Chapter 1
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File Systems and Databases
Database Systems: Design, Implementation, and
Management, 4th Edition
Peter Rob & Carlos Coronel
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Introducing the Database
Major Database Concepts
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Data and information
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Data - Raw facts

Information - Processed data
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Data management

Database

Metadata

Database management system (DBMS)
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Sales per Employee for Each of ROBCOR’S Two Divisions
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Figure 1.1
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Introducing the Database
 Importance of DBMS
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It helps make data management more efficient and
effective.
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Its query language allows quick answers to ad hoc
queries.
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It provides end users better access to more and bettermanaged data.
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It promotes an integrated view of organization’s
operations -- “big picture.”
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It reduces the probability of inconsistent data.
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The DBMS Manages the Interaction
Between the End User and the Database
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Figure 1.2
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Introducing the Database
 Why Database Design Is Important?
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A well-designed database facilitates data management
and becomes a valuable information generator.
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A poorly designed database is a breeding ground for
uncontrolled data redundancies.
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A poorly designed database generates errors that lead
to bad decisions.
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Historical Roots
 Why Study File Systems?
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It provides historical perspective.
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It teaches lessons to avoid pitfalls of data management.
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Its simple characteristics facilitate understanding of the
design complexity of a database.
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It provides useful knowledge for converting a file
system to a database system.
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Contents of the CUSTOMER File
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Figure 1.3
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Table 1.1 Basic File Terminology
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Data
“Raw” facts that have little meaning unless they have been
organized in some logical manner. The smallest piece of data
that can be “recognized” by the computer is a single
character, such as the letter A, the number 5, or some
symbol such as; ‘ ? > * +. A single character requires one
byte of computer storage.
Field
A character or group of characters (alphabetic or numeric)
that has a specific meaning. A field might define a telephone
numbers, a birth date, a customer name, a year-to-date
(YTD) sales value, and so on.
Record
A logically connected set of one or more fields that describes
a person, place, or thing. For example, the fields that
comprise a record for a customer named J. D. Rudd might
consist of J. D. Rudd’s name, address, phone number, date
of birth, credit limit, unpaid balance, and so on.
File
A collection of related records. For example, a file might
contain data about ROBCOR Company’s vendors; or, a file
might contain the records for the students currently enrolled
at Gigantic University.
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Contents of the AGENT File
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Figure 1.4
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A Simple File System
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Figure 1.5
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File System Critique
 File System Data Management
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File systems require extensive programming in a thirdgeneration language (3GL).

As the number of files expands, system administration
becomes difficult.

Making changes in existing file structures is important
and difficult.

Security features to safeguard data are difficult to
program and usually omitted.

Difficulty to pool data creates islands of information.
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File System Critique
 Structural and Data Dependence
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Structural Dependence
A change in any file’s structure requires the modification of
all programs using that file.
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Data Dependence
A change in any file’s data characteristics requires changes
in all data access programs.
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
Significance of data dependence is the difference
between the data logical format and the data physical
format.
Data dependence makes file systems extremely
cumbersome from a programming and data
management point of view.
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File System Critique
 Field Definitions and Naming Conventions
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A good (flexible) record definition anticipates reporting
requirements by breaking up fields into their
components.

Example:
– Customer Name  Last Name, First Name, Initial
– Customer Address  Street Address, City, State
FIELD
CONTENTS
CUS_LNAME
Customer last name
CUS_FNAME
Customer first name
CUS_INITIAL
Customer initial
CUS_AREACODE
Customer area code
CUS_PHONE
Customer phone
CUS_ADDRESS
Customer street address or box number
CUS_CITY
Customer city
CUS_STATE
Customer state
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File System Critique
 Field Definitions and Naming Conventions
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Selecting proper field names is very important.
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Names must be as descriptive as possible within restrictions.
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Naming must reflect designer’s documentation needs and
user’s reporting and processing requirements.
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File System Critique
 Data Redundancy:
Uncontrolled data redundancy sets the stage for
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Data Inconsistency (lack of data integrity)
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Data anomalies
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Modification anomalies
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Insertion anomalies
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Deletion anomalies
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Figure 1.6
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The Database System Environment
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Figure 1.7
Figure 1.7
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Database Systems
 The Database System Components
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Hardware
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Computer
Peripherals
Software
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Operating systems software
DBMS software
Applications programs and utilities software
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Database Systems
 The Database System Components
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People
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Procedures
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Systems administrators
Database administrators (DBAs)
Database designers
Systems analysts and programmers
End users
Instructions and rules that govern the design and use of the
database system
Data

Collection of facts stored in the database
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Database Systems
 The Database System Components
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The complexity of database systems depends on various
organizational factors:
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Organization’s size
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Organization’s function
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Organization’s corporate culture
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Organizational activities and environment
Database solutions must be cost effective AND
strategically effective.
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Database Systems
 Types of Database Systems

Number of Users

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Single-user
– Desktop database
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Multiuser
– Workgroup database
– Enterprise database
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Scope
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Desktop
Workgroup
Enterprise
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Database Systems
 Types of Database Systems
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Location
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Centralized
Distributed
Use
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Transactional (Production)
Decision support
Data warehouse
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Database Systems
 DBMS Functions
1. Data Dictionary Management
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2. Data Storage Management
3. Data Transformation and Presentation
4. Security Management
5. Multi-User Access Control
6. Backup and Recovery Management
7. Data Integrity Management
8. Database Access Languages (DDL and DML) and Application
Programming Interfaces
9. Database Communication Interfaces
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Database Models
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 A database model is a collection of logical
constructs used to represent the data structure
and the data relationships found within the
database.
 Two Categories of Database Models
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Conceptual models focus on the logical nature of the data
representation. They are concerned with what is
represented rather than how it is represented.
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Implementation models place the emphasis on how the
data are represented in the database or on how the data
structures are implemented.
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Database Models
 Three Types of Relationships
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One-to-many relationships (1:M)
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A painter paints many different paintings, but each one of
them is painted by only that painter.
– PAINTER (1) paints PAINTING (M)
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Many-to-many relationships (M:N)
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An employee might learn many job skills, and each job skill
might be learned by many employees.
– EMPLOYEE (M) learns SKILL (N)
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One-to-one relationships (1:1)

