Abstract
A Multi-Feature Cube (MF-Cube) query is a complex-data-mining query based on data cubes, which computes the dependent complex aggregates at multiple granularities. Existing computations designed for simple data cube queries can be used to compute distributive and algebraic MF-Cubes queries. In this paper we propose an efficient computation of holistic MF-Cubes queries. This method computes holistic MF-Cubes with PDAP (Part Distributive Aggregate Property). The efficiency is gained by using dynamic subset data selection strategy (Iceberg query technique) to reduce the size of materialized data cube. Also for efficiency, this approach adopts the chunk-based caching technique to reuse the output of previous queries. We experimentally evaluate our algorithm using synthetic and real-world datasets, and demonstrate that our approach delivers up to about twice the performance of traditional computations.
This work is partially supported by Australian large ARC grants (DP0449535, DP0559536 and DP0667060), a China NSFC major research Program (60496327), a China NSFC grant (60463003) and a grant from Overseas Outstanding Talent Research Program of Chinese Academy of Sciences (06S3011S01).
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Zhang, S., Wang, R., Guo, Y. (2006). Efficient Computation of Multi-feature Data Cubes. In: Lang, J., Lin, F., Wang, J. (eds) Knowledge Science, Engineering and Management. KSEM 2006. Lecture Notes in Computer Science(), vol 4092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811220_52
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DOI: https://doi.org/10.1007/11811220_52
Publisher Name: Springer, Berlin, Heidelberg
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