the instructor’s preference.
■ A companion website (
includes data to be loaded into various types of relational databases for more
realistic student laboratory exercises.
■ A dependency chart (shown later in this preface) to show which chapters
depend on other earlier chapters; this can guide the instructor who wants to
tailor the order of presentation of the chapters.
■ A collection of supplements, including a robust set of materials for instructors and students such as PowerPoint slides, figures from the text, and an
instructor’s guide with solutions.

Organization and Contents of the Seventh Edition
There are some organizational changes in the seventh edition as well as improvement to the individual chapters. The book is now divided into 12 parts as follows:
■

Part 1 (Chapters 1 and 2) describes the basic introductory concepts necessary for a good understanding of database models, systems, and languages.
Chapters 1 and 2 introduce databases, typical users, and DBMS concepts,
terminology, and architecture, as well as a discussion of the progression of
database technologies over time and a brief history of data models. These
chapters have been updated to introduce some of the newer technologies
such as NOSQL systems.

■

Part 2 (Chapters 3 and 4) includes the presentation on entity-relationship
modeling and database design; however, it is important to note that instructors can cover the relational model chapters (Chapters 5 through 8) before
Chapters 3 and 4 if that is their preferred order of presenting the course
materials. In Chapter 3, the concepts of the Entity-Relationship (ER) model
and ER diagrams are presented and used to illustrate conceptual database
design. Chapter 4 shows how the basic ER model can be extended to incorporate additional modeling concepts such as subclasses, specialization, generalization, union types (categories) and inheritance, leading to the
enhanced-ER (EER) data model and EER diagrams. The notation for the class
diagrams of UML are also introduced in Chapters 7 and 8 as an alternative
model and diagrammatic notation for ER/EER diagrams.

■

Part 3 (Chapters 5 through 8) includes a detailed presentation on relational
databases and SQL with some additional new material in the SQL chapters
to cover a few SQL constructs that were not in the previous edition. Chapter 5

Preface

describes the basic relational model, its integrity constraints, and update
operations. Chapter 6 describes some of the basic parts of the SQL standard
for relational databases, including data definition, data modification operations, and simple SQL queries. Chapter 7 presents more complex SQL queries, as well as the SQL concepts of triggers, assertions, views, and schema
modification. Chapter 8 describes the formal operations of the relational
algebra and introduces the relational calculus. The material on SQL (Chapters 6 and 7) is presented before our presentation on relational algebra and
calculus in Chapter 8 to allow instructors to start SQL projects early in a
course if they wish (it is possible to cover Chapter 8 before Chapters 6 and 7
if the instructor desires this order). The final chapter in Part 2, Chapter 9,
covers ER- and EER-to-relational mapping, which are algorithms that can be
used for designing a relational database schema from a conceptual ER/EER
schema design.
■

Part 4 (Chapters 10 and 11) are the chapters on database programming techniques; these chapters can be assigned as reading materials and augmented
with materials on the particular language used in the course for programming projects (much of this documentation is readily available on the Web).
Chapter 10 covers traditional SQL programming topics, such as embedded
SQL, dynamic SQL, ODBC, SQLJ, JDBC, and SQL/CLI. Chapter 11 introduces
Web database programming, using the PHP scripting language in our examples, and includes new material that discusses Java technologies for Web
database programming.

■

Part 5 (Chapters 12 and 13) covers the updated material on object-relational
and object-oriented databases (Chapter 12) and XML (Chapter 13); both of
these chapters now include a presentation of how the SQL standard incorporates object concepts and XML concepts into more recent versions of the
SQL standard. Chapter 12 first introduces the concepts for object databases,
and then shows how they have been incorporated into the SQL standard in
order to add object capabilities to relational database systems. It then covers
the ODMG object model standard, and its object definition and query languages. Chapter 13 covers the XML (eXtensible Markup Language) model
and languages, and discusses how XML is related to database systems. It
presents XML concepts and languages, and compares the XML model to
traditional database models. We also show how data can be converted
between the XML and relational representations, and the SQL commands
for extracting XML documents from relational tables.

■

Part 6 (Chapters 14 and 15) are the normalization and relational design
theory chapters (we moved all the formal aspects of normalization algorithms to Chapter 15). Chapter 14 defines functional dependencies, and
the normal forms that are based on functional dependencies. Chapter 14
also develops a step-by-step intuitive normalization approach, and includes
the definitions of multivalued dependencies and join dependencies.
Chapter 15 covers normalization theory, and the formalisms, theories,

ix

x

Preface

and algorithms developed for relational database design by normalization, including the relational decomposition algorithms and the relational
synthesis algorithms.
■ Part 7 (Chapters 16 and 17) contains the chapters on file organizations on
disk (Chapter 16) and indexing of database files (Chapter 17). Chapter 16
describes primary methods of organizing files of records on disk, including
ordered (sorted), unordered (heap), and hashed files; both static and
dynamic hashing techniques for disk files are covered. Chapter 16 has been
updated to include materials on buffer management strategies for DBMSs as
well as an overview of new storage devices and standards for files and modern storage architectures. Chapter 17 describes indexing techniques for files,
including B-tree and B+-tree data structures and grid files, and has been
updated with new examples and an enhanced discussion on indexing,
including how to choose appropriate indexes and index creation during
physical design.
■

Part 8 (Chapters 18 and 19) includes the chapters on query processing algorithms (Chapter 18) and optimization techniques (Chapter 19); these two
chapters have been updated and reorganized from the single chapter that
covered both topics in the previous editions and include some of the newer
techniques that are used in commercial DBMSs. Chapter 18 presents algorithms for searching for records on disk files, and for joining records from
two files (tables), as well as for other relational operations. Chapter 18 contains new material, including a discussion of the semi-join and anti-join
operations with examples of how they are used in query processing, as well
as a discussion of techniques for selectivity estimation. Chapter 19 covers
techniques for query optimization using cost estimation and heuristic rules;
it includes new material on nested subquery optimization, use of histograms,
physical optimization, and join ordering methods and optimization of
typical queries in data warehouses.

■

Part 9 (Chapters 20, 21, and 22) covers transaction processing concepts;
concurrency control; and database recovery from failures. These chapters
have been updated to include some of the newer techniques that are used
in some commercial and open source DBMSs. Chapter 20 introduces the
techniques needed for transaction processing systems, and defines the
concepts of recoverability and serializability of schedules; it has a new section on buffer replacement policies for DBMSs and a new discussion on
the concept of snapshot isolation. Chapter 21 gives an overview of the various types of concurrency control protocols, with a focus on two-phase
locking. We also discuss timestamp ordering and optimistic concurrency
control techniques, as well as multiple-granularity locking. Chapter 21
includes a new presentation of concurrency control methods that are based
on the snapshot isolation concept. Finally, Chapter 23 focuses on database
recovery protocols, and gives an overview of the concepts and techniques
that are used in recovery.

Preface

■

Part 10 (Chapters 23, 24, and 25) includes the chapter on distributed databases (Chapter 23), plus the two new chapters on NOSQL storage systems
for big data (Chapter 24) and big data technologies based on Hadoop and
MapReduce (Chapter 25). Chapter 23 introduces distributed database
concepts, including availability and scalability, replication and fragmentation of data, maintaining data consistency among replicas, and many other
concepts and techniques. In Chapter 24, NOSQL systems are categorized
into four general categories with an example system in each category used
for our examples, and the data models, operations, as well as the replication/distribution/scalability strategies of each type of NOSQL system are
discussed and compared. In Chapter 25, the MapReduce programming
model for distributed processing of big data is introduced, and then we
have presentations of the Hadoop system and HDFS (Hadoop Distributed
File System), as well as the Pig and Hive high-level interfaces, and the
YARN architecture.
■ Part 11 (Chapters 26 through 29) is entitled Advanced Database Models,
Systems, and Applications and includes the following materials: Chapter 26
introduces several advanced data models including active databases/triggers (Section 26.1), temporal databases (Section 26.2), spatial databases (Section 26.3), multimedia databases (Section 26.4), and deductive
databases (Section 26.5). Chapter 27 discusses information retrieval (IR)
and Web search, and includes topics such as IR and keyword-based search,
comparing DB with IR, retrieval models, search evaluation, and ranking
algorithms. Chapter 28 is an introduction to data mining including overviews of various data mining methods such as associate rule mining, clustering, classification, and sequential pattern discovery. Chapter 29 is an
overview of data warehousing including topics such as data warehousing
models and operations, and the process of building a data warehouse.
■ Part 12 (Chapter 30) includes one chapter on database security, which
includes a discussion of SQL commands for discretionary access control
(GRANT, REVOKE), as well as mandatory security levels and models for
including mandatory access control in relational databases, and a discussion
of threats such as SQL injection attacks, as well as other techniques and
methods related to data security and privacy.
Appendix A gives a number of alternative diagrammatic notations for displaying a
conceptual ER or EER schema. These may be substituted for the notation we use, if
the instructor prefers. Appendix B gives some important physical parameters of
disks. Appendix C gives an overview of the QBE graphical query language, and
Appendixes D and E (available on the book’s Companion Website located at
cover legacy database systems, based on
the hierarchical and network database models. They have been used for more than
thirty years as a basis for many commercial database applications and transactionprocessing systems.

