Data Warehouse and OLAP

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Data Warehouse and OLAP

• Data Mining Concepts and Techniques
• by J. Han and M. Kamber

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What is a data warehouse?

• A repository of information collected from
  multiple sources, stored under a unified schema
  at a single site
• Characteristics
• Subject-oriented
• Integrated
• Time-variant
• Nonvolatile
• A semantically consistent data store for decision
  support at enterprise level

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Data warehousing and a multidimensional data model

• DWing - the process of constructing and using DW.
• OLTP and OLAP user system orientation, data
  contents, database design, view, access patterns
  (Table 2.1)
• A DW is usually modeled by a multidimensional
  database structure - a data cube
• An example of a data cube of Student Information
• dimensions nationality, level, status,

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Schemas for multidimensional databases

• OLTP and OLAP on the same databases(?)
• achieving high performance of both systems
• Dimensions are the perspectives or entities
• Facts are numeric measures
• Dimension table and fact table
• DW schemas
• Star
• Snowflakes
• Fact constellations

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Examples for defining schemas

• define cube, define dimension asin
• Star
• Fig. 2.4
• Example 2.4
• Snowflake
• Fig. 2.5
• Example 2.5
• Fact constellation
• Fig. 2.6
• Example 2.6

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OLAP operations

• Concept hierarchy
• one for each dimension
• Operations (Fig. 2.10)
• Dice
• Slice
• Pivot
• Roll-up
• Drill-down

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Data Warehouse Architecture

• A 3-tier data warehouse architecture
• Front-end tools
• OLAP server
• Data warehouse server
• Three models
• Enterprise warehouse
• Data mart
• Virtual warehouse

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Metadata Repository

• When used in DW, metadata are the data that
  define warehouse objects
• a directory to help the decision support system
  analyst locate the contents of the DW
• a guide to the mapping of data from the source to
  the DW
• a guide to the algorithms used for summarization
• A metadata repository contains
• the DW structure, operational metadata,
  algorithms used, mapping, data related to system
  performance, business metadata

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From DW to DM

• DW back-end tools and utilites
• data extraction, cleaning, transformation, load,
  refresh
• The uses of DW
• generate reports and answer predefined queries
• analyze summarized and detailed data
• performing multidimensional analysis
• knowledge discovery and strategic decision making
  using data mining
• Information, analytical processing and DM