1. Chapter 13 The Data Warehouse
Database Systems: Design, Implementation, and Management, Fifth Edition, Rob and Coronel

2. In this chapter, you will learn:
How operational data and decision support differ
What a data warehouse is and how its data are prepared
What star schemas are and how they are constructed
What steps are required to implement a data warehouse successfully
What data mining is and what role it plays in decision support
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

3. The Need for Data Analysis
External and internal forces require tactical and strategic decisions
Search for competitive advantage
Business environments are dynamic
Decision-making cycle time is reduced
Different managers require different decision support systems (DSS)
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

4. Decision Support Systems
Is a methodology
Extracts information from data
Uses information as basis for decision making
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

5. Decision Support Systems
Decision support system (DSS)
Arrangement of computerized tools
Used to assist managerial decision
Extensive data “massaging” to produce information
Used at all levels in organization
Tailored to focus on specific areas and needs
Interactive
Provides ad hoc query tools
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

6. DSS Components
Figure 13.1
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

7. Operational vs. Decision Support Data
Operational data
Relational, normalized database
Optimized to support transactions
Real time updates
DSS
Snapshot of operational data
Summarized
Large amounts of data
Data analyst viewpoint
Timespan
Granularity
Dimensionality
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

8. The DSS Database Requirements
Database schema
Support complex (non-normalized) data
Extract multidimensional time slices
Data extraction and filtering
End-user analytical interface
Database size
Very large databases (VLDBs)
Contains redundant and duplicated data
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

9. Data Warehouse Integrated Subject-Oriented Time Variant Non-Volatile
Centralized
Holds data retrieved from entire organization
Subject-Oriented
Optimized to give answers to diverse questions
Used by all functional areas
Time Variant
Flow of data through time
Projected data
Non-Volatile
Data never removed
Always growing
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

10. Creating a Data Warehouse
Figure 13.3
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

11. Data Marts Single-subject data warehouse subset
Decision support to small group
Can be test for exploring potential benefits of Data warehouses
Address local or departmental problems
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

12. DSS Architectural Styles
Traditional mainframe-based OLTP
Managerial information system (MIS) with 3GL
First-generation departmental DSS
First-generation enterprise data warehouse using RDMS
Second-generation data warehouse using MDBMS
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

13. Twelve Data Warehouse Rules
1. Separated from operational environment
2. Data are integrated
3. Contains historical data over long time horizon
4. Snapshot data captured at given time
5. Subject-oriented data
6. Mainly read-only data with periodic batch updates from operational source, no online updates
7. Development life cycle differs from classical one, data driven not process driven
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

14. Twelve Data Warehouse Rules (Con’t.)
8. Contains different levels of data detail
Current and old detail
Lightly and highly summarized
9. Characterized by read-only transactions to large data sets
10. Environment has system to trace data resources, transformation, and storage
11. Metadata critical components
Identify and define data elements
Provide the source, transformation, integration, storage, usage, relationships, and history of data elements
12. Contains charge-back mechanism for usage
Enforces optimal use of data
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

15. Online Analytical Processing (OLAP)
Advanced data analysis environment
Supports decision making, business modeling, and operations research activities
Characteristics of OLAP
Use multidimensional data analysis techniques
Provide advanced database support
Provide easy-to-use end-user interfaces
Support client/server architecture
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

16. OLAP Client/Server Architecture
Figure 13.6
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

17. OLAP Server Arrangement
Figure 13.7
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

18. OLAP Server with Multidimensional Data Store Arrangement
Figure 13.8
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

19. OLAP Server with Local Mini-Data-Marts
Figure 13.9
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

20. Relational OLAP (ROLAP)
OLAP functionality
Uses relational DB query tools
Extensions to RDBMS
Multidimensional data schema support
Data access language and query performance optimized for multidimensional data
Support for very large databases (VLDBs)
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

21. Typical ROLAP Client/Server Architecture
Figure 13.10
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

22. Multidimensional OLAP (MOLAP)
OLAP functionality to multidimensional databases (MDBMS)
Stored data in multidimensional data cube
N-dimensional cubes called hypercubes
Cube cache memory speeds processing
Affected by how the database system handles density of data cube called sparsity
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

23. MOLAP Client/Server Architecture
Figure 13.11
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

24. Star Schema Data-modeling technique
Maps multidimensional decision support into relational database
Yield model for multidimensional data analysis while preserving relational structure of operational DB
Four Components:
Facts
Dimensions
Attributes
Attribute hierarchies
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

25. Simple Star Schema Figure 13.12
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

26. Slice and Dice View of Sales
Figure 13.14
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

27. Star Schema Representation
Facts and dimensions represented by physical tables in data warehouse DB
Fact table related to each dimension table (M:1)
Fact and dimension tables related by foreign keys
Subject to the primary/foreign key constraints
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

28. Star Schema for Sales Figure 13.17
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

29. Performance-Improving Techniques for Star Schema
Normalization of dimensional tables
Multiple fact tables representing different aggregation levels
Denormalization of the fact tables
Table partitioning and replication
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

30. Data Warehouse Implementation Road Map
Figure 13.21
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

31. Data Mining Seeks to discover unknown data characteristics
Automatically searches data for anomalies and relationships
Data mining tools
Analyze data
Uncover problems or opportunities
Form computer models based on findings
Predict business behavior with models
Require minimal end-user intervention
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

32. Extraction of Knowledge from Data
Figure 13.22
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel

33. Data Mining Process Figure 13.23
Database Systems: Design, Implementation, & Management, 5th Edition, Rob & Coronel