xi

xii

Preface

Guidelines for Using This Book
There are many different ways to teach a database course. The chapters in Parts 1
through 7 can be used in an introductory course on database systems in the order
that they are given or in the preferred order of individual instructors. Selected chapters and sections may be left out and the instructor can add other chapters from the
rest of the book, depending on the emphasis of the course. At the end of the opening section of some of the book’s chapters, we list sections that are candidates for
being left out whenever a less-detailed discussion of the topic is desired. We suggest
covering up to Chapter 15 in an introductory database course and including selected
parts of other chapters, depending on the background of the students and the
desired coverage. For an emphasis on system implementation techniques, chapters
from Parts 7, 8, and 9 should replace some of the earlier chapters.
Chapters 3 and 4, which cover conceptual modeling using the ER and EER models,
are important for a good conceptual understanding of databases. However, they
may be partially covered, covered later in a course, or even left out if the emphasis
is on DBMS implementation. Chapters 16 and 17 on file organizations and indexing
may also be covered early, later, or even left out if the emphasis is on database models and languages. For students who have completed a course on file organization,
parts of these chapters can be assigned as reading material or some exercises can be
assigned as a review for these concepts.
If the emphasis of a course is on database design, then the instructor should cover
Chapters 3 and 4 early on, followed by the presentation of relational databases. A
total life-cycle database design and implementation project would cover conceptual
design (Chapters 3 and 4), relational databases (Chapters 5, 6, and 7), data model
mapping (Chapter 9), normalization (Chapter 14), and application programs
implementation with SQL (Chapter 10). Chapter 11 also should be covered if the
emphasis is on Web database programming and applications. Additional documentation on the specific programming languages and RDBMS used would be required.
The book is written so that it is possible to cover topics in various sequences. The
following chapter dependency chart shows the major dependencies among chapters. As the diagram illustrates, it is possible to start with several different topics
following the first two introductory chapters. Although the chart may seem complex, it is important to note that if the chapters are covered in order, the dependencies are not lost. The chart can be consulted by instructors wishing to use an
alternative order of presentation.
For a one-semester course based on this book, selected chapters can be assigned as
reading material. The book also can be used for a two-semester course sequence.
The first course, Introduction to Database Design and Database Systems, at the
sophomore, junior, or senior level, can cover most of Chapters 1 through 15. The
second course, Database Models and Implementation Techniques, at the senior or
first-year graduate level, can cover most of Chapters 16 through 30. The twosemester sequence can also be designed in various other ways, depending on the
preferences of the instructors.

Preface

xiii

1, 2
Introductory

5
Relational
Model

3, 4
ER, EER
Models

6, 7
SQL

8
Relational
Algebra
16, 17
File Organization,
Indexing

9
ER-, EER-toRelational

20, 21, 22
Transactions,
CC, Recovery
14, 15
FD, MVD,
Normalization

23, 24, 25
DDB, NOSQL,
Big Data

12, 13
ODB, ORDB,
XML

10, 11
DB, Web
Programming

26, 27
Advanced
Models, IR

28, 29
Data Mining,
Warehousing

18, 19
Query Processing,
Optimization

Supplemental Materials
Support material is available to qualified instructors at Pearson’s instructor
resource center ( For access, contact your
local Pearson representative.
■

PowerPoint lecture notes and figures.
■ A solutions manual.

Acknowledgments
It is a great pleasure to acknowledge the assistance and contributions of many individuals to this effort. First, we would like to thank our editor, Matt Goldstein, for
his guidance, encouragement, and support. We would like to acknowledge the
excellent work of Rose Kernan for production management, Patricia Daly for a

30
DB
Security

xiv

Preface

thorough copy editing of the book, Martha McMaster for her diligence in proofing
the pages, and Scott Disanno, Managing Editor of the production team. We also
wish to thank Kelsey Loanes from Pearson for her continued help with the project,
and reviewers Michael Doherty, Deborah Dunn, Imad Rahal, Karen Davis, Gilliean
Lee, Leo Mark, Monisha Pulimood, Hassan Reza, Susan Vrbsky, Li Da Xu, Weining
Zhang and Vincent Oria.
Ramez Elmasri would like to thank Kulsawasd Jitkajornwanich, Vivek Sharma, and
Surya Swaminathan for their help with preparing some of the material in Chapter 24. Sham Navathe would like to acknowledge the following individuals who
helped in critically reviewing and revising various topics. Dan Forsythe and Satish
Damle for discussion of storage systems; Rafi Ahmed for detailed re-organization
of the material on query processing and optimization; Harish Butani, Balaji
Palanisamy, and Prajakta Kalmegh for their help with the Hadoop and MapReduce
technology material; Vic Ghorpadey and Nenad Jukic for revision of the Data
Warehousing material; and finally, Frank Rietta for newer techniques in database
security, Kunal Malhotra for various discussions, and Saurav Sahay for advances in
information retrieval systems.
We would like to repeat our thanks to those who have reviewed and contributed to
previous editions of Fundamentals of Database Systems.
■

First edition. Alan Apt (editor), Don Batory, Scott Downing, Dennis
Heimbinger, Julia Hodges, Yannis Ioannidis, Jim Larson, Per-Ake Larson,
Dennis McLeod, Rahul Patel, Nicholas Roussopoulos, David Stemple,
Michael Stonebraker, Frank Tompa, and Kyu-Young Whang.
■ Second edition. Dan Joraanstad (editor), Rafi Ahmed, Antonio Albano, David
Beech, Jose Blakeley, Panos Chrysanthis, Suzanne Dietrich, Vic Ghorpadey,
Goetz Graefe, Eric Hanson, Junguk L. Kim, Roger King, Vram Kouramajian,
Vijay Kumar, John Lowther, Sanjay Manchanda, Toshimi Minoura, Inderpal
Mumick, Ed Omiecinski, Girish Pathak, Raghu Ramakrishnan, Ed Robertson,
Eugene Sheng, David Stotts, Marianne Winslett, and Stan Zdonick.
■ Third edition. Maite Suarez-Rivas and Katherine Harutunian (editors);
Suzanne Dietrich, Ed Omiecinski, Rafi Ahmed, Francois Bancilhon, Jose
Blakeley, Rick Cattell, Ann Chervenak, David W. Embley, Henry A. Etlinger,
Leonidas Fegaras, Dan Forsyth, Farshad Fotouhi, Michael Franklin, Sreejith
Gopinath, Goetz Craefe, Richard Hull, Sushil Jajodia, Ramesh K. Karne,
Harish Kotbagi, Vijay Kumar, Tarcisio Lima, Ramon A. Mata-Toledo, Jack
McCaw, Dennis McLeod, Rokia Missaoui, Magdi Morsi, M. Narayanaswamy,
Carlos Ordonez, Joan Peckham, Betty Salzberg, Ming-Chien Shan, Junping
Sun, Rajshekhar Sunderraman, Aravindan Veerasamy, and Emilia E. Villareal.
■ Fourth edition. Maite Suarez-Rivas, Katherine Harutunian, Daniel Rausch,
and Juliet Silveri (editors); Phil Bernhard, Zhengxin Chen, Jan Chomicki,
Hakan Ferhatosmanoglu, Len Fisk, William Hankley, Ali R. Hurson, Vijay
Kumar, Peretz Shoval, Jason T. L. Wang (reviewers); Ed Omiecinski (who
contributed to Chapter 27). Contributors from the University of Texas at

Preface

Arlington are Jack Fu, Hyoil Han, Babak Hojabri, Charley Li, Ande Swathi,
and Steven Wu; Contributors from Georgia Tech are Weimin Feng, Dan Forsythe, Angshuman Guin, Abrar Ul-Haque, Bin Liu, Ying Liu, Wanxia Xie,
and Waigen Yee.
■ Fifth edition. Matt Goldstein and Katherine Harutunian (editors); Michelle
Brown, Gillian Hall, Patty Mahtani, Maite Suarez-Rivas, Bethany Tidd, and
Joyce Cosentino Wells (from Addison-Wesley); Hani Abu-Salem, Jamal R.
Alsabbagh, Ramzi Bualuan, Soon Chung, Sumali Conlon, Hasan Davulcu,
James Geller, Le Gruenwald, Latifur Khan, Herman Lam, Byung S. Lee,
Donald Sanderson, Jamil Saquer, Costas Tsatsoulis, and Jack C. Wileden
(reviewers); Raj Sunderraman (who contributed the laboratory projects);
Salman Azar (who contributed some new exercises); Gaurav Bhatia, Fariborz Farahmand, Ying Liu, Ed Omiecinski, Nalini Polavarapu, Liora Sahar,
Saurav Sahay, and Wanxia Xie (from Georgia Tech).
■ Sixth edition. Matt Goldstein (editor); Gillian Hall (production management); Rebecca Greenberg (copy editing); Jeff Holcomb, Marilyn Lloyd,
Margaret Waples, and Chelsea Bell (from Pearson); Rafi Ahmed, Venu
Dasigi, Neha Deodhar, Fariborz Farahmand, Hariprasad Kumar, Leo Mark,
Ed Omiecinski, Balaji Palanisamy, Nalini Polavarapu, Parimala R. Pranesh,
Bharath Rengarajan, Liora Sahar, Saurav Sahay, Narsi Srinivasan, and
Wanxia Xie.
Last, but not least, we gratefully acknowledge the support, encouragement, and
patience of our families.
R. E.
S.B.N.

