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Implementing Data Cubes Efficiently

Harinarayan, V. and Rajaraman, A. and Ullman, J. (1995) Implementing Data Cubes Efficiently. Technical Report. Stanford InfoLab. (Publication Note: ACM International Conference on Management of Data, Montreal (SIGMOD 1996), June 4-6, 1996, Quebec, Canada. Best Paper Award.)




Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube. A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We then present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query

Item Type:Techreport (Technical Report)
Subjects:Computer Science > Data Integration and Mediation
Computer Science > Query Processing
Projects:Information Integration
Related URLs:Project Homepage
ID Code:102
Deposited By:Import Account
Deposited On:25 Feb 2000 16:00
Last Modified:14 Jan 2009 14:27

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