Transcript Datawarehouse_ch13

13
Chapter 13
The Data Warehouse
Hachim Haddouti
13
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
• ROLAP, MOLAP
• What data mining is and what role it plays in
decision support
Hachim Haddouti and Rob & Coronel, Ch13
2
13
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)
Hachim Haddouti and Rob & Coronel, Ch13
3
13
Decision
Support Systems
• Decision Support
– Is a methodology
– Extracts information from data
– Uses information as basis for decision making
Hachim Haddouti and Rob & Coronel, Ch13
4
13
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
Hachim Haddouti and Rob & Coronel, Ch13
5
13
DSS Components
Hachim Haddouti and Rob & Coronel, Ch13
6
13
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
Hachim Haddouti and Rob & Coronel, Ch13
7
13
History
Unchanged Vision:
right information
to the right time and place
MIS (=Management Informationssystem)
DSS (=Decision
Support System)
MAIS (=Marketing Informationssystem)
EIS (=Executive
Information
System)
60' 70'
80', Begin 90'
Hachim Haddouti and Rob & Coronel, Ch13
Data-Warehousesystem
EIS (=Enterprise Intelligence
System)
IDF (=Information Delivery
Facility)
Information
Warehouse
EIS (=Enterprise Information
System)
Mid 90'
8
13
Data Warehouse
• Integrated
– 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
Hachim Haddouti and Rob & Coronel, Ch13
9
13
Creating a Data Warehouse
Hachim Haddouti and Rob & Coronel, Ch13
10
13
Data Warehouse Shape
?
Purchase
Storage
Sales
Personnel
Financial
Internal Information Sources
Data Warehouse
External information sources
Customer
Supllier
Hachim Haddouti and Rob & Coronel, Ch13
Market
competition
11
13
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
Hachim Haddouti and Rob & Coronel, Ch13
12
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
Hachim Haddouti and Rob & Coronel, Ch13
13
13
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
Hachim Haddouti and Rob & Coronel, Ch13
14
13
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
Hachim Haddouti and Rob & Coronel, Ch13
15
OLAP Client/Server Architecture
Hachim Haddouti and Rob & Coronel, Ch13
13
16
OLAP Server Arrangement
Hachim Haddouti and Rob & Coronel, Ch13
13
17
OLAP Server with Multidimensional
Data Store Arrangement
Hachim Haddouti and Rob & Coronel, Ch13
13
18
OLAP Server with
Local Mini-Data-Marts
Hachim Haddouti and Rob & Coronel, Ch13
13
19
13
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)
Hachim Haddouti and Rob & Coronel, Ch13
20
Typical ROLAP
Client/Server Architecture
Hachim Haddouti and Rob & Coronel, Ch13
13
21
13
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
Hachim Haddouti and Rob & Coronel, Ch13
22
MOLAP Client/Server Architecture
Hachim Haddouti and Rob & Coronel, Ch13
13
23
13
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
Hachim Haddouti and Rob & Coronel, Ch13
24
13
Simple Star Schema
Figure 13.12
Hachim Haddouti and Rob & Coronel, Ch13
25
13
Slice and Dice View of Sales
Hachim Haddouti and Rob & Coronel, Ch13
26
13
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
Hachim Haddouti and Rob & Coronel, Ch13
27
13
Star Schema for Sales
Hachim Haddouti and Rob & Coronel, Ch13
28
13
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
Hachim Haddouti and Rob & Coronel, Ch13
29
13
Extraction of Knowledge from Data
Hachim Haddouti and Rob & Coronel, Ch13
30
13
Example 1
Hachim Haddouti and Rob & Coronel, Ch13
31
13
Example 2
Hachim Haddouti and Rob & Coronel, Ch13
32