Information Granularity, Big Data, and Computational Intelligence

  • Witold Pedrycz
  • Shyi-Ming Chen

Part of the Studies in Big Data book series (SBD, volume 8)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Fundamentals

    1. Front Matter
      Pages 1-1
    2. Georgios Chatzimilioudis, Andreas Konstantinidis, Demetrios Zeinalipour-Yazti
      Pages 3-22
    3. Yuichi Goto
      Pages 23-38
    4. Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz
      Pages 39-61
    5. Ali Jalal-Kamali, M. Shahriar Hossain, Vladik Kreinovich
      Pages 63-87
    6. Yue-Shi Lee, Show-Jane Yen
      Pages 121-140
    7. Yan-ping Zhang, Ling Zhang, Chenchu Xu
      Pages 171-190
    8. Han Liu, Alexander Gegov, Frederic Stahl
      Pages 209-230
  3. Architectures

  4. Case Studies

    1. Front Matter
      Pages 347-347
    2. James N. K. Liu, Yanxing Hu, Yulin He, Pak Wai Chan, Lucas Lai
      Pages 389-408
  5. Back Matter
    Pages 443-444

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 some abstract concepts more tangible. 



Big Data Computational Intelligence Information Granularity

Editors and affiliations

  • Witold Pedrycz
    • 1
  • Shyi-Ming Chen
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-08253-0
  • Online ISBN 978-3-319-08254-7
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site
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