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dc.creatorSilva, Jesús
dc.creatorHernandez Palma, Hugo Gaspar
dc.creatorNiebles Núñez, William
dc.creatorOvallos-Gazabon, David
dc.creatorVarela, Noel
dc.date.accessioned2020-04-23T16:34:52Z
dc.date.available2020-04-23T16:34:52Z
dc.date.issued2020
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttps://hdl.handle.net/11323/6240
dc.descriptionRetracted
dc.description.abstractTechnological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.spa
dc.language.isoengspa
dc.publisherJournal of Physics: Conference Seriesspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectParallel algorithmspa
dc.subjectProcessing timespa
dc.subjectBig dataspa
dc.titleParallel algorithm for reduction of data processing time in big dataspa
dc.typeArticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doidoi:10.1088/1742-6596/1432/1/012095


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