Lossless Reduction of Datacubes using Partitions

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Author(s): Alain Casali (Aix-Marseille Universités, France), Sébastien Nedjar (Aix-Marseille Universités, France), Rosine Cicchetti (Aix-Marseille Universités, France), Lotfi Lakhal (Aix-Marseille Universités, France)and Noël Novelli (Aix-Marseille Universités, France)
Copyright: 2009
Volume: 5
Issue: 1
Pages: 18
Source title: International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/jdwm.2009010102

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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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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Lossless Reduction of Datacubes using Partitions

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