On-Line Analytic Processing Chetan Meshram Class Id:221

Agenda  Introduction  Multidimensional View of OLAP Data  Star Schemas Examples  Slicing and Dicing  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.

 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>= ‘ ’ Group BY state; Query classifies recent Sales by state of the dealer and touches large amount of data  OLTP :Online Transaction Processing Bank Deposits, 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 Date Dealers Cars

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

References  ical_processing   _dimensions.html

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