1. Basic database terminology and concept

2. DATA REPRESENTATION Analog vs. Digital Digital Two states (1) on
(0) off

3. DATA REPRESENTATION Binary number system
Combination of ones and zeroes represent characters

4. Bit and Byte Bit Byte Short for binary digit Smallest element of data
Either zero or one
Byte
Group of eight bits, which operate as a single unit
Represents one character or number

5. Representing Characters in Bytes

6. Using Binary Code to Calculate

7. Hierarchy of Data
Bit
Byte (Character)
Field
Record
File
Database

8. Database objects/tools
Table
Form
Query
Report

9. Table
A table is a grid of rows and columns
COLUMN
R O W

10. Field A single trait or characteristic about a subject of a table NAMA
TARIKH LAHIR
JANTINA
NO TEL
GAJI

11. Data type Characteristic designate for an Access field Text Number
Currency
Date/time
Yes/No
Memo
OLE object
Hyperlink

12. Exercise Salutation (Mr., Mrs., Ms.) Date of birth Home address
Whether a student is allergy to medication
The words to describe type of allergy
Photo of a student
How many sibling in the house
Salary

13. Record A group of traits about a particular item …
Simply a row in a table!
NAMA
TARIKH LAHIR
JANTINA
NO TEL
GAJI …

14. Value
The actual data entered at the intersection of a row and column

15. Primary keys
Key is a field that serves a specific function within a table.
AutoNumber
Requirement for a relational database. Means, in a field that is the primary key, there can never be duplicate data
example

16. It’s about nothing: Null values and Zero-Length strings
“Report that say something hasn’t happen are always interesting to me because, as we know, there are known knowns, there are things we know we know. We also know there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns-the ones we don’t know we don’t know”
At a press conference in 2003
Donald Rumsfeld
Secretary of Defense
(in the Bush Administration)

17. It’s about nothing: Null values and Zero-Length strings
Example:
Student Name
Has Tel. No.?
Tel. No.
Jasman
Yes
Asmah No
Does not exist
Syafiq
Exist, but we don’t know it
Elangovan
Don’t know
Don’t know if there’s one

18. Activity 2

19. Creating a Database

20. Three main steps Determine the data you need You describe the data
You enter the data into the database

21. Determine Data Needs
Two basic approaches can be used to determine data needs:
A process-oriented approach
An enterprise modeling approach

22. A process-oriented approach
To define data needs in a process-oriented approach:
Define the problem
Identify necessary decisions
Describe information needs
Determine the necessary processing
Specify data needs

23. A process-oriented approach
Sometimes called the problem-oriented approach
Because it begins with a problem
A problem can be good or bad

24. A process-oriented approach
Once problems are identified, the data and processes dealing with problem solutions can be determined
The strength is that it addresses problems well

25. A process-oriented approach
Although the process-oriented approach enables the data needs of each system to be defined in a logical manner, its weakness is the difficulty of linking the data from one problem to another
IS cannot easily share data if they are isolated from other IS dealing with other kind of problems.

26. Enterprise modeling approach

27. Data Planning Process Database development is a top-down process
Develop an enterprise model that defines the basic business process of the enterprise
Define the information needs of end users in a business process
Identify the key data elements that are needed to perform specific business activities (entity relationship diagrams)

28. Enterprise modeling approach
The strength of this approach is that it takes advantages of a broad view of data resources
All areas are considered, and synergy of data resources between areas can be leveraged

29. Enterprise modeling approach
Strategic Planning for information resources
Determining all
of the org.’s data
needs
Storing that data
in the database

30. Data Modeling Techniques
Modeling the org.’s data needs is supported by techniques that describe the data, how the data aggregates into tables, and how tables relate to each other.
A number of techniques: entity-relationship diagrams and class diagrams

31. Entity-relationship diagrams
Are used to describe relationships between conceptual collections of data so that their related records can be joined together

