Transcript Chapter 13

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Chapter 13
The Data Warehouse
Database Systems: Design, Implementation, and
Management, Fifth Edition, Rob and Coronel
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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
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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)
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Decision
Support Systems
• Decision Support
– Is a methodology
– Extracts information from data
– Uses information as basis for decision making
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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
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DSS Components
Figure 13.1
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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
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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
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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
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Creating a Data Warehouse
Figure 13.3
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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
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DSS Architectural Styles
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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
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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
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Twelve Data Warehouse Rules (Con’t.)
8. Contains different levels of data detail
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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
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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
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Enforces optimal use of data
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Online Analytical
Processing (OLAP)
• Advanced data analysis environment
• Supports decision making, business modeling,
and operations research activities
• Characteristics of OLAP
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Use multidimensional data analysis techniques
Provide advanced database support
Provide easy-to-use end-user interfaces
Support client/server architecture
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OLAP Client/Server Architecture
Figure 13.6
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OLAP Server Arrangement
Figure 13.7
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OLAP Server with Multidimensional
Data Store Arrangement
Figure 13.8
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OLAP Server with
Local Mini-Data-Marts
Figure 13.9
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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)
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Typical ROLAP
Client/Server Architecture
Figure 13.10
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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
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MOLAP Client/Server Architecture
Figure 13.11
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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:
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Facts
Dimensions
Attributes
Attribute hierarchies
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Simple Star Schema
Figure 13.12
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Slice and Dice View of Sales
Figure 13.14
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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
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Star Schema for Sales
Figure 13.17
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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
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Data Warehouse
Implementation Road Map
Figure 13.21
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Data Mining
• Seeks to discover unknown data characteristics
• Automatically searches data for anomalies and
relationships
• Data mining tools
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Analyze data
Uncover problems or opportunities
Form computer models based on findings
Predict business behavior with models
Require minimal end-user intervention
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Extraction of Knowledge from Data
Figure 13.22
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Data Mining Process
Figure 13.23
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