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.
CITATION STYLE
Dehne, F., Eavis, T., & Liang, B. (2007). Compressing data cube in parallel olap systems. Data Science Journal, 6(SUPPL.). https://doi.org/10.2481/dsj.6.S184
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