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<dc:creator>Fernández, Alberto .</dc:creator>
<dc:creator>Carmona del Jesús, Cristóbal José</dc:creator>
<dc:creator>Jesus, María José del .</dc:creator>
<dc:creator>Herrera, Francisco .</dc:creator>
<dc:date>2016-04</dc:date>
<dc:description>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</dc:description>
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<dc:identifier>http://hdl.handle.net/10259/4794</dc:identifier>
<dc:language>eng</dc:language>
<dc:publisher>Atlantis Press</dc:publisher>
<dc:title>A view on Fuzzy Systems for big data: progress and opportunities</dc:title>
<dc:type>info:eu-repo/semantics/article</dc:type>
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