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Lossless Reduction of Datacubes using Partitions

Lossless Reduction of Datacubes using Partitions

Alain Casali, Sébastien Nedjar, Rosine Cicchetti, Lotfi Lakhal, Noël Novelli
Copyright: © 2009 |Volume: 5 |Issue: 1 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202010|EISSN: 1548-3924|DOI: 10.4018/jdwm.2009010102
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MLA

Casali, Alain, et al. "Lossless Reduction of Datacubes using Partitions." IJDWM vol.5, no.1 2009: pp.18-35. http://doi.org/10.4018/jdwm.2009010102

APA

Casali, A., Nedjar, S., Cicchetti, R., Lakhal, L., & Novelli, N. (2009). Lossless Reduction of Datacubes using Partitions. International Journal of Data Warehousing and Mining (IJDWM), 5(1), 18-35. http://doi.org/10.4018/jdwm.2009010102

Chicago

Casali, Alain, et al. "Lossless Reduction of Datacubes using Partitions," International Journal of Data Warehousing and Mining (IJDWM) 5, no.1: 18-35. http://doi.org/10.4018/jdwm.2009010102

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Abstract

Datacubes are especially useful for answering efficiently queries on data warehouses. Nevertheless the amount of generated aggregated data is huge with respect to the initial data which is itself very large. Recent research has addressed the issue of a summary of Datacubes in order to reduce their size. The approach presented in this paper fits in a similar trend. We propose a concise representation, called Partition Cube, based on the concept of partition and we give a new algorithm to compute it. We propose a Relational Partition Cube, a novel ROLAP cubing solution for managing Partition Cubes using the relational technology. Analytical evaluations show that the storage space of Partition Cubes is smaller than Datacubes. In order to confirm analytical comparison, experiments are performed in order to compare our approach with Datacubes and with two of the best reduction methods, the Quotient Cube and the Closed Cube.

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