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A Genetic Selection Algorithm for OLAP Data Cubes

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Abstract

Multidimensional data analysis, as supported by OLAP (online analytical processing) systems, requires the computation of many aggregate functions over a large volume of historically collected data. To decrease the query time and to provide various viewpoints for the analysts, these data are usually organized as a multidimensional data model, called data cubes. Each cell in a data cube corresponds to a unique set of values for the different dimensions and contains the metric of interest. The data cube selection problem is, given the set of user queries and a storage space constraint, to select a set of materialized cubes from the data cubes to minimize the query cost and/or the maintenance cost. This problem is known to be an NP-hard problem. In this study, we examined the application of genetic algorithms to the cube selection problem. We proposed a greedy-repaired genetic algorithm, called the genetic greedy method. According to our experiments, the solution obtained by our genetic greedy method is superior to that found using the traditional greedy method. That is, within the same storage constraint, the solution can greatly reduce the amount of query cost as well as the cube maintenance cost.

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Authors and Affiliations

  1. Department of Information Management, I-Shou University, Kaohsiung, Taiwan

    Wen-Yang Lin

  2. Institute of Information Engineering, I-Shou University, Kaohsiung, Taiwan

    I-Chung Kuo

  3. Department of Information Management, I-Shou University, Kaohsiung ROC. , 84008, Taiwan

    Wen-Yang Lin

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  1. Wen-Yang Lin
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  2. I-Chung Kuo
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Correspondence to Wen-Yang Lin.

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Lin, WY., Kuo, IC. A Genetic Selection Algorithm for OLAP Data Cubes. Knowledge and Information Systems 6, 83–102 (2004). https://doi.org/10.1007/s10115-003-0093-x

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  • DOI: https://doi.org/10.1007/s10115-003-0093-x

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