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Approximate query processing using wavelets

Chakrabarti Kaushik, Garofalakis Minos, Rastogi Rajeev, Shim Kyuseok

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URIhttp://purl.tuc.gr/dl/dias/845EC50A-7796-4CC5-BFD5-8DA24931F688-
Identifierhttp://link.springer.com/article/10.1007%2Fs007780100049-
Identifierhttps://doi.org/10.1007/s007780100049-
Languageen-
Extent25 pagesen
TitleApproximate query processing using waveletsen
CreatorChakrabarti Kaushiken
CreatorGarofalakis Minosen
CreatorΓαροφαλακης Μινωςel
CreatorRastogi Rajeeven
CreatorShim Kyuseoken
PublisherSpringer Verlagen
Content SummaryApproximate query processing has emerged as a cost-effective approach for dealing with the huge data volumes and stringent response-time requirements of today's decision support systems (DSS). Most work in this area, however, has so far been limited in its query processing scope, typically focusing on specific forms of aggregate queries. Furthermore, conventional approaches based on sampling or histograms appear to be inherently limited when it comes to approximating the results of complex queries over high-dimensional DSS data sets. In this paper, we propose the use of multi-dimensional wavelets as an effective tool for general-purpose approximate query processing in modern, high-dimensional applications. Our approach is based on building wavelet-coefficient synopses of the data and using these synopses to provide approximate answers to queries. We develop novel query processing algorithms that operate directly on the wavelet-coefficient synopses of relational tables, allowing us to process arbitrarily complex queries entirely in the wavelet-coefficient domain. This guarantees extremely fast response times since our approximate query execution engine can do the bulk of its processing over compact sets of wavelet coefficients, essentially postponing the expansion into relational tuples until the end-result of the query. We also propose a novel wavelet decomposition algorithm that can build these synopses in an I/O-efficient manner. Finally, we conduct an extensive experimental study with synthetic as well as real-life data sets to determine the effectiveness of our wavelet-based approach compared to sampling and histograms. Our results demonstrate that our techniques: (1) provide approximate answers of better quality than either sampling or histograms; (2) offer query execution-time speedups of more than two orders of magnitude; and (3) guarantee extremely fast synopsis construction times that scale linearly with the size of the data.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-29-
Date of Publication2001-
SubjectQuery processingen
Subject Data synopsesen
SubjectApproximate query answersen
SubjectWavelet decompositionen
Bibliographic CitationK. Chakrabarti, M. Garofalakis, R. Rastogi and K. Shim, "Approximate query processing using wavelets", VLDB J., vol. 10, no. 2-3, pp. 199-223, Sep. 2001. doi:10.1007/s007780100049en

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