Data Science and Big Data: An Environment of Computational Intelligence

  • Witold Pedrycz
  • Shyi-Ming Chen

Part of the Studies in Big Data book series (SBD, volume 24)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Fundamentals

    1. Front Matter
      Pages 1-1
    2. Rocco Langone, Vilen Jumutc, Johan A. K. Suykens
      Pages 3-28
    3. Hossein Yazdani, Daniel Ortiz-Arroyo, Kazimierz Choroś, Halina Kwasnicka
      Pages 29-48
    4. Sachin Subhash Patil, Shefali Pratap Sonavane
      Pages 49-81
    5. Balakumar Balasingam, Pujitha Mannaru, David Sidoti, Krishna Pattipati, Peter Willett
      Pages 83-107
    6. Joan Capdevila, Jesús Cerquides, Jordi Torres
      Pages 161-186
  3. Applications

    1. Front Matter
      Pages 187-187
    2. Abduljalil Mohamed, Mohamed Salah Hamdi, Sofiène Tahar
      Pages 189-207
    3. Alba Amato, Salvatore Venticinque
      Pages 209-229
    4. Mladen Kezunovic, Zoran Obradovic, Tatjana Dokic, Bei Zhang, Jelena Stojanovic, Payman Dehghanian et al.
      Pages 265-299
  4. Back Matter
    Pages 301-303

About this book


This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.
Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today’s knowledge-driven economy.
Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.
The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.


Big Data Data Science Computational Intelligence Data Analytics Internet of Things

Editors and affiliations

  • Witold Pedrycz
    • 1
  • Shyi-Ming Chen
    • 2
  1. 1.Electrical & Computer EngineeringUniversity of Alberta Electrical & Computer EngineeringEdmonton ALCanada
  2. 2.Dept of CS and Information EngineeringNational Taiwan Univ of Science and Tech Dept of CS and Information EngineeringTaipeiTaiwan

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-53473-2
  • Online ISBN 978-3-319-53474-9
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site
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