Research Papers
Compressing Data Cube in Parallel OLAP Systems
Authors:
Frank Dehne ,
School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Canada K1S 5B6
Todd Eavis,
Computer Science Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Canada, H3G 1M8
Boyong Liang
School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Canada K1S 5B6
Abstract
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.
How to Cite:
Dehne, F., Eavis, T. and Liang, B., 2007. Compressing Data Cube in Parallel OLAP Systems. Data Science Journal, 6, pp.S184–S197. DOI: http://doi.org/10.2481/dsj.6.S184
Published on
28 Mar 2007.
Peer Reviewed
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