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A Comprehensive Context-Aware Recommender System Framework Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Big Data and Computational Intelligence: Background, Trends, Challenges, and Opportunities

Authors : Sukey Nakasima-López, Mauricio A. Sanchez, Juan R. Castro

Published in: Computer Science and Engineering—Theory and Applications

Publisher: Springer International Publishing

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Abstract

The boom of technologies such as social media, mobile devices, internet of things, and so on, has generated enormous amounts of data that represent a tremendous challenge, since they come from different sources, different formats and are being generated in real time at an exponential speed which brings with it new necessities, opportunities, and many challenges both in the technical and analytical area. Some of the prevailing necessities lie on the development of computationally efficient algorithms that can extract value and knowledge from data and can manage the noise within in it. Computational intelligence can be seen as a key alternative to manage inaccuracies and extract value from Big Data, using fuzzy logic techniques for a better representation of the problem. And, if the concept of granular computing is also added, we will have new opportunities to decomposition of a complex data model into smaller, more defined, and meaningful granularity levels, therefore different perspectives could yield more manageable models. In this paper, two related subjects are covered, (1) the fundamentals and concepts of Big Data are described, and (2) an analysis of how computational intelligence techniques could bring benefits to this area is discussed.

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Metadata
Title
Big Data and Computational Intelligence: Background, Trends, Challenges, and Opportunities
Authors
Sukey Nakasima-López
Mauricio A. Sanchez
Juan R. Castro
Copyright Year
2018
Book
Computer Science and Engineering—Theory and Applications

Print ISBN: 978-3-319-74059-1

Electronic ISBN: 978-3-319-74060-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-74060-7_10

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