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Fox MISSpring 2011
Data Warehouse
Week 8Introduction of Data Warehouse
Multidimensional Analysis: OLAP
Data Warehouse
• Integrated, Subject-Oriented, Time-Variant, Nonvolatile database that provides support for decision making
Characteristics of Data Warehouse• Integrated
– Centralized– Holds data retrieved from entire organization
• Time Variant – Flow of data through time– Projected data
• Non-Volatile – Data never removed– Always growing
• Subject-Oriented – Optimized to give answers to diverse questions– Used by all functional areas
• 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
Online Analytical Processing (OLAP)
Multidimensional View of Sales• Multidimensional analysis involves viewing data
simultaneously categorized along potentially many dimensions
Data Warehouse Modeling: Star Schema
• Data-modeling technique • Also called star-join schema, data cube, or multi-dimensional
schema• The simplest style of data warehouse schema. • The star schema consists of one or more fact tables referencing any
number of dimension tables• Maps multidimensional decision support into relational database• Yield model for multidimensional data analysis while preserving
relational structure of operational DB
• Facts– The fact table holds the main data. It includes a large amount of
aggregated data, such as price and units sold• Dimensions
– Dimension tables, which are usually smaller than fact tables, include the attributes that describe the facts.
• Attributes