Each store is managed by a single employee and each store
manager (employee) only manages a single store.
– EMPLOYEE (1) manages STORE (1)
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Database Models
 Three Types of Implementation Database Models
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Hierarchical database model
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Network database model
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Relational database model
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A Hierarchical Structure
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Figure 1.8
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Database Models
 Hierarchical Database Model
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Basic Structure
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Collection of records logically organized to conform to the
upside-down tree (hierarchical) structure.
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The top layer is perceived as the parent of the segment
directly beneath it.
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The segments below other segments are the children of the
segment above them.
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A tree structure is represented as a hierarchical path on the
computer’s storage media.
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Database Models
 Hierarchical Database Model
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Advantages
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Conceptual simplicity
Database security
Data independence
Database integrity
Efficiency dealing with a large database
Disadvantages
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Complex implementation
Difficult to manage
Lacks structural independence
Applications programming and use complexity
Implementation limitations
Lack of standards
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Child with Multiple Parents
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Figure 1.9
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Database Models
 Network Database Model
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Basic Structure
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Set -- A relationship is called a set. Each set is composed of
at least two record types: an owner (parent) record and a
member (child) record.
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A set is represents a 1:M relationship between the owner and
the member.
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A Network Database Model
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Figure 1.10
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Database Models
 Network Database Model
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Advantages
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Conceptual simplicity
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Handles more relationship types
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Data access flexibility
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Promotes database integrity
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Data independence
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Conformance to standards
Disadvantages
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System complexity
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Lack of structural independence
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Database Models
 Relational Database Model
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Basic Structure
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RDBMS allows operations in a human logical environment.
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The relational database is perceived as a collection of tables.
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Each table consists of a series of row/column intersections.
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Tables (or relations) are related to each other by sharing a
common entity characteristic.
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The relationship type is often shown in a relational schema.
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A table yields complete data and structural independence.
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Linking Relational Tables
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Figure 1.11
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Database Models
 Relational Database Model
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Advantages
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Structural independence
Improved conceptual simplicity
Easier database design, implementation, management, and
use
Ad hoc query capability (SQL)
Powerful database management system
Disadvantages
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Substantial hardware and system software overhead
Possibility of poor design and implementation
Potential “islands of information” problems
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A Relational Schema
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Figure 1.12
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Database Models
 Entity-Relationship Data Model
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It is one of the most widely accepted graphical data modeling
tools.
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It graphically represents data as entities and their relationships in
a database structure.
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It complements the relational data model concepts.
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Database Models
 Entity Relationship Data Model
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Basic Structure
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E-R models are normally represented in an entity
relationship diagram (ERD).
An entity is represented by a rectangle.
Each entity is described by a set of attributes. An attribute
describes a particular characteristics of the entity.
A relationship is represented by a diamond connected to the
related entities.
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Figure 1.13 Relationship Depiction: The ERD
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Figure 1.14
Relationship Depiction: The Crow’s Foot
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Database Models
 Entity-Relationship Data Model
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Advantages
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Exceptional conceptual simplicity
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Visual representation
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Effective communication tool
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Integrated with the relational database model
Disadvantages
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Limited constraint representation
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Limited relationship representation
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No data manipulation language
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Loss of information content
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Database Models
 Object-Oriented Database Model
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Characteristics
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An object is described by its factual content.
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An object includes information about relationships between
the facts within the object, as well as with other objects.
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An object is a self-contained building block for autonomous
structures.
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Database Models
 Object-Oriented Database Model
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Basic Structure
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Objects are abstractions of real-world entities or events.
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Attributes describe the properties of an object.
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Objects that share similar characteristics are grouped in
classes.
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A class is a collection of similar objects with shared structure
(attributes) and behavior (methods).
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Classes are organized in a class hierarchy.
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An object can inherit the attributes and methods of the classes
above it.
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A Comparison: The OO Data Model and the ER Model
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Figure 1.15
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Database Models
 Object-Oriented Database Model
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Advantages
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Add semantic content
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Visual presentation includes semantic content
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Database integrity
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Both structural and data independence
Disadvantages
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Lack of OODM standards
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Complex navigational data access
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Steep learning curve
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High system overhead slows transactions
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The Development of Data Models
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Figure 1.16
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Wrap-Up: The Evolution of Data
Models
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 Common characteristics required for data
models:
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A data model must show some degree of conceptual simplicity
without compromising the semantic completeness.
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A data model must represent the real world as closely as possible.

The representation of the real-world transformations (behavior)
must be in compliance with the consistency and integrity
characteristics of any data model.
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Wrap-Up: The Evolution of Data
Models
 Database Models and the Internet
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The use of the Internet as a prime business tool is shifting
focus to database products that interface efficiently and
easily with the Internet.
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Successful “Internet age” databases are characterized
by:
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Flexible, efficient, and secure Internet access.
Support for complex data types and relationships.
Seamless interfacing with multiple data sources and structures.
Simplicity of the conceptual database model.
An abundance of available database tools.
A powerful DBMS to help make the DBA’s job easier.
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