Improving estimation accuracy of aggregate queries on data cubes

  • Authors:

  • Elaheh Pourabbas;Arie Shoshani

  • Affiliations:

  • National Research Council, Rome, Italy;Lawrence Berkeley National Laboratory, Berkeley, CA, USA

  • Venue:
  • Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
  • Year:
  • 2008

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Abstract

In this paper, we investigate the problem of estimation of a target database from summary databases derived from a base data cube. We show that such estimates can be derived by choosing a primary database which uses a proxy database to estimate the results. This technique is common in statistics, but an important issue we are addressing is the accuracy of these estimates. Specifically, given multiple primary and multiple proxy databases, that share the same summary measure, the problem is how to select the primary and proxy databases that will generate the most accurate target database estimation possible. We propose an algorithmic approach for determining the steps to select or compute the source databases from multiple summary databases, which makes use of the principles of information entropy. We show that the source databases with the largest number of cells in common provide the more accurate estimates. We prove that this is consistent with maximizing the entropy. We provide some experimental results on the accuracy of the target database estimation in order to verify our results.