xv

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Contents
Preface
vii
About the Authors

xxx

1

■ part
Introduction to Databases ■
chapter 1 Databases and Database Users

3

1.1 Introduction
4
1.2 An Example
6
1.3 Characteristics of the Database Approach
10
1.4 Actors on the Scene
15
1.5 Workers behind the Scene
17
1.6 Advantages of Using the DBMS Approach
17
1.7 A Brief History of Database Applications
23
1.8 When Not to Use a DBMS
27
1.9 Summary
27
Review Questions
28
Exercises
28
Selected Bibliography
29

chapter 2 Database System Concepts
and Architecture

31

2.1 Data Models, Schemas, and Instances
32
2.2 Three-Schema Architecture and Data Independence
36
2.3 Database Languages and Interfaces
38
2.4 The Database System Environment
42
2.5 Centralized and Client/Server Architectures for DBMSs
46
2.6 Classification of Database Management Systems
51
2.7 Summary
54
Review Questions
55
Exercises
55
Selected Bibliography
56

xvii

xviii

Contents

2

■ part
Conceptual Data Modeling and Database Design ■
chapter 3 Data Modeling Using the Entity–Relationship (ER)
Model

59

3.1 Using High-Level Conceptual Data Models
for Database Design
60
3.2 A Sample Database Application
62
3.3 Entity Types, Entity Sets, Attributes, and Keys
63
3.4 Relationship Types, Relationship Sets, Roles, and Structural
Constraints
72
3.5 Weak Entity Types
79
3.6 Refining the ER Design for the COMPANY Database
80
3.7 ER Diagrams, Naming Conventions, and Design Issues
81
3.8 Example of Other Notation: UML Class Diagrams
85
3.9 Relationship Types of Degree Higher than Two
88
3.10 Another Example: A UNIVERSITY Database
92
3.11 Summary
94
Review Questions
96
Exercises
96
Laboratory Exercises
103
Selected Bibliography
104

chapter 4 The Enhanced Entity–Relationship (EER)
Model

107

4.1 Subclasses, Superclasses, and Inheritance
108
4.2 Specialization and Generalization
110
4.3 Constraints and Characteristics of Specialization and Generalization
Hierarchies
113
4.4 Modeling of UNION Types Using Categories
120
4.5 A Sample UNIVERSITY EER Schema, Design Choices, and Formal
Definitions
122
4.6 Example of Other Notation: Representing Specialization and
Generalization in UML Class Diagrams
127
4.7 Data Abstraction, Knowledge Representation, and Ontology
Concepts
128
4.8 Summary
135
Review Questions
135
Exercises
136
Laboratory Exercises
143
Selected Bibliography
146

Contents

3

■ part
The Relational Data Model and SQL ■
chapter 5 The Relational Data Model and Relational
Database Constraints

149

5.1 Relational Model Concepts
150
5.2 Relational Model Constraints and Relational Database Schemas
5.3 Update Operations, Transactions, and Dealing with Constraint
Violations
165
5.4 Summary
169
Review Questions
170
Exercises
170
Selected Bibliography
175

chapter 6 Basic SQL

157

177

6.1 SQL Data Definition and Data Types
179
6.2 Specifying Constraints in SQL
184
6.3 Basic Retrieval Queries in SQL
187
6.4 INSERT, DELETE, and UPDATE Statements in SQL
6.5 Additional Features of SQL
201
6.6 Summary
202
Review Questions
203
Exercises
203
Selected Bibliography
205

198

chapter 7 More SQL: Complex Queries, Triggers, Views,
and Schema Modification

207

7.1 More Complex SQL Retrieval Queries
207
7.2 Specifying Constraints as Assertions and Actions as Triggers
7.3 Views (Virtual Tables) in SQL
228
7.4 Schema Change Statements in SQL
232
7.5 Summary
234
Review Questions
236
Exercises
236
Selected Bibliography
238

chapter 8 The Relational Algebra and Relational Calculus
8.1 Unary Relational Operations: SELECT and PROJECT
8.2 Relational Algebra Operations from Set Theory
246

241

225

239

xix

xx

Contents

8.3 Binary Relational Operations: JOIN and DIVISION
8.4 Additional Relational Operations
259
8.5 Examples of Queries in Relational Algebra
265
8.6 The Tuple Relational Calculus
268
8.7 The Domain Relational Calculus
277
8.8 Summary
279
Review Questions
280
Exercises
281
Laboratory Exercises
286
Selected Bibliography
288

251

chapter 9 Relational Database Design by ER- and
EER-to-Relational Mapping

289

9.1 Relational Database Design Using ER-to-Relational Mapping
9.2 Mapping EER Model Constructs to Relations
298
9.3 Summary
303
Review Questions
303
Exercises
303
Laboratory Exercises
305
Selected Bibliography
306

290

4

■ part
Database Programming Techniques ■
chapter 10 Introduction to SQL Programming
Techniques

309

10.1 Overview of Database Programming Techniques and Issues
10.2 Embedded SQL, Dynamic SQL, and SQL J
314
10.3 Database Programming with Function Calls and Class
Libraries: SQL/CLI and JDBC
326
10.4 Database Stored Procedures and SQL/PSM
335
10.5 Comparing the Three Approaches
338
10.6 Summary
339
Review Questions
340
Exercises
340
Selected Bibliography
341

chapter 11 Web Database Programming Using PHP
11.1 A Simple PHP Example
344
11.2 Overview of Basic Features of PHP

346

310

343

Contents

11.3 Overview of PHP Database Programming
353
11.4 Brief Overview of Java Technologies for Database Web
Programming
358
11.5 Summary
358
Review Questions
359
Exercises
359
Selected Bibliography
359

■ part

5

Object, Object-Relational, and XML: Concepts, Models,
Languages, and Standards ■
chapter 12 Object and Object-Relational
Databases

363

12.1 Overview of Object Database Concepts
365
12.2 Object Database Extensions to SQL
379
12.3 The ODMG Object Model and the Object Definition Language
ODL
386
12.4 Object Database Conceptual Design
405
12.5 The Object Query Language OQL
408
12.6 Overview of the C++ Language Binding in the ODMG
Standard
417
12.7 Summary
418
Review Questions
420
Exercises
421
Selected Bibliography
422

chapter 13 XML: Extensible Markup Language

425

13.1 Structured, Semistructured, and Unstructured Data
426
13.2 XML Hierarchical (Tree) Data Model
430
13.3 XML Documents, DTD, and XML Schema
433
13.4 Storing and Extracting XML Documents
from Databases
442
13.5 XML Languages
443
13.6 Extracting XML Documents from Relational Databases
447
13.7 XML/SQL: SQL Functions for Creating XML Data
453
13.8 Summary
455
Review Questions
456
Exercises
456
Selected Bibliography
456

xxi

xxii

Contents

6

■ part
Database Design Theory and Normalization ■
chapter 14 Basics of Functional Dependencies
and Normalization for Relational
Databases
459
14.1 Informal Design Guidelines for Relation
Schemas
461
14.2 Functional Dependencies
471
14.3 Normal Forms Based on Primary Keys
474
14.4 General Definitions of Second and Third Normal
Forms
483
14.5 Boyce-Codd Normal Form
487
14.6 Multivalued Dependency and Fourth
Normal Form
491
14.7 Join Dependencies and Fifth Normal Form
494
14.8 Summary
495
Review Questions
496
Exercises
497
Laboratory Exercises
501
Selected Bibliography
502

chapter 15 Relational Database Design Algorithms
and Further Dependencies

503

15.1 Further Topics in Functional Dependencies: Inference Rules,
Equivalence, and Minimal Cover
505
15.2 Properties of Relational Decompositions
513
15.3 Algorithms for Relational Database Schema
Design
519
15.4 About Nulls, Dangling Tuples, and Alternative Relational
Designs
523
15.5 Further Discussion of Multivalued Dependencies
and 4NF
527
15.6 Other Dependencies and Normal Forms
530
15.7 Summary
533
Review Questions
534
Exercises
535
Laboratory Exercises
536
Selected Bibliography
537

Contents

7

■ part
File Structures, Hashing, Indexing, and Physical
Database Design ■
chapter 16 Disk Storage, Basic File Structures,
Hashing, and Modern Storage
Architectures
541
16.1 Introduction
542
16.2 Secondary Storage Devices
547
16.3 Buffering of Blocks
556
16.4 Placing File Records on Disk
560
16.5 Operations on Files
564
16.6 Files of Unordered Records (Heap Files)
16.7 Files of Ordered Records (Sorted Files)
16.8 Hashing Techniques
572
16.9 Other Primary File Organizations
582
16.10 Parallelizing Disk Access Using RAID
Technology
584
16.11 Modern Storage Architectures
588
16.12 Summary
592
Review Questions
593
Exercises
595
Selected Bibliography
598

