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Efficient Computation of Data Cubes and Aggregate Views

Efficient Computation of Data Cubes and Aggregate Views

Leonardo Tininini
Copyright: © 2005 |Pages: 6
ISBN13: 9781591405573|ISBN10: 1591405572|EISBN13: 9781591405597
DOI: 10.4018/978-1-59140-557-3.ch080
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MLA

Tininini, Leonardo. "Efficient Computation of Data Cubes and Aggregate Views." Encyclopedia of Data Warehousing and Mining, edited by John Wang, IGI Global, 2005, pp. 421-426. https://doi.org/10.4018/978-1-59140-557-3.ch080

APA

Tininini, L. (2005). Efficient Computation of Data Cubes and Aggregate Views. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining (pp. 421-426). IGI Global. https://doi.org/10.4018/978-1-59140-557-3.ch080

Chicago

Tininini, Leonardo. "Efficient Computation of Data Cubes and Aggregate Views." In Encyclopedia of Data Warehousing and Mining, edited by John Wang, 421-426. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-557-3.ch080

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Abstract

This paper reviews the main techniques for the efficient calculation of aggregate multidimensional views and data cubes, possibly using specifically designed indexing structures. The efficient evaluation of aggregate multidimensional queries is obviously one of the most important aspects in data warehouses (OLAP systems). In particular, a fundamental requirement of such systems is the ability to perform multidimensional analyses in online response times. As multidimensional queries usually involve a huge amount of data to be aggregated, the only way to achieve this is by pre-computing some queries, storing the answers permanently in the database and reusing these almost exclusively when evaluating queries in the multidimensional database. These pre-computed queries are commonly referred to as materialized views and carry several related issues, particularly how to efficiently compute them (the focus of this paper), but also which views to materialize and how to maintain them.

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