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dc.date.accessioned 2016-12-05T12:14:50Z
dc.date.available 2016-12-05T12:14:50Z
dc.date.issued 2016-11
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/57265
dc.description.abstract Knowledge Discovery in Databases (KDD) techniques present limitations when the volume of data to process is very large. Any KDD algorithm needs to do several iterations on the complete set of data in order to carry out its work. For continuous data stream processing it is necessary to store part of it in a temporal window. In this paper, we present a technique that uses the size of the temporal window in a dynamic way, based on the frequency of the data arrival and the response time of the KDD task. The obtained results show that this technique reaches a great size window where each example of the stream is used in more than one iteration of the KDD task. en
dc.format.extent 76-83 es
dc.language en es
dc.subject big data en
dc.subject mapreduce en
dc.subject stream processing en
dc.title Data stream treatment using sliding windows with MapReduce en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/2016/12/JCST-43-Paper-2.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Basgall, María José es
sedici.creator.person Hasperué, Waldo es
sedici.creator.person Naiouf, Marcelo es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution 3.0 Unported (CC BY 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by/3.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 16, no. 2 es


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Creative Commons Attribution 3.0 Unported (CC BY 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution 3.0 Unported (CC BY 3.0)