<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-29T09:12:09Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/4794" metadataPrefix="marc">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/4794</identifier><datestamp>2021-11-10T09:38:23Z</datestamp><setSpec>com_10259_3847</setSpec><setSpec>com_10259_5086</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_3848</setSpec></header><metadata><record xmlns="http://www.loc.gov/MARC21/slim" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dcterms="http://purl.org/dc/terms/" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
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<subfield code="a">Fernández, Alberto .</subfield>
<subfield code="e">author</subfield>
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<subfield code="a">Carmona del Jesús, Cristóbal José</subfield>
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<subfield code="a">Jesus, María José del .</subfield>
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<datafield tag="720" ind1=" " ind2=" ">
<subfield code="a">Herrera, Francisco .</subfield>
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<subfield code="c">2016-04</subfield>
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<subfield code="a">Currently, we are witnessing a growing trend in the study and application of problems in the framework of&#xd;
Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a&#xd;
high volume of information. For this reason, we observe a migration of the standard Data Mining systems&#xd;
towards a new functional paradigm that allows at working with Big Data. By means of the MapReduce&#xd;
model and its different extensions, scalability can be successfully addressed, while maintaining a good&#xd;
fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining,&#xd;
those models based on fuzzy systems stand out for many applications. Among their advantages, we&#xd;
must stress the use of a representation close to the natural language. Additionally, they use an inference&#xd;
model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty.&#xd;
Despite the success of this type of systems, their migration to the Big Data environment in the&#xd;
different learning areas is at a preliminary stage yet. In this paper, we will carry out an overview of the&#xd;
main existing proposals on the topic, analyzing the design of these models. Additionally, we will discuss&#xd;
those problems related to the data distribution and parallelization of the current algorithms, and also its&#xd;
relationship with the fuzzy representation of the information. Finally, we will provide our view on the&#xd;
expectations for the future in this framework according to the design of those methods based on fuzzy&#xd;
sets, as well as the open challenges on the topic</subfield>
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<subfield code="a">1875-6891</subfield>
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<subfield code="a">http://hdl.handle.net/10259/4794</subfield>
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<subfield code="a">10.1080/18756891.2016.1180820</subfield>
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<subfield code="a">Big Data</subfield>
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<subfield code="a">Fuzzy Rule Based Classification Systems,</subfield>
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<subfield code="a">Clustering</subfield>
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<subfield code="a">MapReduce</subfield>
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<subfield code="a">Hadoop</subfield>
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<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">Spark</subfield>
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<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">Flink</subfield>
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<datafield tag="245" ind1="0" ind2="0">
<subfield code="a">A view on Fuzzy Systems for big data: progress and opportunities</subfield>
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