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dc.contributor.authorSalvador, Jaime-
dc.contributor.authorRuiz, Zoila-
dc.contributor.authorGarcía-Rodríguez, José-
dc.date.accessioned2018-01-03T21:55:14Z-
dc.date.available2018-01-03T21:55:14Z-
dc.date.issued2017-
dc.identifier.citationSalvador, Jaime; Ruiz, Zoila y García Rodríguez, José (2017). Big data infrastructure: a survey. Springer International Publishing AG 2017, pp. 249–258es_ES
dc.identifier.otherBIBLIOTECA GENERAL - UCE-
dc.identifier.urihttp://www.dspace.uce.edu.ec/handle/25000/13653-
dc.description.abstractIn the last years, the volume of information is growing faster than ever before, moving from small datasets to huge volumes of information. This data growth has forced researchers to look for new alternatives to process and store this data, since traditional techniques have been limited by the size and structure of the information. On the other hand, the power of parallel computing in new processors has gradually increased, from single processor architectures to multiple processor, cores and threads. This latter fact enabled the use of machine learning techniques to take advantage of parallel processing capabilities offered by new architectures on large volumes of data. The present paper reviews and proposes a classification, using as criteria, the hardware infrastructures used in works of machine learning parallel approaches applied to large volumes of data.es_ES
dc.language.isoenges_ES
dc.publisher[s.l.]: Springeres_ES
dc.rightsopenAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/ec/es_ES
dc.subjectAPRENDIZAJE AUTOMÁTICOes_ES
dc.subjectDATOSes_ES
dc.subjectGPUes_ES
dc.subjectMAPA REDUCIDOes_ES
dc.titleBig data infrastructure: a surveyes_ES
dc.typeArtículoes_ES
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