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Iceberg-cube computation with PC clusters


Raymond T. Ng, Alan S. Wagner, and Yu Yin

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Abstract

In this paper, we investigate the approach of using low cost PC clusters to parallelize the computation of iceberg-cube queries. We concentrate on techniques directed towards on-line querying of large, high-dimensional datasets where it is assumed that the total cube has not been precomputed. The algorithmic space we explore considers trade-offs between parallelism, computation and I/O. Our main contribution is the development and a comprehensive evaluation of various novel, parallel algorithms. Specifically: (1) Algorithm RP is a straightforward parallel version of BUC; (2) Algorithm BPP attempts to reduce I/O by outputting results in a more efficient way; (3) Algorithm ASL, which maintains cells in a cuboid in a skiplist, is designed to put the utmost priority on load balancing; and (4) alternatively, Algorithm PT load-balances by using binary partitioning to divide the cube lattice as evenly as possible.


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