data mining

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Topics: Data warehouse
4 Components of DSS (Decision Support System)
Data Store – The DSS Database
Data Extraction and Filtering
End-User Query Tool
End User Presentation Tools
Operational
Stored in Normalized Relational Database
Support transactions that represent daily operations (Not Query Friendly)
Differences with DSS
3 Main Differences
Time Span
Granularity
Dimensionality

Operational
DSS
Time span
Real time
Historic

Current transaction

Short time frame
Long time frame

Specific Data facts
Patterns
Granularity
Specific transaction that occur at a given time
Shown at different levels of aggregation

Different summary levels
-Decompose (drill down)
-Summarize(roll up)
Dimensionality
Represents atomic transactions
Data is related in many ways

Develop the larger picture

Multi-dimensional view of data

DSS Database Requirements
Data Extraction and Filtering
End User Analytical Interface
Must support advanced data modeling and data presentation tools
Data analysis tools
Query generation
Must Allow the User to Navigate through the DSS
Size Requirements
VERY Large – Terabytes
Advanced Hardware (Multiple processors, multiple disk arrays, etc.)
Data Warehouse
Definition: Integrated, Subject-Oriented, Time-Variant, Nonvolatile database that provides support for decision making
Integrated: The data warehouse is a centralized, consolidated database that integrated data derived from the entire organization
Subject oriented: Data is arranged and optimized to provide answer to questions from diverse functional areas. Data is organized and summarized by topic(Sales / Marketing / Finance / Distribution / Etc. )
Time Variant: represents the flow of data through time
Can contain projected data from statistical models
Data is periodically uploaded then time-dependent data is recomputed
Non-volatile: Once data is entered it is NEVER removed
Data warehouse architecture
Generic 2-level architecture
Data Marts: Small, Single-Subject data warehouse subset that provides decision support to a small

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