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CH-20 Information Integration

CH-20 Information Integration. Continued……. What is OLAP? On-Line Analytic Processing (OLAP) OLAP is used to query very large amount of data in the data warehouse of company It involves highly complex queries that use one or more aggregators

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CH-20 Information Integration

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  1. CH-20 Information Integration Continued……

  2. What is OLAP? • On-Line Analytic Processing (OLAP) • OLAP is used to query very large amount of data in the data warehouse of company • It involves highly complex queries that use one or more aggregators • OLAP queries are also called as decision support queries • What is OLTP? • Common database operation touch very small amount of data and they are referred as OLTP (online transaction processing) • OLAP queries are considered as long transactions and long transactions locking the entire database would shutdown the ordinary OLTP transactions so OLAP data is stored separately in data warehouse rather then in ordinary database

  3. Example of OLAP and OLTP queries Consider data warehouse of automobile company the schema can be as follows Sales (serialNo, date, dealer, price) Autos (serialNo, model, color) Dealers( name, city, state, phone) The typical OLAP query can be for finding the average sales price by state SELECT state, AVG (price) FROM Sales, Dealers WHERE Sales.dealer = Dealers.name AND date >= ‘2001-01-04’ GROUP BY state; In same example the typical OLTP query can be for finding the price at which the auto with serial number 123 was sold

  4. Multidimensional view of OLTP data : • In typical OLAP application we have a central relation called fact table. • Fact table represents events or objects of interest. • It is helpful to think that objects in the fact table are arranged in the multidimensional space. • consider the earlier example of automobile company the fact table can be build for sales which is the object of interest and is viewed as a 3 dimensional data cube .

  5. Multidimensional View Of OLAP Data Each single point in cube represents sales of single automobile and dimension represents properties of sales.

  6. Star Schemas A star schema consist of schema for the fact table, which links to several other relations called “dimension tables”.

  7. Slicing and Dicing • The row data cube can be partitioned along each dimension at some level of granularity for analysis this partitioning operations are known as slicing and dicing. • In SQL this partitioning is done by “ GROUP BY” clause.

  8. Lets consider the automobile example. suppose car named Gobi is not selling well and we want to find exactly which colors are not doing well • SQL query is as follows: • SELECT color, SUM (Price) • FROM Sales NATURAL JOIN Autos • WHERE model = ‘Gobi’ • GROUP BY color; • This query dice by color and slice by model, Focusing on particular model, the Gobi, and ignores other data.

  9. Thank You !

  10. References: Database systems the complete book by Garcia,Ullman,Windom

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