The document discusses database management and related concepts. It defines database management as applying information systems technologies to manage an organization's data resources to meet business needs. It describes different database structures like hierarchical, network, relational, and object-oriented. It also discusses database development processes like conceptual design, entity-relationship modeling, normalization, and implementation. Data warehousing and data mining are also summarized.

1. Database Management

2. Why Study Data Resource Management?
• Today’s business enterprises cannot survive or
succeed without quality data about their internal
operations and external environment.
Definition:
• A managerial activity that applies information
systems technologies to the task of managing an
organization’s data resources to meet the
information needs of their business stakeholders

3. Foundation Data Concepts
• Character – single alphabetic, numeric or
other symbol
• Field – group of related characters
• Entity – person, place, object or event
• Attribute – characteristic of an entity

4. Foundation Data Concepts
• Record – collection of attributes that describe
an entity
• File – group of related records
• Database – integrated collection of logically
related data elements

5. Traditional File Processing vs DBMS
Definition: Traditional File Processing
• Data are organized, stored, and processed in
independent files of data records
Definition: Database Management Systems
• Software that controls the creation,
maintenance, and use of databases

6. File Processing Systems

7. Problems of File Processing
• Data Redundancy – duplicate data requires an update to
be made to all files storing that data
• Lack of Data Integration – data stored in separate files
require special programs for output making ad hoc
reporting difficult
• Data Dependence – programs must include information
about how the data is stored so a change in storage
format requires a change in programs

8. Database Management Approach
Definition:
• Consolidates data records into one database
that can be accessed by many different
application programs.
• Software interface between users and
databases
• Data definition is stored once, separately from
application programs

9. Database Management Approach

10. DBMS Software Components

11. Uses of DBMS Software

12. Types of Databases

13. Types of Databases
• Operational – store detailed data needed to
support the business processes and
operations of a company
• Distributed – databases that are replicated
and distributed in whole or in part to network
servers at a variety of sites

14. Types of Databases
• External – contain a wealth of information
available from commercial online services and
from many sources on the World Wide Web
• Hypermedia – consist of hyperlinked pages of
multimedia

15. Fundamental Database Structures

16. Database Structures
• Hierarchical – relationships between records form a hierarchy or treelike
structure. A record is subdivided into segments that are connected to
each other in one to many parent – child relationship. Relationships
among the records are one-to-many.
• Network – data can be accessed by one of several paths because any data
element or record can be related to any number of other data elements.
An older logical database model that is useful for depicting many-to-many
relationships. The network structure can represent more complex logical
relationships, and is still used by many mainframe DBMS packages.

17. Relational Database Structure
Definition:
• All data elements within the database are
viewed as being stored in the form of simple
tables

18. Relational Database

19. Multidimensional Database Structure
Definition:
• Variation of the relational model that uses
multidimensional structures to organize data
and express the relationships between data

20. Multidimensional Database Structure

21. Object-Oriented Database
Structure
Definition:
• Can accommodate more complex data types including
graphics, pictures, voice and text
• Encapsulation – data values and operations that can be
performed on them are stored as a unit
• Inheritance – automatically creating new objects by
replicating some or all of the characteristics of one or
more existing objects

22. Evaluation of Database Structures
• Hierarchical data structure is best for
structured, routine types of transaction
processing.
• Network data structure is best when many-to-
many relationships are needed.
• Relational data structure is best when ad hoc
reporting is required.

23. Database Development
• Enterprise-wide database development is
usually controlled by database administrators
(DBA)
• Data dictionary – catalog or directory
containing metadata
• Metadata – data about data

24. Database Development Process
• First, develop a Conceptual design
– - an abstract model of the database from the user or
business perspective .
– - Create physical and logical view
• Second, organize with Entity-Relationship (ER)
modeling
– process of planning the database design
– Entity classes  Instance  Identifiers  Relationships

25. Database Development Process
• Third, analyze the data structure by applying the
Normalization process
– method that reduces a relational database to its most
streamlined form
– Helps achieve
• minimum redundancy
• maximum data integrity
• best processing performance

26. Database Development Process
• Fourth, physically implement the data structure in
the database management system software (
– Create tables
– Define fields and field properties
– Establish primary keys
– Define table relationships
– Add actual data (records) to tables

27. Logical vs. Physical Views
• Logical – logical consist of conceptual design
within an abstract model which data
elements and relationships (subschemas) are
used in the model.
• Physical – the design shows how the database
is arranged, physically stored and accessed on
the storage devices of a computer system.

28. Logical and Physical Database
Views

29. Database Maintenance
• Updating a database continually to reflect
new business transactions and other events
• Updating a database to correct data and
ensure accuracy of the data

30. Data Mining
Definition:
Analyzing the data in a data warehouse to
reveal hidden patterns and trends in
historical business activity

31. Data Mining Uses
• Perform “market-basket analysis” to identify new
product bundles.
• Find root causes to quality or manufacturing
problems.
• Prevent customer attrition and acquire new
customers.
• Cross-sell to existing customers.
• Profile customers with more accuracy.

32. Data Warehouse vs Data Mart
Definition:Data Warehouse
• Large database that stores data that have been
extracted from the various operational, external, and
other databases of an organization
Definition: Data Mart
• Databases that hold subsets of data from a data
warehouse that focus on specific aspects of a
company, such as a department or a business
process

33. Data Warehouse System

34. Basic Characteristics of
Data Warehouse
Organized by business dimension or subject
• Data are organized by subject (by customer, supplier, vendor, product,
region etc) and contain information relevant for decision and data
analysis.
Consistent
• Data in different databases may encoded differently. E.g, gender data may
be encoded 0 and 1 in one operational system and or M and F in another.
In data warehouse , it must be coded in a consistent manner
Nonvolatile
• Data are not updated after they are entered into the warehouse

35. Basic Characteristics of
Data Warehouse (cont’)
Historical
• The data are kept for many years so that they can be used for trends,
forecasting and comparison over time.
Use online analytical processing
• Online Analytical Processing (OLAP) involves the analysis of accumulated
data by end users which are designed to support decision makers.
Multidimensional Structure
• Data warehouse use multidimensional data structure so it allow users to
view and analyze data from the various perspective of the various
business dimensions.

36. How a Data Warehouse &Data Mart Hold Data