1. Intro to MIS – MGS351 Databases and Data Warehouses Chapter 3

2. Chapter Overview m Data Hierarchy m Traditional File Environment m Databases – Relational, Hierarchical, Network m Design and Normalization m Data Warehousing

3. Data Hierarchy m Database m Table, File, Relation m Records, Rows, Tuples m Fields, Columns, Attributes m Bytes m Bits

4. Data Hierarchy Figure 7-1

5. Traditional File Environment Issues: m Data Redundancy m Data Inconsistency m Data Isolation m Data Integrity m Security m Application / Data Dependence

6. Traditional File Processing Figure 7-3

7. Database Approach m Minimal data redundancy m Data consistency m Integration of data m Sharing of data m Uniform security, privacy and integrity m Data independence

8. Database Environment Figure 7-4

9. DBMS Components Data definition language: Specifies content and structure of database and defines each data element Data manipulation language: Manipulates data in a database Data dictionary: Stores definitions of data elements, and data characteristics

10. Evolution of Databases

11. Relational Database Represents data as two- dimensional tables called relations Relates data across tables based on common data element Examples: DB2, Oracle, MS SQL Server

12. Relational Database Figure 7-6

13. Hierarchical Database Organizes data in a tree-like structure Supports one-to-many parent-child relationships Prevalent in large legacy systems

14. Hierarchical Database Figure 7-8

15. Network Database m Depicts data logically as many-to- many relationships m Less flexible compared to RDBMS m Lack support for ad-hoc and English language-like queries

16. Network Database Figure 7-9

17. Database Design m Conceptual / Logical Design – Abstract model of database from business perspective. m Physical Design – shows how the database is arranged on storage devices.

18. ER Diagram Figure 7-10

19. m Knowledge about your: – Customers – Competitors – Partners – Competitive environment – Internal operations Business Intelligence

21. oOnline transaction processing (OLTP) - the gathering of input information, processing that information, and updating existing information to reflect the gathered and processed information. oOperational databases - databases that support OLTP. oOnline analytical processing (OLAP) - the manipulation of information to support decision making.

22. Data Warehousing m Multidimensional Data Models m OLAP m Data Marts – subset of data warehouse m Data Mining – finding hidden patterns and trends in data

23. Data Warehousing m Data warehouse - a logical collection of information gathered from many different operational databases (Extract, Transform, Load) used to create business intelligence that supports business analysis activities and decision- making tasks.

24. Data Warehouse

25. Multidimensional Data Model Figure 7-15

26. Data Marts m Data mart - a subset of a data warehouse in which only a focused portion of the data warehouse information is kept.

27. Data Mining Tools

28. Distributed Databases Figure 7-13

29. Databases and the Web Figure 7-18