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Sampling with incremental mapreduce

dc.contributor.authorSchäfer, Marc
dc.contributor.authorSchildgen, Johannes
dc.contributor.authorDeßloch, Stefan
dc.contributor.editorRitter, Norbert
dc.contributor.editorHenrich, Andreas
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorThor, Andreas
dc.contributor.editorFriedrich, Steffen
dc.contributor.editorWingerath, Wolfram
dc.date.accessioned2017-06-30T11:39:34Z
dc.date.available2017-06-30T11:39:34Z
dc.date.issued2015
dc.description.abstractThe goal of this paper is to increase the computation speed of MapReduce jobs by reducing the accuracy of the result. Often, the timely processing is more important than the precision of the result. Hadoop has no built-in functionality for such an approximation technique, so the user has to implement sampling techniques manually. We introduce an automatic system for computing arithmetic approximations. The sampling is based on techniques from statistics and the extrapolation is done generically. This system is also extended by an incremental component which enables the reuse of already computed results to enlarge the sampling size. This can be used iteratively to further increase the sampling size and also the precision of the approximation. We present a transparent incremental sampling approach, so the developed components can be integrated in the Hadoop framework in a non-invasive manner.en
dc.identifier.isbn978-3-88579-636-7
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-242
dc.titleSampling with incremental mapreduceen
dc.typeText/Conference Paper
gi.citation.endPage130
gi.citation.publisherPlaceBonn
gi.citation.startPage121
gi.conference.date2.-3. März 2015
gi.conference.locationHamburg

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