1. Management Information Systems By Effy Oz & Andy Jones
Chapter 6: Databases and Data Warehouses

2. Objectives
Explain the difference between traditional file organisation and the database approach to managing digital data
Explain how relational and object oriented database management systems are used to construct databases, populate them with data, and manipulate the data to produce information
Enumerate the most important features and operations of a relational database, the most popular database model

3. Objectives (continued)
Understand how data modeling and design creates a conceptual blueprint of a database
Discuss how databases are used on the Web
List the operations involved in transferring data from transactional databases to data warehouses

4. Managing Digital Data Businesses collect and dissect data
Data can be stored in powerful database format
Easy access and manipulation
Databases have profound impact on business
Database technology integrated with Internet

5. The Traditional File Approach
Traditional file approach: no mechanism for manipulating data
Database approach: has mechanism for manipulating data
Traditional approach inconvenient
High data redundancy
Low data integrity
Data redundancy: duplication of data
Data integrity: accuracy of data

6. The Traditional File Approach (continued)

7. The Database Approach People Events Products
Database approach: data organised as entities
Entity: object that has data
People
Events
Products
Character: smallest piece of data
Field: single piece of information about entity
Record: collection of fields

8. The Database Approach (continued)
File: collection of related records
Database management system (DBMS): program used to build databases
Populates with data
Manipulates data
Query: message requesting access to data

9. The Database Approach (continued)
Database has security issues
Database administrator (DBA): limits user access to database
Requires users to enter codes
DBMS bundled with fourth-generation languages

10. The Database Approach (continued)

11. The Database Approach (continued)

12. The Database Approach (continued)

13. Database Models Database model: general logical structure
How records stored in database
Records linked differently in different models
Models constantly changing

14. The Relational Model Relational Model: consists of tables
Based on relational algebra
Tuple: record
Attribute: field
Relation: table
Key: identifier field
Used to retrieve records

15. Relational Model (continued)

16. The Relational Model (continued)
Primary key: unique key
Uniquely identifies record
Required in table
Composite key: combination of fields
Serves as primary key
Foreign key: shared field
Links tables
Join table: composite of tables

17. The Relational Model (continued)

18. The Relational Model (continued)
Table relationships with other tables
One-to-many relationship: one item in table linked to many items in other table
Many-to-many relationship: many items in table linked to many items of other table

19. The Object-Oriented Model
Object-Oriented model: uses object-oriented approach
Encapsulation: combined storage of data and relevant procedures
Allows object to be planted in different data sets
Inheritance: creates new object by replicating characteristics of existing (parent) object

20. The Object-Oriented Model (continued)

21. Relational Operations
Relational operation: create temporary subset of table
Create limited list or joined table list
Select records based on conditions
Project columns
Join tables to create temporary table

22. Structured Query Language
Structured query language: language of choice for DBMSs
Advantages
Standardised language
Used in many host languages
Portable

23. The Schema and Metadata
Schema: plan
Describes structure of database
Names and sizes of fields
Identifies primary keys
Data dictionary: repository of information about data

24. The Schema and Metadata (continued)
Metadata: data about data
Source of data
Tables related to data
Field information
Usage of data
Population rules

25. The Schema and Metadata (continued)

26. Data Modeling Databases must be carefully planned
Data modeling: analysis and organisation of data
Proactive process
Develop conceptual blueprint
Entity relationship diagram: graphical representation of relationships

27. Data Modeling (continued)
Entity relationship diagram
Boxes identify entities
Lines indicate relationship
Crossbars indicate mandatory fields
Circles indicate optional
Crows feet identify “many”

28. Data Modeling (continued)

29. Data Modeling (continued)

30. Databases on the Web Web dependent on databases
Interface between Web and database required
CGI
ASP
API

31. Databases on the Web (continued)

32. Data Warehousing Data collections used for transactions
Accumulation of transaction data useful
Data warehouse: large database
Typically relational
Supports decision making
Data copied from transactional database
Data mart: collection of data focusing on particular subject

33. From Database to Data Warehouse
Transactional database not suitable for business analysis
Only current data
Not historic
Data warehouse requires large storage capacity
Mainframe computers used
Scalability issue

34. Phases in Building a Data Warehouse
Begin building data warehouse after equipment secured
Extraction phase
Create files from transactional database
Transformation phase
Cleanse and modify data
Loading phase
Transfer files to data warehouse

35. Phases in Building a Data Warehouse (continued)

36. Summary organisations collect vast amounts of data
Database approach has advantages over traditional approach
Character: smallest piece of data
File: collection of records
Designer must construct schema to construct database

37. Summary (continued)
Database management system enables database construction and manipulation
Relational and object-oriented database models have different advantages
Keys used to form links among entities
Object-oriented database maintains links differently
SQL adopted as international standard

38. Summary (continued)
Designers conduct data modeling to show required tables
Databases often linked to Web
Data warehouses contain huge collections of historical data
Data warehouse allows data extraction, transformation, and loading
Invasion of privacy is exacerbated by database technology