567
568

chapter 17 Indexing Structures for Files and Physical
Database Design

601

17.1 Types of Single-Level Ordered Indexes
602
17.2 Multilevel Indexes
613
17.3 Dynamic Multilevel Indexes Using B-Trees
and B+-Trees
617
17.4 Indexes on Multiple Keys
631
17.5 Other Types of Indexes
633
17.6 Some General Issues Concerning Indexing
638
17.7 Physical Database Design in Relational
Databases
643
17.8 Summary
646
Review Questions
647
Exercises
648
Selected Bibliography
650

xxiii

xxiv

Contents

8

■ part
Query Processing and Optimization ■
chapter 18 Strategies for Query Processing
18.1 Translating SQL Queries into Relational Algebra
and Other Operators
657
18.2 Algorithms for External Sorting
660
18.3 Algorithms for SELECT Operation
663
18.4 Implementing the JOIN Operation
668
18.5 Algorithms for PROJECT and Set Operations
676
18.6 Implementing Aggregate Operations and Different
Types of JOINs
678
18.7 Combining Operations Using Pipelining
681
18.8 Parallel Algorithms for Query Processing
683
18.9 Summary
688
Review Questions
688
Exercises
689
Selected Bibliography
689

chapter 19 Query Optimization

691

19.1 Query Trees and Heuristics for Query
Optimization
692
19.2 Choice of Query Execution Plans
701
19.3 Use of Selectivities in Cost-Based
Optimization
710
19.4 Cost Functions for SELECT Operation
714
19.5 Cost Functions for the JOIN Operation
717
19.6 Example to Illustrate Cost-Based Query
Optimization
726
19.7 Additional Issues Related to Query
Optimization
728
19.8 An Example of Query Optimization in Data
Warehouses
731
19.9 Overview of Query Optimization in Oracle
733
19.10 Semantic Query Optimization
737
19.11 Summary
738
Review Questions
739
Exercises
740
Selected Bibliography
740

655

Contents

9

■ part
Transaction Processing, Concurrency Control,
and Recovery ■
chapter 20 Introduction to Transaction Processing
Concepts and Theory

745

20.1 Introduction to Transaction Processing
746
20.2 Transaction and System Concepts
753
20.3 Desirable Properties of Transactions
757
20.4 Characterizing Schedules Based on Recoverability
20.5 Characterizing Schedules Based on Serializability
20.6 Transaction Support in SQL
773
20.7 Summary
776
Review Questions
777
Exercises
777
Selected Bibliography
779

chapter 21 Concurrency Control Techniques

759
763

781

21.1 Two-Phase Locking Techniques for Concurrency
Control
782
21.2 Concurrency Control Based on Timestamp Ordering
792
21.3 Multiversion Concurrency Control Techniques
795
21.4 Validation (Optimistic) Techniques and Snapshot Isolation
Concurrency Control
798
21.5 Granularity of Data Items and Multiple Granularity
Locking
800
21.6 Using Locks for Concurrency Control in Indexes
805
21.7 Other Concurrency Control Issues
806
21.8 Summary
807
Review Questions
808
Exercises
809
Selected Bibliography
810

chapter 22 Database Recovery Techniques

813

22.1 Recovery Concepts
814
22.2 NO-UNDO/REDO Recovery Based on Deferred
Update
821
22.3 Recovery Techniques Based on Immediate Update

823

xxv

xxvi

Contents

22.4 Shadow Paging
826
22.5 The ARIES Recovery Algorithm
827
22.6 Recovery in Multidatabase Systems
831
22.7 Database Backup and Recovery from Catastrophic Failures
22.8 Summary
833
Review Questions
834
Exercises
835
Selected Bibliography
838

832

10

■ part
Distributed Databases, NOSQL Systems,
and Big Data ■
chapter 23 Distributed Database Concepts

841

23.1 Distributed Database Concepts
842
23.2 Data Fragmentation, Replication, and Allocation Techniques for
Distributed Database Design
847
23.3 Overview of Concurrency Control and Recovery in Distributed
Databases
854
23.4 Overview of Transaction Management in Distributed Databases
857
23.5 Query Processing and Optimization in Distributed Databases
859
23.6 Types of Distributed Database Systems
865
23.7 Distributed Database Architectures
868
23.8 Distributed Catalog Management
875
23.9 Summary
876
Review Questions
877
Exercises
878
Selected Bibliography
880

chapter 24 NOSQL Databases and Big Data Storage
Systems

883

24.1 Introduction to NOSQL Systems
884
24.2 The CAP Theorem
888
24.3 Document-Based NOSQL Systems and MongoDB
24.4 NOSQL Key-Value Stores
895
24.5 Column-Based or Wide Column NOSQL Systems
24.6 NOSQL Graph Databases and Neo4j
903
24.7 Summary
909
Review Questions
909
Selected Bibliography
910

890
900

Contents

chapter 25 Big Data Technologies Based on MapReduce
and Hadoop

911

25.1 What Is Big Data?
914
25.2 Introduction to MapReduce and Hadoop
25.3 Hadoop Distributed File System (HDFS)
25.4 MapReduce: Additional Details
926
25.5 Hadoop v2 alias YARN
936
25.6 General Discussion
944
25.7 Summary
953
Review Questions
954
Selected Bibliography
956

916
921

11

■ part
Advanced Database Models, Systems, and
Applications ■
chapter 26 Enhanced Data Models: Introduction to Active,
Temporal, Spatial, Multimedia, and Deductive
Databases 961
26.1 Active Database Concepts and Triggers
963
26.2 Temporal Database Concepts
974
26.3 Spatial Database Concepts
987
26.4 Multimedia Database Concepts
994
26.5 Introduction to Deductive Databases
999
26.6 Summary
1012
Review Questions
1014
Exercises
1015
Selected Bibliography
1018

chapter 27 Introduction to Information Retrieval
and Web Search

1021

27.1 Information Retrieval (IR) Concepts
1022
27.2 Retrieval Models
1029
27.3 Types of Queries in IR Systems
1035
27.4 Text Preprocessing
1037
27.5 Inverted Indexing
1040
27.6 Evaluation Measures of Search Relevance
1044
27.7 Web Search and Analysis
1047

xxvii

xxviii

Contents

27.8 Trends in Information Retrieval
27.9 Summary
1063
Review Questions
1064
Selected Bibliography
1066

1057

chapter 28 Data Mining Concepts

1069

28.1 Overview of Data Mining Technology
1070
28.2 Association Rules
1073
28.3 Classification
1085
28.4 Clustering
1088
28.5 Approaches to Other Data Mining Problems
1091
28.6 Applications of Data Mining
1094
28.7 Commercial Data Mining Tools
1094
28.8 Summary
1097
Review Questions
1097
Exercises
1098
Selected Bibliography
1099

chapter 29 Overview of Data Warehousing
and OLAP

1101

29.1 Introduction, Definitions, and Terminology
1102
29.2 Characteristics of Data Warehouses
1103
29.3 Data Modeling for Data Warehouses
1105
29.4 Building a Data Warehouse
1111
29.5 Typical Functionality of a Data Warehouse
1114
29.6 Data Warehouse versus Views
1115
29.7 Difficulties of Implementing Data Warehouses
1116
29.8 Summary
1117
Review Questions
1117
Selected Bibliography
1118

12

■ part
Additional Database Topics: Security ■
chapter 30 Database Security

1121

30.1 Introduction to Database Security Issues
1122
30.2 Discretionary Access Control Based on Granting and Revoking
Privileges
1129
30.3 Mandatory Access Control and Role-Based Access Control for
Multilevel Security
1134

Contents

30.4 SQL Injection
1143
30.5 Introduction to Statistical Database Security
1146
30.6 Introduction to Flow Control
1147
30.7 Encryption and Public Key Infrastructures
1149
30.8 Privacy Issues and Preservation
1153
30.9 Challenges to Maintaining Database Security
1154
30.10 Oracle Label-Based Security
1155
30.11 Summary
1158
Review Questions
1159
Exercises
1160
Selected Bibliography
1161

appendix A Alternative Diagrammatic Notations for ER
Models

1163

appendix B Parameters of Disks

1167

appendix C Overview of the QBE Language

1171

C.1 Basic Retrievals in QBE
1171
C.2 Grouping, Aggregation, and Database Modification in QBE

appendix

D Overview of the Hierarchical Data Model
(located on the Companion Website at

appendix

E Overview of the Network Data Model
(located on the Companion Website at

Selected Bibliography
Index

1215

1179

1175

xxix

About the Authors
Ramez Elmasri is a professor and the associate chairperson of the Department of
Computer Science and Engineering at the University of Texas at Arlington. He has
over 140 refereed research publications, and has supervised 16 PhD students and
over 100 MS students. His research has covered many areas of database management and big data, including conceptual modeling and data integration, query
languages and indexing techniques, temporal and spatio-temporal databases, bioinformatics databases, data collection from sensor networks, and mining/analysis
of spatial and spatio-temporal data. He has worked as a consultant to various companies, including Digital, Honeywell, Hewlett Packard, and Action Technologies,
as well as consulting with law firms on patents. He was the Program Chair of the
1993 International Conference on Conceptual Modeling (ER conference) and program vice-chair of the 1994 IEEE International Conference on Data Engineering.
He has served on the ER conference steering committee and has been on the program committees of many conferences. He has given several tutorials at the VLDB,
ICDE, and ER conferences. He also co-authored the book “Operating Systems: A
Spiral Approach” (McGraw-Hill, 2009) with Gil Carrick and David Levine. Elmasri
is a recipient of the UTA College of Engineering Outstanding Teaching Award in
1999. He holds a BS degree in Engineering from Alexandria University, and MS
and PhD degrees in Computer Science from Stanford University.
Shamkant B. Navathe is a professor and the founder of the database research group
at the College of Computing, Georgia Institute of Technology, Atlanta. He has
worked with IBM and Siemens in their research divisions and has been a consultant
to various companies including Digital, Computer Corporation of America,
Hewlett Packard, Equifax, and Persistent Systems. He was the General Co-chairman
of the 1996 International VLDB (Very Large Data Base) conference in Bombay,
India. He was also program co-chair of ACM SIGMOD 1985 International Conference and General Co-chair of the IFIP WG 2.6 Data Semantics Workshop in 1995.
He has served on the VLDB foundation and has been on the steering committees of
several conferences. He has been an associate editor of a number of journals
including ACM Computing Surveys, and IEEE Transactions on Knowledge and
Data Engineering. He also co-authored the book “Conceptual Design: An Entity
Relationship Approach” (Addison Wesley, 1992) with Carlo Batini and Stefano
Ceri. Navathe is a fellow of the Association for Computing Machinery (ACM) and
recipient of the IEEE TCDE Computer Science, Engineering and Education Impact
award in 2015. Navathe holds a PhD from the University of Michigan and has over
150 refereed publications in journals and conferences.

