The HGEDA Hybrid Algorithm for OLAP Data Cubes

Abstract:

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On-Line Analytical Processing (OLAP) tools are frequently used in business, science and health to extract useful knowledge from massive databases. An important and hard optimization problem in OLAP data warehouses is the view selection problem, consisting of selecting a set of aggregate views of the data for speeding up future query processing. In this paper we present a new approach, named HGEDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. Experimental results show that the HGEDA are competitive with the genetic algorithm on a variety of problem instances, often finding approximate optimal solutions in a reasonable amount of time.

Info:

Periodical:

Edited by:

Han Zhao

Pages:

3158-3162

DOI:

10.4028/www.scientific.net/AMM.130-134.3158

Citation:

C. Jia and H. W. Chen, "The HGEDA Hybrid Algorithm for OLAP Data Cubes", Applied Mechanics and Materials, Vols. 130-134, pp. 3158-3162, 2012

Online since:

October 2011

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Price:

$35.00

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