OLAP OLAP. Data Warehouse. OLAP Data Model: the Data Cube S e s s io n

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1 OLAP OLAP On-Line Analytical Processing In contrast to on-line transaction processing (OLTP) Mostly ad hoc queries involving aggregation Response time rather than throughput is the main performance measure. The database is often a data warehouse. Data Warehouse Usually a set of views of many data sources, including databases managed by differing database systems and nondatabases Tend to be huge, historical data sets. Data usually need not be completely upto-date. OLAP Data Model: the Data Cube S e s s io n m o rn in g e v e n in g to ta l O p e ra to r o o to ta l

2 SQL for Simple 2-Dimensional Cube Relational View of the Simple SQL Query SELECT Sales.operatorID, Times.session, Sum(Sales.totalPrice) AS SumOfTotalPrice FROM Times INNER JOIN Sales ON Times.timeID = Sales.timeID GROUP BY Sales.operatorID, Times.session operatorid session SumOfTotalPrice o1 morning $80.00 o2 evening $50.00 o2 morning $ o3 evening $ Multi-dimensional view of the simple SQL Query session morning evening operatorid o1 $80.00 o2 $ $50.00 o3 $ SQL for 2-Dimensional Cube SELECT Sales.operatorID, Times.session, Sum(Sales.totalPrice) AS SumOfTotalPrice GROUP BY Sales.operatorID, Times.session SELECT Sales.operatorID, 'ALL', Sum(Sales.totalPrice) AS SumOfTotalPrice GROUP BY Sales.operatorID SELECT 'ALL', Times.session, Sum(Sales.totalPrice) AS SumOfTotalPrice GROUP BY Times.session SELECT 'ALL', 'ALL', Sum(Sales.totalPrice) AS SumOfTotalPrice ; 2

3 Relational View of the Complex SQL Query operatorid session SumOfTotalPrice ALL ALL $ ALL evening $ ALL morning $ o1 ALL $80.00 o1 morning $80.00 o2 ALL $ o2 evening $50.00 o2 morning $ o3 ALL $ o3 evening $ Multi-dimensional view of the Complex SQL Query session morning evening ALL operatorid o1 $80.00 $80.00 o2 $ $50.00 $ o3 $ $ ALL $ $ $ A Three-Dimensional Cube Cubes of k Dimensions Ordinary data area has size dom(a1) x dom(a2) x x dom(ak) Total SQL Select statements in is 2 k 3

4 Physical Data Model 1: ROLAP Multidimensional views built on top of relational system Extensions to relational operators (e.g., CUBE, top five, ) needed Relation schemas typically chosen via star or snowflake design Advantage: extension of relational systems, including support for relations Fact Table Star Schemas Dimension Table Normalizing dimension tables (e.g., Times) yields snowflake schema. Dimensions Are Hierarchies Time, for example, can be measured in various units: session Time-id season-year year month-year month Moving up the hierarchy is drilling down. Moving down the hierarchy is rolling up. Physical Data Model 2: MOLAP Data stored as very large, typically sparse, arrays Data compression needed Advantage: typical OLAP queries supported very efficiently Disadvantage: SQL typically not supported 4

5 Evaluation Techniques Redundancy of values in data cube a principal concern: do not compute 2 k -way union! Many techniques based on properties of aggregation functions (Gray et al.): distributive functions f() have property: there is a g() such that g(f(x1,, Xk), f(y1,, Yn)) = f(x1,, Xk,Y1,, Yn) (ex.: sum, min) algebraic functions can also be computed incrementally (ex.: avg() ) holistic functions cannot be computed incrementally (ex.: median, rank) Conclusions OLAP is big business. ROLAP vs. MOLAP is generating a lot of PR effort. Oracle supports both ROLAP (SQL:1999 OLAP extensions) and MOLAP (Analytical Workspace). OLAP implementation techniques are a hot research area. 5