32. Class diagrams
Are used to describe both the data relationship and the actions that operate on the data in the relationships

33. Entity-relationship diagrams
ERDs
Deal with data in entities and the relationships between entities
Entities – conceptual collections of related data fields
Tables are the result of breaking entities into smaller units that conform to the rules for database structures

34. Entity-relationship diagrams
An entity may turn into table, but frequently an entity is broken into several tables
ERDs are a higher level conceptualization of data than tables

35. Entity-relationship diagrams
Entities in ERDs will have names and relationship links
ERD relationships will denote if a record in one entity will relate to one or more records in other entity

36. Entity-relationship diagrams
Let us assume that we need to describe the data needed for a new IS:
IS Sekolah SAYA
3 separate data entities will exist: …

38. 1 1 M M

39. To read the relationship:
One-to-many relationship - “one school record may relate to many staff records and one staff record may relate to only one school record”

40. Many-to-many relationship

41. Many-to-many relationship
A single subject could have many staff, and a single staff could be on many subject

42. 1 1 M M M M

43. Class Diagrams clsMataPel Title Ting addMataPel deleteMataPel
updateMataPel
clsSchool
SchoolName
Adress
addSchool
deleteSchool
updateSchool 1
1…* 1
1…*
1…*
clsStud
Name IC
StudAdd
addStud
deleteStud
updateStud
ExamStud
clsStaf
Name
Salary
HireDate
addStaf
deleteStaf
updateStaf
1…*

44. Using the database
Forms, reports and queries are common methods for accessing the database held in database management system

45. Database Structures
In all IS, data resources must be organized and structured in some logical manner so that they can be accessed easily, processed efficiently, retrieved quickly, and managed effectively.

46. Database Structures Common database structures… Hierarchical Network
Relational
Object-oriented
Multi-dimensional

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

48. Network Structure Used in some mainframe DBMS packages
Many-to-many relationships

49. 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

50. Relational Operations
Select
Create a subset of records that meet a stated criterion
Example: employees earning more than $30,000
Join
Combine two or more tables temporarily
Looks like one big table
Project
Create a subset of columns in a table

51. 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)

52. Multidimensional Model

53. 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

54. Object-Oriented Structure
Source: Adapted from Ivar Jacobsen, Maria Ericsson, and Ageneta Jacobsen, The Object Advantage: Business Process
Reengineering with Object Technology (New York: ACM Press, 1995), p. 65.
1995, Association for Computing Machinery. By permission.

55. Object-Oriented Structure
Used in object-oriented database management systems (OODBMS)
Supports complex data types more efficiently than relational databases
Examples: graphic images, video clips, web pages
Example use for product design

56. 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

57. Database Development Database Administrator (DBA)
In charge of enterprise database development
Improves the integrity and security of organizational databases
Uses Data Definition Language (DDL) to develop and specify data contents, relationships, and structure
Stores these specifications in a data dictionary or a metadata repository

58. Data Dictionary A data dictionary It contains information on..
Contains data about data (metadata)
Relies on specialized software component to manage a database of data definitions
It contains information on..
The names and descriptions of all types of data records and their interrelationships
Requirements for end users’ access and use of application programs
Database maintenance
Security

59. Based on database structure above, describe the characteristics of the IS or the organization which hold the IS. Use the following table for discussion:

60. System types Simple Complex
Has few components, and the relationship or interaction between elements is uncomplicated and straightforward
Has many elements that are highly related and interconnected
Open
Closed
Interact with its environment
Has no interaction with the environment
Stable
Dynamic
Undergoes very little change over time
Undergoes rapid and constant change over time
Adaptive
Non-adaptive
Is able to change in response to changes in the environment
Is not able to change in response to changes in the environment
Permanent
Temporary
Exists for a relatively long period of time
Exists for only relatively short period of time

61. Database Structures
Source: Management Information Systems by James A. O'Brien and George Marakas. McGraw-Hill Higher Education