Data Science Journal (Mar 2007)

Compressing Data Cube in Parallel OLAP Systems

  • Frank Dehne,
  • Todd Eavis,
  • Boyong Liang

DOI
https://doi.org/10.2481/dsj.6.S184
Journal volume & issue
Vol. 6

Abstract

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This paper proposes an efficient algorithm to compress the cubes in the progress of the parallel data cube generation. This low overhead compression mechanism provides block-by-block and record-by-record compression by using tuple difference coding techniques, thereby maximizing the compression ratio and minimizing the decompression penalty at run-time. The experimental results demonstrate that the typical compression ratio is about 30:1 without sacrificing running time. This paper also demonstrates that the compression method is suitable for Hilbert Space Filling Curve, a mechanism widely used in multi-dimensional indexing.

Keywords