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Strategies for complex data cube queries

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Abstract

This paper proposes a computation method for holistic multi-feature cube (MF-Cube) queries based on the characteristics of MF-Cubes. Three simple yet efficient strategies are designed to optimize the dependent complex aggregate at multiple granularities for a complex data-mining query within data cubes. One strategy is the computation of Holistic MF-Cube queries using the PDAP (Part Distributive Aggregate Property). More efficiency is gained by another strategy, that of dynamic subset data selection (the iceberg query technique), which reduces the size of the materialized data cubes. To extend this efficiency further, the second approach may adopt the chunk-based caching technique that reuses the output of previous queries. By combining these three strategies, we design an algorithm called the PDIC (Part Distributive Iceberg Chunk). We experimentally evaluate this algorithm using synthetic and real-world datasets and demonstrate that our approach delivers up to approximately twice the performance efficiency of traditional computation methods.

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Authors and Affiliations

  1. Faculty of Information Technology, University of Technology Sydney, P.O. Box 123, Broadway, Sydney, NSW, 2007, Australia

    Shichao Zhang

  2. College of CS and IT, Guangxi Normal University, Guilin, China

    Shichao Zhang

  3. Department of Comuter Science, Guangxi University of Technology, Liuzhou, China

    Rifeng Wang

  4. School of EE and CS, Peking University, Beijing, China

    Zhi Jin

Authors
  1. Shichao Zhang
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  2. Rifeng Wang
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  3. Zhi Jin
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Correspondence to Shichao Zhang.

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Zhang, S., Wang, R. & Jin, Z. Strategies for complex data cube queries. Appl Intell 31, 332–346 (2009). https://doi.org/10.1007/s10489-008-0130-2

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  • DOI: https://doi.org/10.1007/s10489-008-0130-2

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