xxx

part

1

Introduction
to Databases

This page intentionally left blank

chapter

1

Databases and
Database Users

D

atabases and database systems are an essential
component of life in modern society: most of us
encounter several activities every day that involve some interaction with a database.
For example, if we go to the bank to deposit or withdraw funds, if we make a hotel
or airline reservation, if we access a computerized library catalog to search for a
bibliographic item, or if we purchase something online—such as a book, toy, or
computer—chances are that our activities will involve someone or some computer
program accessing a database. Even purchasing items at a supermarket often automatically updates the database that holds the inventory of grocery items.
These interactions are examples of what we may call traditional database
applications, in which most of the information that is stored and accessed is either
textual or numeric. In the past few years, advances in technology have led to exciting
new applications of database systems. The proliferation of social media Web sites,
such as Facebook, Twitter, and Flickr, among many others, has required the creation of huge databases that store nontraditional data, such as posts, tweets,
images, and video clips. New types of database systems, often referred to as big data
storage systems, or NOSQL systems, have been created to manage data for social
media applications. These types of systems are also used by companies such as
Google, Amazon, and Yahoo, to manage the data required in their Web search
engines, as well as to provide cloud storage, whereby users are provided with storage capabilities on the Web for managing all types of data including documents,
programs, images, videos and emails. We will give an overview of these new types
of database systems in Chapter 24.
We now mention some other applications of databases. The wide availability of
photo and video technology on cellphones and other devices has made it possible to
3

4

Chapter 1 Databases and Database Users

store images, audio clips, and video streams digitally. These types of files are becoming an important component of multimedia databases. Geographic information
systems (GISs) can store and analyze maps, weather data, and satellite images.
Data warehouses and online analytical processing (OLAP) systems are used in
many companies to extract and analyze useful business information from very large
databases to support decision making. Real-time and active database technology
is used to control industrial and manufacturing processes. And database search
techniques are being applied to the World Wide Web to improve the search for
information that is needed by users browsing the Internet.
To understand the fundamentals of database technology, however, we must start
from the basics of traditional database applications. In Section 1.1 we start by defining a database, and then we explain other basic terms. In Section 1.2, we provide a
simple UNIVERSITY database example to illustrate our discussion. Section 1.3
describes some of the main characteristics of database systems, and Sections 1.4
and 1.5 categorize the types of personnel whose jobs involve using and interacting
with database systems. Sections 1.6, 1.7, and 1.8 offer a more thorough discussion
of the various capabilities provided by database systems and discuss some typical
database applications. Section 1.9 summarizes the chapter.
The reader who desires a quick introduction to database systems can study
Sections 1.1 through 1.5, then skip or browse through Sections 1.6 through 1.8 and
go on to Chapter 2.

1.1 Introduction
Databases and database technology have had a major impact on the growing use of
computers. It is fair to say that databases play a critical role in almost all areas where
computers are used, including business, electronic commerce, social media, engineering, medicine, genetics, law, education, and library science. The word database
is so commonly used that we must begin by defining what a database is. Our initial
definition is quite general.
A database is a collection of related data.1 By data, we mean known facts that can
be recorded and that have implicit meaning. For example, consider the names,
telephone numbers, and addresses of the people you know. Nowadays, this data is
typically stored in mobile phones, which have their own simple database software.
This data can also be recorded in an indexed address book or stored on a hard
drive, using a personal computer and software such as Microsoft Access or Excel.
This collection of related data with an implicit meaning is a database.
The preceding definition of database is quite general; for example, we may consider
the collection of words that make up this page of text to be related data and hence to
1

We will use the word data as both singular and plural, as is common in database literature; the context
will determine whether it is singular or plural. In standard English, data is used for plural and datum for
singular.

1.1 Introduction

constitute a database. However, the common use of the term database is usually
more restricted. A database has the following implicit properties:
■

A database represents some aspect of the real world, sometimes called the
miniworld or the universe of discourse (UoD). Changes to the miniworld
are reflected in the database.
■ A database is a logically coherent collection of data with some inherent
meaning. A random assortment of data cannot correctly be referred to as a
database.
■ A database is designed, built, and populated with data for a specific purpose.
It has an intended group of users and some preconceived applications in
which these users are interested.
In other words, a database has some source from which data is derived, some degree
of interaction with events in the real world, and an audience that is actively interested in its contents. The end users of a database may perform business transactions
(for example, a customer buys a camera) or events may happen (for example, an
employee has a baby) that cause the information in the database to change. In order
for a database to be accurate and reliable at all times, it must be a true reflection of
the miniworld that it represents; therefore, changes must be reflected in the database as soon as possible.
A database can be of any size and complexity. For example, the list of names and
addresses referred to earlier may consist of only a few hundred records, each with a
simple structure. On the other hand, the computerized catalog of a large library
may contain half a million entries organized under different categories—by primary author’s last name, by subject, by book title—with each category organized
alphabetically. A database of even greater size and complexity would be maintained
by a social media company such as Facebook, which has more than a billion users.
The database has to maintain information on which users are related to one another
as friends, the postings of each user, which users are allowed to see each posting,
and a vast amount of other types of information needed for the correct operation of
their Web site. For such Web sites, a large number of databases are needed to keep
track of the constantly changing information required by the social media Web site.
An example of a large commercial database is Amazon.com. It contains data for
over 60 million active users, and millions of books, CDs, videos, DVDs, games,
electronics, apparel, and other items. The database occupies over 42 terabytes
(a terabyte is 1012 bytes worth of storage) and is stored on hundreds of computers
(called servers). Millions of visitors access Amazon.com each day and use the
database to make purchases. The database is continually updated as new books
and other items are added to the inventory, and stock quantities are updated as
purchases are transacted.
A database may be generated and maintained manually or it may be computerized. For example, a library card catalog is a database that may be created and
maintained manually. A computerized database may be created and maintained
either by a group of application programs written specifically for that task or by a

5

6

Chapter 1 Databases and Database Users

database management system. Of course, we are only concerned with computerized databases in this text.
A database management system (DBMS) is a computerized system that enables
users to create and maintain a database. The DBMS is a general-purpose software
system that facilitates the processes of defining, constructing, manipulating, and
sharing databases among various users and applications. Defining a database
involves specifying the data types, structures, and constraints of the data to be
stored in the database. The database definition or descriptive information is also
stored by the DBMS in the form of a database catalog or dictionary; it is called
meta-data. Constructing the database is the process of storing the data on some
storage medium that is controlled by the DBMS. Manipulating a database includes
functions such as querying the database to retrieve specific data, updating the database to reflect changes in the miniworld, and generating reports from the data.
Sharing a database allows multiple users and programs to access the database
simultaneously.
An application program accesses the database by sending queries or requests for
data to the DBMS. A query2 typically causes some data to be retrieved; a transaction
may cause some data to be read and some data to be written into the database.
Other important functions provided by the DBMS include protecting the database
and maintaining it over a long period of time. Protection includes system protection against hardware or software malfunction (or crashes) and security protection
against unauthorized or malicious access. A typical large database may have a life
cycle of many years, so the DBMS must be able to maintain the database system by
allowing the system to evolve as requirements change over time.
It is not absolutely necessary to use general-purpose DBMS software to implement
a computerized database. It is possible to write a customized set of programs to create and maintain the database, in effect creating a special-purpose DBMS software
for a specific application, such as airlines reservations. In either case—whether we
use a general-purpose DBMS or not—a considerable amount of complex software
is deployed. In fact, most DBMSs are very complex software systems.
To complete our initial definitions, we will call the database and DBMS software
together a database system. Figure 1.1 illustrates some of the concepts we have
discussed so far.

1.2 An Example
Let us consider a simple example that most readers may be familiar with: a
UNIVERSITY database for maintaining information concerning students, courses,
and grades in a university environment. Figure 1.2 shows the database structure
and a few sample data records. The database is organized as five files, each of which
2

The term query, originally meaning a question or an inquiry, is sometimes loosely used for all types of
interactions with databases, including modifying the data.

1.2 An Example

Users/Programmers
Database
System
Application Programs/Queries

DBMS
Software

Software to Process
Queries/Programs

Software to Access
Stored Data

Stored Database
Definition
(Meta-Data)

Stored Database
Figure 1.1
A simplified database
system environment.

stores data records of the same type.3 The STUDENT file stores data on each student, the COURSE file stores data on each course, the SECTION file stores data on
each section of a course, the GRADE_REPORT file stores the grades that students
receive in the various sections they have completed, and the PREREQUISITE file
stores the prerequisites of each course.
To define this database, we must specify the structure of the records of each file by
specifying the different types of data elements to be stored in each record. In
Figure 1.2, each STUDENT record includes data to represent the student’s Name,
Student_number, Class (such as freshman or ‘1’, sophomore or ‘2’, and so forth),
and Major (such as mathematics or ‘MATH’ and computer science or ‘CS’); each
COURSE record includes data to represent the Course_name, Course_number,
Credit_hours, and Department (the department that offers the course), and so
on. We must also specify a data type for each data element within a record. For
example, we can specify that Name of STUDENT is a string of alphabetic characters,
Student_number of STUDENT is an integer, and Grade of GRADE_REPORT is a
3

We use the term file informally here. At a conceptual level, a file is a collection of records that may or
may not be ordered.

