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Salahaddin University-Erbil
Salahaddin University-Erbil
TechnologyBusiness
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On-Line Application Processing Warehousing Data Cubes Data Mining
Overview ,[object Object],[object Object],[object Object]
The Data Warehouse ,[object Object],[object Object],[object Object],[object Object]
OLTP ,[object Object],[object Object],[object Object]
OLAP ,[object Object],[object Object],[object Object]
OLAP Examples ,[object Object],[object Object]
Common Architecture ,[object Object],[object Object],[object Object]
Star Schemas ,[object Object],[object Object],[object Object],[object Object]
Example: Star Schema ,[object Object],[object Object],[object Object]
Example, Continued ,[object Object],[object Object],[object Object],[object Object]
Visualization – Star Schema Dimension Table (Beers) Dimension Table (etc.) Dimension Table (Drinkers) Dimension Table (Bars) Fact Table - Sales Dimension Attrs. Dependent Attrs.
Dimensions and Dependent Attributes ,[object Object],[object Object],[object Object]
Example: Dependent Attribute ,[object Object],[object Object]
Approaches to Building Warehouses ,[object Object],[object Object]
ROLAP Techniques ,[object Object],[object Object]
Typical OLAP Queries ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Typical OLAP Queries --- (2) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example: OLAP Query ,[object Object],[object Object],[object Object],[object Object]
Example: In SQL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using Materialized Views ,[object Object],[object Object]
Example: Materialized View ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example --- Continued ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Since bar -> addr and beer -> manf, there is no real grouping. We need addr and manf in the SELECT.
Example --- Concluded ,[object Object],[object Object],[object Object],[object Object],[object Object]
MOLAP and Data Cubes ,[object Object],[object Object],[object Object]
Visualization - Data Cubes price bar beer drinker
Marginals ,[object Object],[object Object]
Visualization - Data Cube w/ Aggregation price bar beer drinker SUM over all Drinkers
Example: Marginals ,[object Object],[object Object]
Structure of the Cube ,[object Object],[object Object],[object Object]
Drill-Down ,[object Object],[object Object]
Roll-Up ,[object Object],[object Object]
Roll Up and Drill Down $ of Anheuser-Busch by drinker/bar $ of A-B / drinker Roll up by Bar $ of A-B Beers / drinker Drill down by Beer 40 31 38 Blue Chalk 42 36 50 Nut- House 30 33 45 Joe’s Bar Mary Bob Jim 112 100 133 Mary Bob Jim 35 40 48 Bud Light 37 31 45 M’lob 40 29 40 Bud Mary Bob Jim
Materialized Data-Cube Views ,[object Object],[object Object]
Example ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining ,[object Object],[object Object],[object Object],[object Object]
Market-Basket Data ,[object Object],[object Object]
Example: Market Baskets ,[object Object],[object Object],[object Object]
Finding Frequent Pairs ,[object Object],[object Object],[object Object]
Frequent Pairs in SQL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Look for two Basket tuples with the same basket and different items. First item must precede second, so we don’t count the same pair twice. Create a group for each pair of items that appears in at least one basket. Throw away pairs of items that do not appear at least s times.
A-Priori Trick --- (1) ,[object Object],[object Object]
A-Priori Trick --- (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Items that appear in at least s baskets.
A-Priori Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example: A-Priori ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Olap

  • 1. On-Line Application Processing Warehousing Data Cubes Data Mining
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  • 11. Visualization – Star Schema Dimension Table (Beers) Dimension Table (etc.) Dimension Table (Drinkers) Dimension Table (Bars) Fact Table - Sales Dimension Attrs. Dependent Attrs.
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  • 25. Visualization - Data Cubes price bar beer drinker
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  • 27. Visualization - Data Cube w/ Aggregation price bar beer drinker SUM over all Drinkers
  • 28.
  • 29.
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  • 31.
  • 32. Roll Up and Drill Down $ of Anheuser-Busch by drinker/bar $ of A-B / drinker Roll up by Bar $ of A-B Beers / drinker Drill down by Beer 40 31 38 Blue Chalk 42 36 50 Nut- House 30 33 45 Joe’s Bar Mary Bob Jim 112 100 133 Mary Bob Jim 35 40 48 Bud Light 37 31 45 M’lob 40 29 40 Bud Mary Bob Jim
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