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International Conference on Database Theory

ICDT 2005: Database Theory - ICDT 2005 pp 368–382Cite as

Optimal Workload-Based Weighted Wavelet Synopses

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 3363)

Abstract

In recent years wavelets were shown to be effective data synopses. We are concerned with the problem of finding efficiently wavelet synopses for massive data sets, in situations where information about query workload is available. We present linear time, I/O optimal algorithms for building optimal workload-based wavelet synopses for point queries. The synopses are based on a novel construction of weighted inner-products and use weighted wavelets that are adapted to those products. The synopses are optimal in the sense that the subset of retained coefficients is the best possible for the bases in use with respect to either the mean-squared absolute or relative errors. For the latter, this is the first optimal wavelet synopsis even for the regular, non-workload-based case. Experimental results demonstrate the advantage obtained by the new optimal wavelet synopses, as well as the robustness of the synopses to deviations in the actual query workload.

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Author information

Authors and Affiliations

  1. School of Computer Science, Tel-Aviv University,  

    Yossi Matias & Daniel Urieli

Authors
  1. Yossi Matias
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  2. Daniel Urieli
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Editor information

Editors and Affiliations

  1. Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040, Vienna, Austria

    Thomas Eiter

  2. School of Informatics, University of Edinburgh,  

    Leonid Libkin

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Matias, Y., Urieli, D. (2004). Optimal Workload-Based Weighted Wavelet Synopses. In: Eiter, T., Libkin, L. (eds) Database Theory - ICDT 2005. ICDT 2005. Lecture Notes in Computer Science, vol 3363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30570-5_25

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  • DOI: https://doi.org/10.1007/978-3-540-30570-5_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24288-8

  • Online ISBN: 978-3-540-30570-5

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