7

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Chapter 1 Databases and Database Users

STUDENT
Name

Student_number

Class

Major

Smith

17

1

CS

Brown

8

2

CS

COURSE
Course_name

Course_number

Credit_hours

Department

Intro to Computer Science

CS1310

4

CS

Data Structures

CS3320

4

CS

Discrete Mathematics

MATH2410

3

MATH

Database

CS3380

3

CS

SECTION
Section_identifier

Course_number

Semester

Year

Instructor

85

MATH2410

Fall

07

King

92

CS1310

Fall

07

Anderson

102

CS3320

Spring

08

Knuth

112

MATH2410

Fall

08

Chang

119

CS1310

Fall

08

Anderson

135

CS3380

Fall

08

Stone

GRADE_REPORT
Student_number

Section_identifier

Grade

17

112

B

17

119

C

8

85

A

8

92

A

8

102

B

8

135

A

PREREQUISITE
Course_number
Figure 1.2
A database that stores
student and course
information.

Prerequisite_number

CS3380

CS3320

CS3380

MATH2410

CS3320

CS1310

1.2 An Example

single character from the set {‘A’, ‘B’, ‘C’, ‘D’, ‘F’, ‘I’}. We may also use a coding
scheme to represent the values of a data item. For example, in Figure 1.2 we represent the Class of a STUDENT as 1 for freshman, 2 for sophomore, 3 for junior,
4 for senior, and 5 for graduate student.
To construct the UNIVERSITY database, we store data to represent each student,
course, section, grade report, and prerequisite as a record in the appropriate file.
Notice that records in the various files may be related. For example, the record for
Smith in the STUDENT file is related to two records in the GRADE_REPORT file that
specify Smith’s grades in two sections. Similarly, each record in the PREREQUISITE
file relates two course records: one representing the course and the other representing the prerequisite. Most medium-size and large databases include many types of
records and have many relationships among the records.
Database manipulation involves querying and updating. Examples of queries are as
follows:
■

Retrieve the transcript—a list of all courses and grades—of ‘Smith’
List the names of students who took the section of the ‘Database’ course
offered in fall 2008 and their grades in that section
■ List the prerequisites of the ‘Database’ course
■

Examples of updates include the following:
■

Change the class of ‘Smith’ to sophomore
■ Create a new section for the ‘Database’ course for this semester
■ Enter a grade of ‘A’ for ‘Smith’ in the ‘Database’ section of last semester
These informal queries and updates must be specified precisely in the query language of the DBMS before they can be processed.
At this stage, it is useful to describe the database as part of a larger undertaking
known as an information system within an organization. The Information Technology (IT) department within an organization designs and maintains an information system consisting of various computers, storage systems, application software,
and databases. Design of a new application for an existing database or design of a
brand new database starts off with a phase called requirements specification and
analysis. These requirements are documented in detail and transformed into a
conceptual design that can be represented and manipulated using some computerized tools so that it can be easily maintained, modified, and transformed into a
database implementation. (We will introduce a model called the Entity-Relationship model in Chapter 3 that is used for this purpose.) The design is then translated
to a logical design that can be expressed in a data model implemented in a commercial DBMS. (Various types of DBMSs are discussed throughout the text, with an
emphasis on relational DBMSs in Chapters 5 through 9.)
The final stage is physical design, during which further specifications are provided for
storing and accessing the database. The database design is implemented, populated
with actual data, and continuously maintained to reflect the state of the miniworld.

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Chapter 1 Databases and Database Users

1.3 Characteristics of the Database Approach
A number of characteristics distinguish the database approach from the much
older approach of writing customized programs to access data stored in files. In
traditional file processing, each user defines and implements the files needed for a
specific software application as part of programming the application. For example,
one user, the grade reporting office, may keep files on students and their grades.
Programs to print a student’s transcript and to enter new grades are implemented
as part of the application. A second user, the accounting office, may keep track of
students’ fees and their payments. Although both users are interested in data about
students, each user maintains separate files—and programs to manipulate these
files—because each requires some data not available from the other user’s files.
This redundancy in defining and storing data results in wasted storage space and
in redundant efforts to maintain common up-to-date data.
In the database approach, a single repository maintains data that is defined once
and then accessed by various users repeatedly through queries, transactions, and
application programs. The main characteristics of the database approach versus the
file-processing approach are the following:
■

Self-describing nature of a database system
■ Insulation between programs and data, and data abstraction
■ Support of multiple views of the data
■ Sharing of data and multiuser transaction processing
We describe each of these characteristics in a separate section. We will discuss additional characteristics of database systems in Sections 1.6 through 1.8.

1.3.1 Self-Describing Nature of a Database System
A fundamental characteristic of the database approach is that the database system
contains not only the database itself but also a complete definition or description of
the database structure and constraints. This definition is stored in the DBMS catalog, which contains information such as the structure of each file, the type and storage format of each data item, and various constraints on the data. The information
stored in the catalog is called meta-data, and it describes the structure of the primary database (Figure 1.1). It is important to note that some newer types of database systems, known as NOSQL systems, do not require meta-data. Rather the data
is stored as self-describing data that includes the data item names and data values
together in one structure (see Chapter 24).
The catalog is used by the DBMS software and also by database users who need
information about the database structure. A general-purpose DBMS software
package is not written for a specific database application. Therefore, it must refer
to the catalog to know the structure of the files in a specific database, such as the
type and format of data it will access. The DBMS software must work equally well
with any number of database applications—for example, a university database, a

1.3 Characteristics of the Database Approach

11

banking database, or a company database—as long as the database definition is
stored in the catalog.
In traditional file processing, data definition is typically part of the application programs themselves. Hence, these programs are constrained to work with only one
specific database, whose structure is declared in the application programs. For
example, an application program written in C++ may have struct or class declarations. Whereas file-processing software can access only specific databases, DBMS
software can access diverse databases by extracting the database definitions from
the catalog and using these definitions.
For the example shown in Figure 1.2, the DBMS catalog will store the definitions of
all the files shown. Figure 1.3 shows some entries in a database catalog. Whenever a
request is made to access, say, the Name of a STUDENT record, the DBMS software
refers to the catalog to determine the structure of the STUDENT file and the position
and size of the Name data item within a STUDENT record. By contrast, in a typical
file-processing application, the file structure and, in the extreme case, the exact
location of Name within a STUDENT record are already coded within each program
that accesses this data item.

Figure 1.3
An example of a
database catalog for
the database in
Figure 1.2.

RELATIONS
Relation_name

No_of_columns

STUDENT

4

COURSE

4

SECTION

5

GRADE_REPORT

3

PREREQUISITE

2

COLUMNS
Column_name

Data_type

Belongs_to_relation

Name

Character (30)

STUDENT

Student_number

Character (4)

STUDENT

Class

Integer (1)

STUDENT

Major

Major_type

STUDENT

Course_name

Character (10)

COURSE

Course_number

XXXXNNNN

COURSE

….

….

…..

….

….

…..

….

….

…..

Prerequisite_number

XXXXNNNN

PREREQUISITE

Note: Major_type is defined as an enumerated type with all known majors.
XXXXNNNN is used to define a type with four alphabetic characters followed by four numeric digits.

12

Chapter 1 Databases and Database Users

1.3.2 Insulation between Programs and Data,
and Data Abstraction
In traditional file processing, the structure of data files is embedded in the application programs, so any changes to the structure of a file may require changing all
programs that access that file. By contrast, DBMS access programs do not require
such changes in most cases. The structure of data files is stored in the DBMS catalog separately from the access programs. We call this property program-data
independence.
For example, a file access program may be written in such a way that it can access
only STUDENT records of the structure shown in Figure 1.4. If we want to add
another piece of data to each STUDENT record, say the Birth_date, such a program
will no longer work and must be changed. By contrast, in a DBMS environment, we
only need to change the description of STUDENT records in the catalog (Figure 1.3)
to reflect the inclusion of the new data item Birth_date; no programs are changed.
The next time a DBMS program refers to the catalog, the new structure of
STUDENT records will be accessed and used.
In some types of database systems, such as object-oriented and object-relational
systems (see Chapter 12), users can define operations on data as part of the database
definitions. An operation (also called a function or method) is specified in two
parts. The interface (or signature) of an operation includes the operation name and
the data types of its arguments (or parameters). The implementation (or method) of
the operation is specified separately and can be changed without affecting the interface. User application programs can operate on the data by invoking these operations through their names and arguments, regardless of how the operations are
implemented. This may be termed program-operation independence.
The characteristic that allows program-data independence and program-operation
independence is called data abstraction. A DBMS provides users with a conceptual
representation of data that does not include many of the details of how the data is
stored or how the operations are implemented. Informally, a data model is a type of
data abstraction that is used to provide this conceptual representation. The data
model uses logical concepts, such as objects, their properties, and their interrelationships, that may be easier for most users to understand than computer storage
concepts. Hence, the data model hides storage and implementation details that are
not of interest to most database users.
Looking at the example in Figures 1.2 and 1.3, the internal implementation of the
STUDENT file may be defined by its record length—the number of characters
(bytes) in each record—and each data item may be specified by its starting byte
within a record and its length in bytes. The STUDENT record would thus be represented as shown in Figure 1.4. But a typical database user is not concerned with the
location of each data item within a record or its length; rather, the user is concerned
that when a reference is made to Name of STUDENT, the correct value is returned.
A conceptual representation of the STUDENT records is shown in Figure 1.2. Many
other details of file storage organization—such as the access paths specified on a

