Next Generation Data Technologies for Collective Computational Intelligence

  • Nik Bessis
  • Fatos Xhafa

Part of the Studies in Computational Intelligence book series (SCI, volume 352)

Table of contents

  1. Front Matter
  2. Foundations and Principles

    1. Front Matter
      Pages 1-1
    2. Richard Mordinyi, Eva Kühn
      Pages 3-30
    3. Ioannis Michelakos, Nikolaos Mallios, Elpiniki Papageorgiou, Michael Vassilakopoulos
      Pages 31-60
    4. Shaun Bridges, Jeffrey Schiffel, Simon Polovina
      Pages 61-86
  3. Advanced Models and Practices

    1. Front Matter
      Pages 137-137
    2. Simon Andrews, Constantinos Orphanides, Simon Polovina
      Pages 139-165
    3. Joanna Jȩdrzejowicz, Piotr Jȩdrzejowicz
      Pages 167-193
    4. Vesna Šešum-Čavić, Eva Kühn
      Pages 195-224
    5. Abid Yahya, Farid Ghani, Othman Sidek, R. B. Ahmad, M. F. M. Salleh, Khawaja M. Yahya
      Pages 225-250
    6. Ana Cristina Bicharra Garcia
      Pages 271-299
  4. Advanced Applications

    1. Front Matter
      Pages 301-301
    2. N. Kryvinska, C. Strauss, L. Auer
      Pages 329-355
    3. N. Kryvinska, C. Strauss, L. Auer
      Pages 357-382
    4. Zahid Halim, A. Raif Baig
      Pages 383-413
    5. Spiros Nikolopoulos, Elisavet Chatzilari, Eirini Giannakidou, Symeon Papadopoulos, Ioannis Kompatsiaris, Athena Vakali
      Pages 415-443
    6. Xiaoxun Sun, Hua Wang, Jiuyong Li
      Pages 445-469
  5. Future Trends and Concepts

    1. Front Matter
      Pages 471-471
    2. Sotiris Diplaris, Andreas Sonnenbichler, Tomasz Kaczanowski, Phivos Mylonas, Ansgar Scherp, Maciej Janik et al.
      Pages 527-573
    3. Daniel Olguín Olguín, Anmol Madan, Manuel Cebrian, Alex (Sandy) Pentland
      Pages 575-597
    4. Darryl Coulthard, Susan Keller
      Pages 599-623
  6. Back Matter

About this book


This book focuses on next generation data technologies in support of collective and computational intelligence. The book brings various next generation data technologies together to capture, integrate, analyze, mine, annotate and visualize distributed data – made available from various community users – in a meaningful and collaborative for the organization manner. A unique perspective on collective computational intelligence is offered by embracing both theory and strategies fundamentals such as data clustering, graph partitioning, collaborative decision making, self-adaptive ant colony, swarm and evolutionary agents. It also covers emerging and next generation technologies in support of collective computational intelligence such as Web 2.0 social networks, semantic web for data annotation, knowledge representation and inference, data privacy and security, and enabling distributed and collaborative paradigms such as P2P, Grid and Cloud Computing due to the geographically dispersed and distributed nature of the data.

The book aims to cover in a comprehensive manner the combinatorial effort of utilizing and integrating various next generations collaborative and distributed data technologies for computational intelligence in various scenarios. The book also distinguishes itself by assessing whether utilization and integration of next generation data technologies can assist in the identification of new opportunities, which may also be strategically fit for purpose.


Collective Computational Intelligence Data Grids Data Stream Distributed Data Analysis and Modelling Distributed Data Mining

Editors and affiliations

  • Nik Bessis
    • 1
  • Fatos Xhafa
    • 2
  1. 1.School of Computing & MathsUniversity of DerbyDerbyUnited Kingdom (UK)
  2. 2.Dept de Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-20343-5
  • Online ISBN 978-3-642-20344-2
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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