International Journal of Computational Intelligence Systems

Volume 9, Issue sup1, April 2016, Pages 69 - 80

A View on Fuzzy Systems for Big Data: Progress and Opportunities

Authors
Alberto Fernández1, alberto.fernandez@ujaen.es, Cristobal José Carmona2, cjcarmona@ubu.es, María José del Jesus1, mjjesus@ujaen.es, Francisco Herrera3, 4, herrera@decsai.ugr.es
1Department of Computer Science, University of Jaén, Jaén, 23071, Spain.
2Department of Civil Engineering, University of Burgos, Burgos, 09006, Spain
3Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Granada, 18071, Spain
4Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Corresponding Author
Alberto Fernándezalberto.fernandez@ujaen.es
Received 8 February 2016, Accepted 12 March 2016, Available Online 9 January 2017.
DOI
https://doi.org/10.1080/18756891.2016.1180820How to use a DOI?
Keywords
Big Data, Fuzzy Rule Based Classification Systems, Clustering, MapReduce, Hadoop, Spark, Flink
Abstract

Currently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that allows at working with Big Data. By means of the MapReduce model and its different extensions, scalability can be successfully addressed, while maintaining a good fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining, those models based on fuzzy systems stand out for many applications. Among their advantages, we must stress the use of a representation close to the natural language. Additionally, they use an inference model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty. Despite the success of this type of systems, their migration to the Big Data environment in the different learning areas is at a preliminary stage yet. In this paper, we will carry out an overview of the main existing proposals on the topic, analyzing the design of these models. Additionally, we will discuss those problems related to the data distribution and parallelization of the current algorithms, and also its relationship with the fuzzy representation of the information. Finally, we will provide our view on the expectations for the future in this framework according to the design of those methods based on fuzzy sets, as well as the open challenges on the topic.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 100
Pages
69 - 80
Publication Date
2017/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2016.1180820How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Alberto Fernández
AU  - Cristobal José Carmona
AU  - María José del Jesus
AU  - Francisco Herrera
PY  - 2017
DA  - 2017/01
TI  - A View on Fuzzy Systems for Big Data: Progress and Opportunities
JO  - International Journal of Computational Intelligence Systems
SP  - 69
EP  - 80
VL  - 9
IS  - sup1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1180820
DO  - https://doi.org/10.1080/18756891.2016.1180820
ID  - Fernández2017
ER  -