1.3 Characteristics of the Database Approach

Starting Position in Record

Length in Characters (bytes)

Name

Data Item Name

1

30

Student_number

31

4

Class

35

1

Major

36

4

13

Figure 1.4
Internal storage format
for a STUDENT record,
based on the database
catalog in Figure 1.3.

file—can be hidden from database users by the DBMS; we discuss storage details in
Chapters 16 and 17.
In the database approach, the detailed structure and organization of each file are
stored in the catalog. Database users and application programs refer to the conceptual representation of the files, and the DBMS extracts the details of file storage
from the catalog when these are needed by the DBMS file access modules. Many
data models can be used to provide this data abstraction to database users. A major
part of this text is devoted to presenting various data models and the concepts they
use to abstract the representation of data.
In object-oriented and object-relational databases, the abstraction process includes
not only the data structure but also the operations on the data. These operations
provide an abstraction of miniworld activities commonly understood by the users.
For example, an operation CALCULATE_GPA can be applied to a STUDENT object
to calculate the grade point average. Such operations can be invoked by the user
queries or application programs without having to know the details of how the
operations are implemented.

1.3.3 Support of Multiple Views of the Data
A database typically has many types of users, each of whom may require a different
perspective or view of the database. A view may be a subset of the database or it may
contain virtual data that is derived from the database files but is not explicitly stored.
Some users may not need to be aware of whether the data they refer to is stored or
derived. A multiuser DBMS whose users have a variety of distinct applications must
provide facilities for defining multiple views. For example, one user of the database
of Figure 1.2 may be interested only in accessing and printing the transcript of each
student; the view for this user is shown in Figure 1.5(a). A second user, who is interested only in checking that students have taken all the prerequisites of each course
for which the student registers, may require the view shown in Figure 1.5(b).

1.3.4 Sharing of Data and Multiuser Transaction Processing
A multiuser DBMS, as its name implies, must allow multiple users to access the
database at the same time. This is essential if data for multiple applications is to be
integrated and maintained in a single database. The DBMS must include concurrency
control software to ensure that several users trying to update the same data

14

Chapter 1 Databases and Database Users

TRANSCRIPT
Student_name
Smith

Brown
(a)

Student_transcript
Course_number

Grade

Semester

Year

Section_id

CS1310

C

Fall

08

119

MATH2410

B

Fall

08

112

MATH2410

A

Fall

07

85

CS1310

A

Fall

07

92

CS3320

B

Spring

08

102

CS3380

A

Fall

08

135

COURSE_PREREQUISITES
Course_name

(b)

Course_number

Database

CS3380

Data Structures

CS3320

Prerequisites
CS3320
MATH2410
CS1310

Figure 1.5
Two views derived from the database in Figure 1.2. (a) The TRANSCRIPT view.
(b) The COURSE_PREREQUISITES view.

do so in a controlled manner so that the result of the updates is correct. For example, when several reservation agents try to assign a seat on an airline flight, the
DBMS should ensure that each seat can be accessed by only one agent at a time for
assignment to a passenger. These types of applications are generally called online
transaction processing (OLTP) applications. A fundamental role of multiuser
DBMS software is to ensure that concurrent transactions operate correctly and
efficiently.
The concept of a transaction has become central to many database applications. A
transaction is an executing program or process that includes one or more database
accesses, such as reading or updating of database records. Each transaction is supposed to execute a logically correct database access if executed in its entirety without interference from other transactions. The DBMS must enforce several
transaction properties. The isolation property ensures that each transaction
appears to execute in isolation from other transactions, even though hundreds of
transactions may be executing concurrently. The atomicity property ensures that
either all the database operations in a transaction are executed or none are. We discuss transactions in detail in Part 9.
The preceding characteristics are important in distinguishing a DBMS from traditional file-processing software. In Section 1.6 we discuss additional features that
characterize a DBMS. First, however, we categorize the different types of people
who work in a database system environment.

1.4 Actors on the Scene

1.4 Actors on the Scene
For a small personal database, such as the list of addresses discussed in Section 1.1,
one person typically defines, constructs, and manipulates the database, and there is
no sharing. However, in large organizations, many people are involved in the
design, use, and maintenance of a large database with hundreds or thousands of
users. In this section we identify the people whose jobs involve the day-to-day use
of a large database; we call them the actors on the scene. In Section 1.5 we consider
people who may be called workers behind the scene—those who work to maintain
the database system environment but who are not actively interested in the database contents as part of their daily job.

1.4.1 Database Administrators
In any organization where many people use the same resources, there is a need for
a chief administrator to oversee and manage these resources. In a database environment, the primary resource is the database itself, and the secondary resource is the
DBMS and related software. Administering these resources is the responsibility of
the database administrator (DBA). The DBA is responsible for authorizing access
to the database, coordinating and monitoring its use, and acquiring software and
hardware resources as needed. The DBA is accountable for problems such as security breaches and poor system response time. In large organizations, the DBA is
assisted by a staff that carries out these functions.

1.4.2 Database Designers
Database designers are responsible for identifying the data to be stored in the database and for choosing appropriate structures to represent and store this data. These
tasks are mostly undertaken before the database is actually implemented and populated with data. It is the responsibility of database designers to communicate with
all prospective database users in order to understand their requirements and to create a design that meets these requirements. In many cases, the designers are on the
staff of the DBA and may be assigned other staff responsibilities after the database
design is completed. Database designers typically interact with each potential group
of users and develop views of the database that meet the data and processing
requirements of these groups. Each view is then analyzed and integrated with the
views of other user groups. The final database design must be capable of supporting
the requirements of all user groups.

1.4.3 End Users
End users are the people whose jobs require access to the database for querying,
updating, and generating reports; the database primarily exists for their use. There
are several categories of end users:
■

Casual end users occasionally access the database, but they may need different information each time. They use a sophisticated database query interface

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Chapter 1 Databases and Database Users

to specify their requests and are typically middle- or high-level managers or
other occasional browsers.
■ Naive or parametric end users make up a sizable portion of database
end users. Their main job function revolves around constantly querying
and updating the database, using standard types of queries and updates—
called canned transactions—that have been carefully programmed and
tested. Many of these tasks are now available as mobile apps for use with
mobile devices. The tasks that such users perform are varied. A few
examples are:
Bank customers and tellers check account balances and post withdrawals
and deposits.
Reservation agents or customers for airlines, hotels, and car rental companies check availability for a given request and make reservations.
Employees at receiving stations for shipping companies enter package
identifications via bar codes and descriptive information through buttons
to update a central database of received and in-transit packages.
Social media users post and read items on social media Web sites.
■ Sophisticated end users include engineers, scientists, business analysts, and
others who thoroughly familiarize themselves with the facilities of the DBMS
in order to implement their own applications to meet their complex requirements.
■ Standalone users maintain personal databases by using ready-made program packages that provide easy-to-use menu-based or graphics-based
interfaces. An example is the user of a financial software package that stores
a variety of personal financial data.
A typical DBMS provides multiple facilities to access a database. Naive end users
need to learn very little about the facilities provided by the DBMS; they simply have
to understand the user interfaces of the mobile apps or standard transactions
designed and implemented for their use. Casual users learn only a few facilities that
they may use repeatedly. Sophisticated users try to learn most of the DBMS facilities
in order to achieve their complex requirements. Standalone users typically become
very proficient in using a specific software package.

1.4.4 System Analysts and Application Programmers
(Software Engineers)
System analysts determine the requirements of end users, especially naive and
parametric end users, and develop specifications for standard canned transactions
that meet these requirements. Application programmers implement these specifications as programs; then they test, debug, document, and maintain these canned
transactions. Such analysts and programmers—commonly referred to as software
developers or software engineers—should be familiar with the full range of capabilities provided by the DBMS to accomplish their tasks.

1.6 Advantages of Using the DBMS Approach

1.5 Workers behind the Scene
In addition to those who design, use, and administer a database, others are associated with the design, development, and operation of the DBMS software and system
environment. These persons are typically not interested in the database content
itself. We call them the workers behind the scene, and they include the following
categories:
■

DBMS system designers and implementers design and implement the
DBMS modules and interfaces as a software package. A DBMS is a very
complex software system that consists of many components, or modules,
including modules for implementing the catalog, query language processing, interface processing, accessing and buffering data, controlling concurrency, and handling data recovery and security. The DBMS must interface
with other system software, such as the operating system and compilers for
various programming languages.
■ Tool developers design and implement tools—the software packages that
facilitate database modeling and design, database system design, and
improved performance. Tools are optional packages that are often purchased separately. They include packages for database design, performance
monitoring, natural language or graphical interfaces, prototyping, simulation, and test data generation. In many cases, independent software vendors
develop and market these tools.
■ Operators and maintenance personnel (system administration personnel)
are responsible for the actual running and maintenance of the hardware and
software environment for the database system.
Although these categories of workers behind the scene are instrumental in making
the database system available to end users, they typically do not use the database
contents for their own purposes.

