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ROLAP implementations of the data cube

ROLAP implementations of the data cube Implementation of the data cube is an important and scientifically interesting issue in On-Line Analytical Processing (OLAP) and has been the subject of a plethora of related publications. Naive implementation methods that compute each node separately and store the result are impractical, since they have exponential time and space complexity with respect to the cube dimensionality. To overcome this drawback, a wide range of methods that provide efficient cube implementation (with respect to both computation and storage) have been proposed, which make use of relational, multidimensional, or graph-based data structures. Furthermore, there are several other methods that compute and store approximate descriptions of data cubes, sacrificing accuracy for condensation. In this article, we focus on Relational-OLAP (ROLAP), following the majority of the efforts so far. We review existing ROLAP methods that implement the data cube and identify six orthogonal parameters/dimensions that characterize them. We place the existing techniques at the appropriate points within the problem space defined by these parameters and identify several clusters that the techniques form with various interesting properties. A careful study of these properties leads to the identification of particularly effective values for the space parameters and indicates the potential for devising new algorithms with better overall performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Computing Surveys (CSUR) Association for Computing Machinery

ROLAP implementations of the data cube

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References (61)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2007 by ACM Inc.
ISSN
0360-0300
DOI
10.1145/1287620.1287623
Publisher site
See Article on Publisher Site

Abstract

Implementation of the data cube is an important and scientifically interesting issue in On-Line Analytical Processing (OLAP) and has been the subject of a plethora of related publications. Naive implementation methods that compute each node separately and store the result are impractical, since they have exponential time and space complexity with respect to the cube dimensionality. To overcome this drawback, a wide range of methods that provide efficient cube implementation (with respect to both computation and storage) have been proposed, which make use of relational, multidimensional, or graph-based data structures. Furthermore, there are several other methods that compute and store approximate descriptions of data cubes, sacrificing accuracy for condensation. In this article, we focus on Relational-OLAP (ROLAP), following the majority of the efforts so far. We review existing ROLAP methods that implement the data cube and identify six orthogonal parameters/dimensions that characterize them. We place the existing techniques at the appropriate points within the problem space defined by these parameters and identify several clusters that the techniques form with various interesting properties. A careful study of these properties leads to the identification of particularly effective values for the space parameters and indicates the potential for devising new algorithms with better overall performance.

Journal

ACM Computing Surveys (CSUR) – Association for Computing Machinery

Published: Nov 2, 2007

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