Computing Full and Iceberg Datacubes Using Partitions

  • Authors:
  • Marc Laporte;Noel Novelli;Rosine Cicchetti;Lotfi Lakhal

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
  • Year:
  • 2002

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Abstract

In this paper, we propose a sound approach and an algorithm for computing a condensed representation of either full or iceberg datacubes. A novel characterization of datacubes based on dimensional-measurable partitions is introduced. From such partitions, iceberg cuboids are achieved by using constrained product linearly in the number of tuples. Moreover, our datacube characterization provides a loss-less condensed representation specially suitable when considering the storage explosion problem and the I/O cost. We show that our algorithm CCUBE turns out to an operational solution more efficient than competive proposals. It enforces a lecticwise and recursive traverse of the dimension set lattice and takes into account the critical problem of memory limitation. Our experimental results shows that CCUBE is a promising candidate for scalable computation.