1.6 Advantages of Using the DBMS Approach
In this section we discuss some additional advantages of using a DBMS and the
capabilities that a good DBMS should possess. These capabilities are in addition to
the four main characteristics discussed in Section 1.3. The DBA must utilize these
capabilities to accomplish a variety of objectives related to the design, administration, and use of a large multiuser database.

1.6.1 Controlling Redundancy
In traditional software development utilizing file processing, every user group
maintains its own files for handling its data-processing applications. For example,
consider the UNIVERSITY database example of Section 1.2; here, two groups of
users might be the course registration personnel and the accounting office. In the
traditional approach, each group independently keeps files on students. The

17

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Chapter 1 Databases and Database Users

accounting office keeps data on registration and related billing information,
whereas the registration office keeps track of student courses and grades. Other
groups may further duplicate some or all of the same data in their own files.
This redundancy in storing the same data multiple times leads to several problems.
First, there is the need to perform a single logical update—such as entering data on
a new student—multiple times: once for each file where student data is recorded.
This leads to duplication of effort. Second, storage space is wasted when the same
data is stored repeatedly, and this problem may be serious for large databases.
Third, files that represent the same data may become inconsistent. This may happen
because an update is applied to some of the files but not to others. Even if an
update—such as adding a new student—is applied to all the appropriate files, the
data concerning the student may still be inconsistent because the updates are applied
independently by each user group. For example, one user group may enter a student’s birth date erroneously as ‘JAN-19-1988’, whereas the other user groups may
enter the correct value of ‘JAN-29-1988’.
In the database approach, the views of different user groups are integrated during
database design. Ideally, we should have a database design that stores each logical
data item—such as a student’s name or birth date—in only one place in the database. This is known as data normalization, and it ensures consistency and saves
storage space (data normalization is described in Part 6 of the text).
However, in practice, it is sometimes necessary to use controlled redundancy to
improve the performance of queries. For example, we may store Student_name and
Course_number redundantly in a GRADE_REPORT file (Figure 1.6(a)) because
whenever we retrieve a GRADE_REPORT record, we want to retrieve the student
name and course number along with the grade, student number, and section identifier. By placing all the data together, we do not have to search multiple files to collect this data. This is known as denormalization. In such cases, the DBMS should

Figure 1.6
Redundant storage
of Student_name
and Course_name in
GRADE_REPORT.
(a) Consistent data.
(b) Inconsistent
record.

GRADE_REPORT

(a)

Student_number

Student_name

Section_identifier Course_number

Grade

17

Smith

112

MATH2410

B

17

Smith

119

CS1310

C

8

Brown

85

MATH2410

A

8

Brown

92

CS1310

A

8

Brown

102

CS3320

B

8

Brown

135

CS3380

A

GRADE_REPORT

(b)

Student_number

Student_name

17

Brown

Section_identifier Course_number
112

MATH2410

Grade
B

1.6 Advantages of Using the DBMS Approach

have the capability to control this redundancy in order to prohibit inconsistencies among the files. This may be done by automatically checking that the
Student_name–Student_number values in any GRADE_REPORT record in Figure 1.6(a) match one of the Name–Student_number values of a STUDENT record (Figure 1.2). Similarly, the Section_identifier–Course_number values in GRADE_REPORT
can be checked against SECTION records. Such checks can be specified to the DBMS
during database design and automatically enforced by the DBMS whenever the
GRADE_REPORT file is updated. Figure 1.6(b) shows a GRADE_REPORT record that
is inconsistent with the STUDENT file in Figure 1.2; this kind of error may be entered
if the redundancy is not controlled. Can you tell which part is inconsistent?

1.6.2 Restricting Unauthorized Access
When multiple users share a large database, it is likely that most users will not be
authorized to access all information in the database. For example, financial data
such as salaries and bonuses is often considered confidential, and only authorized persons are allowed to access such data. In addition, some users may only
be permitted to retrieve data, whereas others are allowed to retrieve and update.
Hence, the type of access operation—retrieval or update—must also be controlled. Typically, users or user groups are given account numbers protected by
passwords, which they can use to gain access to the database. A DBMS should
provide a security and authorization subsystem, which the DBA uses to create
accounts and to specify account restrictions. Then, the DBMS should enforce
these restrictions automatically. Notice that we can apply similar controls to the
DBMS software. For example, only the DBA’s staff may be allowed to use certain
privileged software, such as the software for creating new accounts. Similarly,
parametric users may be allowed to access the database only through the predefined apps or canned transactions developed for their use. We discuss database security and authorization in Chapter 30.

1.6.3 Providing Persistent Storage for Program Objects
Databases can be used to provide persistent storage for program objects and data
structures. This is one of the main reasons for object-oriented database systems
(see Chapter 12). Programming languages typically have complex data structures,
such as structs or class definitions in C++ or Java. The values of program variables
or objects are discarded once a program terminates, unless the programmer explicitly stores them in permanent files, which often involves converting these complex
structures into a format suitable for file storage. When the need arises to read this
data once more, the programmer must convert from the file format to the program
variable or object structure. Object-oriented database systems are compatible with
programming languages such as C++ and Java, and the DBMS software automatically performs any necessary conversions. Hence, a complex object in C++
can be stored permanently in an object-oriented DBMS. Such an object is said to
be persistent, since it survives the termination of program execution and can
later be directly retrieved by another program.

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Chapter 1 Databases and Database Users

The persistent storage of program objects and data structures is an important function of database systems. Traditional database systems often suffered from the socalled impedance mismatch problem, since the data structures provided by the
DBMS were incompatible with the programming language’s data structures.
Object-oriented database systems typically offer data structure compatibility with
one or more object-oriented programming languages.

1.6.4 Providing Storage Structures and Search
Techniques for Efficient Query Processing
Database systems must provide capabilities for efficiently executing queries and
updates. Because the database is typically stored on disk, the DBMS must provide
specialized data structures and search techniques to speed up disk search for the
desired records. Auxiliary files called indexes are often used for this purpose.
Indexes are typically based on tree data structures or hash data structures that are
suitably modified for disk search. In order to process the database records needed
by a particular query, those records must be copied from disk to main memory.
Therefore, the DBMS often has a buffering or caching module that maintains parts
of the database in main memory buffers. In general, the operating system is responsible for disk-to-memory buffering. However, because data buffering is crucial to
the DBMS performance, most DBMSs do their own data buffering.
The query processing and optimization module of the DBMS is responsible for
choosing an efficient query execution plan for each query based on the existing
storage structures. The choice of which indexes to create and maintain is part of
physical database design and tuning, which is one of the responsibilities of the DBA
staff. We discuss query processing and optimization in Part 8 of the text.

1.6.5 Providing Backup and Recovery
A DBMS must provide facilities for recovering from hardware or software failures.
The backup and recovery subsystem of the DBMS is responsible for recovery. For
example, if the computer system fails in the middle of a complex update transaction, the recovery subsystem is responsible for making sure that the database is
restored to the state it was in before the transaction started executing. Disk backup
is also necessary in case of a catastrophic disk failure. We discuss recovery and
backup in Chapter 22.

1.6.6 Providing Multiple User Interfaces
Because many types of users with varying levels of technical knowledge use a database, a DBMS should provide a variety of user interfaces. These include apps for
mobile users, query languages for casual users, programming language interfaces
for application programmers, forms and command codes for parametric users,
and menu-driven interfaces and natural language interfaces for standalone users.
Both forms-style interfaces and menu-driven interfaces are commonly known as

1.6 Advantages of Using the DBMS Approach

graphical user interfaces (GUIs). Many specialized languages and environments
exist for specifying GUIs. Capabilities for providing Web GUI interfaces to a
database—or Web-enabling a database—are also quite common.

1.6.7 Representing Complex Relationships among Data
A database may include numerous varieties of data that are interrelated in many
ways. Consider the example shown in Figure 1.2. The record for ‘Brown’ in the
STUDENT file is related to four records in the GRADE_REPORT file. Similarly,
each section record is related to one course record and to a number of
GRADE_REPORT records—one for each student who completed that section. A
DBMS must have the capability to represent a variety of complex relationships
among the data, to define new relationships as they arise, and to retrieve and
update related data easily and efficiently.

1.6.8 Enforcing Integrity Constraints
Most database applications have certain integrity constraints that must hold for
the data. A DBMS should provide capabilities for defining and enforcing these
constraints. The simplest type of integrity constraint involves specifying a data
type for each data item. For example, in Figure 1.3, we specified that the value of
the Class data item within each STUDENT record must be a one-digit integer and
that the value of Name must be a string of no more than 30 alphabetic characters.
To restrict the value of Class between 1 and 5 would be an additional constraint
that is not shown in the current catalog. A more complex type of constraint that
frequently occurs involves specifying that a record in one file must be related to
records in other files. For example, in Figure 1.2, we can specify that every section
record must be related to a course record. This is known as a referential integrity
constraint. Another type of constraint specifies uniqueness on data item values,
such as every course record must have a unique value for Course_number. This is
known as a key or uniqueness constraint. These constraints are derived from the
meaning or semantics of the data and of the miniworld it represents. It is the
responsibility of the database designers to identify integrity constraints during
database design. Some constraints can be specified to the DBMS and automatically
enforced. Other constraints may have to be checked by update programs or at the
time of data entry. For typical large applications, it is customary to call such constraints business rules.
A data item may be entered erroneously and still satisfy the specified integrity constraints. For example, if a student receives a grade of ‘A’ but a grade of ‘C’ is entered
in the database, the DB…
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