This document discusses fundamental database concepts and data resource management. It defines key database elements like characters, fields, records, files and databases. It describes different database structures like hierarchical, network and relational structures. It also discusses distributed databases, data warehouses, data mining and compares traditional file processing with database management. Database management systems are introduced as software to create, maintain and use organizational databases.

1. Chapter -5
Data Resource Management

2. Fundamental Database Concept
• How data are organized in Information System
• A conceptual framework of level of data
• Data may be logically organized into
– Character
– Field
– Record
– File
– Database

3. • Character
– The basic logical element of data that can be observed and
manipulated
– A single alphabetic, numeric, or other symbol
• Field or data item
– A data item that consists a group of related characters
– Represents an attribute of some entity (object, person, place,
event)
– Fields are organized in some logical manner
• Example: salary amount, job
• Record
– A group of related fields used to describe the attributes of an
entity
– A record represents a collection of attributes
– Primary key concept

4. • File
– A group of related record
– A collection of records that make up a table
– A row and column in a table represents a file in database
• Database
– An integrated collection of logically related data elements
– Database consolidates records previously stored in
separate files in a common pool of data elements that can
be accessed via many applications
– Data stored in DB are independent of applications
– Database contains data elements describing entities and
relationship among entities
– It’s a logical organization of data elements for easy access

5. Logical Data Elements

6. Electric Utility Database

7. Database Structures
• Common database structures…
– Hierarchical
– Network
– Relational
– Object-oriented
– Multi-dimensional

8. Hierarchical Structure
• Early DBMS structure
• Records arranged in
tree-like structure
• Relationships are one-
to-many

9. Network Structure
• Used in some
mainframe DBMS
packages
– Many-to-many
relationships

10. Relational Structure
• Most widely used structure
– Data elements are stored in tables
– Row represents a record; column is a field
– Can relate data in one file with data in another,
if both files share a common data element

11. Multidimensional Structure
• Variation of relational model
– Uses multidimensional structures to
organize data
– Data elements are viewed as being in cubes
– Popular for analytical databases that support
Online Analytical Processing (OLAP)

12. Object-Oriented Structure
• An object consists of
– Data values describing the attributes of an entity
– Operations that can be performed on the data
• Encapsulation
– Combine data and operations
• Inheritance
– New objects can be created by replicating some or all
of the characteristics of parent objects
• Used in OODBMS
• Supports complex data types more efficiently than
relational databases
– Example: graphic images, video clips, web pages

13. Object-Oriented Structure

14. Evaluation of Database Structures
• Hierarchical
– Works for structured, routine transactions
– Can’t handle many-to-many relationship
• Network
– More flexible than hierarchical
– Unable to handle ad hoc requests
• Relational
– Easily responds to ad hoc requests
– Easier to work with and maintain
– Not as efficient/quick as hierarchical or network

15. Many-to-Many Relationship

16. Data Resource Management
• Data resource management is a managerial activity
– Uses database management, data warehousing,
and other IS technologies
– Manages data resources to meet the information needs of
business stakeholders
Types of Databases
• Operational Databases
– Stores detailed data needed to support business processes
and operations
• Also called subject area databases (SADB), transaction
databases, and production databases
• Database examples: customer, human resource, inventory

17. Data Resource Management
• Distributed Databases
– Distributed databases are copies or parts of databases
stored on servers at multiple locations
– a database in which portions of the database are stored in
multiple physical locations and processing is distributed
among multiple database nodes
• Improves database performance at worksites
– Advantages
• Protection of valuable data
• Data can be distributed into smaller databases
• Each location has control of its local data
• All locations can access any data, any where

18. Data Resource Management
• Distributed Databases
– Disadvantages
• Maintaining data accuracy
• Requires extra computing power
• Network necessary to access multiple databases
– Replication
• Look at each distributed database and find changes
• Apply changes to each distributed database
• Very complex and time consuming
– Duplication
• One database is master
• Duplicate the master after hours, in all locations
• Easier to accomplish

19. Data Resource Management
• External Databases
– Databases available for a fee from commercial online
services, or free from the Web
• Example: hypermedia databases, statistical databases,
bibliographic and full text databases
• Search engines like Google or Yahoo are external
databases
• Hypermedia Databases
– A hypermedia database contains
• Hyperlinked pages of multimedia
• Interrelated hypermedia page elements, rather than
interrelated data records

20. Components of Web-Based System

21. Data Warehouse
• Stores static data that has been extracted from other
databases in an organization
– Central source of data that has been cleaned,
transformed, and cataloged
– Data is used for data mining, analytical processing,
analysis, research, decision support
• Data warehouses may be divided into data marts
– Subsets of data that focus on specific aspects
of a company (department or business process)

22. Data Warehouse Components
R

23. Applications and Data Marts

24. Data Mining
• Data in data warehouses are analyzed to reveal hidden
patterns and trends in historical data
• Data mining analysis help business to make strategic decisions
to gain competitive advantage
• Data mining can discover new or unknown correlations,
patterns, and trends in vast amount of data stored in data
warehouse.
• Data Mining software uses various pattern recognition
algorithms and mathematical and statistical techniques
– Market-basket analysis to identify new product bundles
– Find root cause of quality or manufacturing problems
– Prevent customer attrition
– Acquire new customers
– Cross-sell to existing customers
– Profile customers with more accuracy

25. Traditional File Processing
• In traditional file processing system, data are
organized, stored, and processed in independent files
– Each business application designed to use specialized
data files containing specific types of data records
– Cumbersome, costly and inflexible to supply
information needed to manage modern business
• Problems
– Data redundancy
– Lack of data integration
– Data dependence (files, storage devices, software)
– Lack of data integrity or standardization

26. Problems of File Processing
• Data redundancy
– Duplicate data
– Caused problem when data has to be updated
– Separate file maintenance programs
– Inconsistency among data stored in separate files
• Lack of data integrity or standardization
– Different files and programs define different definition
for data elements
– Lack of accuracy and completeness
– Lack of control over its access and use

27. Problems of File Processing
• Data dependence
– Files, storage devices, software are interdependent
– Changes in the format and structure of data/records
in a file required changes to be made in all the
programs that access the file
– Program maintenance was a major burden and
resulted into inconsistency
• Lack of data integration
– Separate individual files couldn’t be used for
connecting them for ad-hoc queries
– Resulted into various reports than one consolidated
view

28. Traditional File Processing

29. Database Management
• The foundation of modern methods of managing
organizational data
– Consolidates data records formerly in separate
files into databases (ETL)
– Data can be accessed by many different
application programs
– A database management system (DBMS) is the
software interface between users and databases
– Involves controlling, maintaining and using data

30. Database Management

31. Database Management System
• In mainframe and server computer systems, a software
package that is used to…
– Create new databases and database applications
– Maintain the quality of the data in an organization’s
databases
– Use the databases of an organization to provide the
information needed by end users
• Database Development
– Defining and organizing the content, relationships, and structure of
the data needed to build a database
• Database Application Development
– Using DBMS to create prototypes of queries, forms, reports, Web
pages
• Database Maintenance
– Using transaction processing systems and other tools to add, delete,
update, and correct data