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Synopses for massive data: samples, histograms, wavelets, sketches

Cormode, Graham, 1977-, Garofalakis Minos, Haas Peter J., Jermaine Chris

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URIhttp://purl.tuc.gr/dl/dias/E41FE882-3778-4C6D-9D73-2D48ED4F8FF1-
Identifierhttps://doi.org/10.1561/1900000004-
Identifierhttp://db.ucsd.edu/static/Synopses.pdf-
Languageen-
Extent296 pagesen
TitleSynopses for massive data: samples, histograms, wavelets, sketchesen
CreatorCormode, Graham, 1977-en
CreatorGarofalakis Minosen
CreatorΓαροφαλακης Μινωςel
CreatorHaas Peter J.en
CreatorJermaine Chrisen
PublisherNow Publishersen
Content SummaryMethods for Approximate Query Processing (AQP) are essential for dealing with massive data. They are often the only means of providing interactive response times when exploring massive datasets, and are also needed to handle high speed data streams. These methods proceed by computing a lossy, compact synopsis of the data, and then executing the query of interest against the synopsis rather than the entire dataset. We describe basic principles and recent developments in AQP. We focus on four key synopses: random samples, histograms, wavelets, and sketches. We consider issues such as accuracy, space and time effi- ciency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance. We also discuss the tradeoffs between the different synopsis types.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-30-
Date of Publication2012-
SubjectHistogramsen
SubjectApproximate query processingen
Bibliographic CitationG. Cormode, M. Garofalakis, P. Haas and C. Jermaine, "Synopses for massive data: samples, histograms, wavelets, sketches", Foundations and Trends in Databases, vol. 4, no. 1-3, pp. 1-294, 2012. doi: 10.1561/1900000004en

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