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

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

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/E41FE882-3778-4C6D-9D73-2D48ED4F8FF1-
Αναγνωριστικόhttps://doi.org/10.1561/1900000004-
Αναγνωριστικόhttp://db.ucsd.edu/static/Synopses.pdf-
Γλώσσαen-
Μέγεθος296 pagesen
ΤίτλοςSynopses for massive data: samples, histograms, wavelets, sketchesen
ΔημιουργόςCormode, Graham, 1977-en
ΔημιουργόςGarofalakis Minosen
ΔημιουργόςΓαροφαλακης Μινωςel
ΔημιουργόςHaas Peter J.en
ΔημιουργόςJermaine Chrisen
ΕκδότηςNow Publishersen
ΠερίληψηMethods 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-30-
Ημερομηνία Δημοσίευσης2012-
Θεματική ΚατηγορίαHistogramsen
Θεματική ΚατηγορίαApproximate query processingen
Βιβλιογραφική ΑναφοράG. 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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