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Emerging, Collective Intelligence for Personal, Organisational and Social Use

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Next Generation Data Technologies for Collective Computational Intelligence

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

Abstract

The main objective of this chapter is to present novel technologies for exploiting multiple layers of intelligence from user-contributed content, which together constitute Collective Intelligence, a form of intelligence that emerges from the collaboration and competition among many individuals, and that seemingly has a mind of its own. User contributed content is analysed by integrating research and development in media analysis, mass content processing, user feedback, social analysis and knowledge management to automatically extract the hidden intelligence and make it accessible to end users and organisations. The exploitation of the emerging Collective Intelligence results is showcased in two distinct case studies: an Emergency Response and a Consumers Social Group case study.

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Authors and Affiliations

  1. Informatics & Telematics Institute, Thermi, Thessaloniki, Greece

    Sotiris Diplaris, Symeon Papadopoulos & Yiannis Kompatsiaris

  2. Karlsruhe Institute of Technology, Germany

    Andreas Sonnenbichler & Michael Ovelgoenne

  3. Software Mind S.A., Krakow, Poland

    Tomasz Kaczanowski

  4. National Technical University of Athens, Greece

    Phivos Mylonas

  5. University of Koblenz-Landau, Germany

    Ansgar Scherp & Maciej Janik

  6. Department of Computer Science, Aristotle University of Thessaloniki, Greece

    Symeon Papadopoulos

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  1. Sotiris Diplaris
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  1. School of Computing & Maths, University of Derby, DE22 1GB, Derby, United Kingdom (UK)

    Nik Bessis

  2. Dept de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

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Diplaris, S. et al. (2011). Emerging, Collective Intelligence for Personal, Organisational and Social Use. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_20

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