Optimal workload-based weighted wavelet synopses

Yossi Matias*, Daniel Urieli

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

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.

Original languageEnglish
Title of host publicationDatabase Theory - ICDT 2005 - 10th International Conference, Proceedings
Pages368-382
Number of pages15
DOIs
StatePublished - 2005
Event10th International Conference on Database Theory, ICDT 2005 - Edinburgh, United Kingdom
Duration: 5 Jan 20057 Jan 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3363 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Database Theory, ICDT 2005
Country/TerritoryUnited Kingdom
CityEdinburgh
Period5/01/057/01/05

Funding

FundersFunder number
Israel Science Foundation

    Fingerprint

    Dive into the research topics of 'Optimal workload-based weighted wavelet synopses'. Together they form a unique fingerprint.

    Cite this