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On-Line Analytic Processing Chetan Meshram Class Id:221 Agenda Introduction Multidimensional View of OLAP Data Star Schemas Examples Slicing and Dicing Conclusion References Introduction - OLAP Provides quick answers to analytical queries that are multi-dimensional in nature. Generally involves highly complex queries that use aggregations. OLAP or Decision-support Queries examine large data. Applications: business reporting for sales, marketing, budgeting and forecasting, financial reporting etc. OLAP Applications Common OLAP application uses Warehouse of sales data Queries that aggregates sales into groups and identify significant groups Example: Schema for Warehouse: Sales(serialNo, date, dealer, price ) Autos(serialNo, model, color) Dealers(name, city, state, phone) OLAP Applications Query: SELECT state, AVG(price) FROM Sales, Dealers Where Sales.dealer = Dealers.name AND date>= ‘2001-01-04’ Group BY state; Query classifies recent Sales by state of the dealer and touches large amount of data OLTP Query: Bank Deposists, Air Line Reservations Touches only tiny portion of the database Ex: Find price at which auto with serial number 123 was sold, touches only a single tuple of data. Multidimensional OLAP Fact Table: Central relation or collection of data arranged in a multidimensional space or cube Dimensions: car, dealer and date Point represents sale of automobile Dimensions represent properties of sale. Multidimensional Space Data Cube Cars Dealers Date Multidimensional OLAP Types: ROLAP: Relational OLAP Data is stored in relations with a specialized structure called ‘Star Schema’. Fact Table contains raw or unaggregated data Other relations contains values along each dimension MOLAP: Multidimensional OLAP A specialized structure called “Data Cube” is used to hold data and its aggregates. Nonrelational operators implemented by system. Star Schemas Schema for the fact table which links to other relations called “dimension tables”. Fact table is at the centre of the “star” whose points are the dimension tables. Fact table consists of dimensions and dependent attributes Ex: Sales(serialNo, date, dealer, price) serialNo, date and dealer are dimensions Price is dependent attribute Star Schemas Example: Dimension tables describe values along each dimension Dimension attribute of fact table is a foreign key of corresponding dimension table Suggest possible groupings in an SQL GROUP BY query Star Schema: Star Schemas Example: Dimension Table: Autos(serialNo, model, color) Dealers(name, city, state, phone) Fact Table: Sales(serialNo, date, dealer, price) serialNo is a foreign key referencing serialNo of Autos Autos.model and Autos.color can be used to group sales in interesting ways. Breakdown of sales by color, or by dealer. Slicing and Dicing Refers to ability to look at the database from different viewpoints Performed along time axis to analyze trends and find patterns. Choice of partition for each dimension “dices” the data cube into smaller cubes GROUP BY and WHERE clause , a query focuses on particular partitions. Slicing and Dicing Example SELECT color, SUM(price) FROM Sales NATURAL JOIN Autos WHERE model = ‘Sedan’ GROUP BY color; Query dices by color and slices by model User Patterns and trends can be understood. References http://en.wikipedia.org/wiki/Online_analyt ical_processing http://en.wikipedia.org/wiki/OLAP_cube http://www.akadia.com/services/ora_olap _dimensions.html Questions?