Abstract
OLAP databases remain the first choice of enterprises to store and analyze huge amount of data. Thereby, to further enhance query performances and minimize the maintenance cost, many techniques exist, among which data partitioning is considered as an efficient technique to achieve this purpose. Although most of business intelligence tools support this feature, defining an appropriate partitioning strategy remains a big challenge. Hence, many approaches have been proposed in the literature. Nevertheless, most of them have been evaluated only in relational model. Therefore, we propose in this paper, a comparative study between our partitioning approach based on the association rules algorithm and a genetic based one. The study aims to compare the results of the aforementioned approaches in case of OLAP partitioning.
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Letrache, K., El Beggar, O., Ramdani, M. (2019). Comparative Analysis of Our Association Rules Based Approach and a Genetic Approach for OLAP Partitioning. In: Podelski, A., Taïani, F. (eds) Networked Systems. NETYS 2018. Lecture Notes in Computer Science(), vol 11028. Springer, Cham. https://doi.org/10.1007/978-3-030-05529-5_28
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DOI: https://doi.org/10.1007/978-3-030-05529-5_28
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