Editors:
- Includes introductory chapters both in terms of big data, the key challenges of the area, development opportunities and a well-focused introduction to Computational Intelligence
- Self-contained and easily accessible volume
- Written by experts in the field
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 8)
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Table of contents (21 chapters)
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Front Matter
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Fundamentals
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Front Matter
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Case Studies
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Front Matter
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About this book
The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data.
This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make someabstract concepts more tangible.
Editors and Affiliations
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Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
Witold Pedrycz
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Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Shyi-Ming Chen
Bibliographic Information
Book Title: Information Granularity, Big Data, and Computational Intelligence
Editors: Witold Pedrycz, Shyi-Ming Chen
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-08254-7
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-08253-0Published: 29 July 2014
Softcover ISBN: 978-3-319-38161-9Published: 17 September 2016
eBook ISBN: 978-3-319-08254-7Published: 14 July 2014
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XI, 444
Number of Illustrations: 97 b/w illustrations, 26 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, e-Commerce/e-business