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Clouds and Continuous Analytics Enabling Social Networks for Massively Multiplayer Online Games

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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

Many of the hundreds of millions Massively Multiplayer Online Games (MMOGs) players are also involved in the social networks built around the MMOGs they play. Through these networks, these players exchange game news, advice, and expertise, and expect in return support such as player reports and clan statistics. Thus, the MMOG social networks need to collect and analyze MMOG data, in a process of continuous MMOG analytics. In this chapter we investigate the use of CAMEO, an architecture for Continuous Analytics for Massively multiplayEr Online games on cloud resources, to support the analytics part of MMOG social networks. We present the design and implementation of CAMEO, with a focus on the cloud-related benefits and challenges. We also use CAMEO to do continuous analytics on a real MMOG community of over 5,000,000 players, thus performing the largest study of an online community, to-date.

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Author information

Authors and Affiliations

  1. Parallel and Distributed Systems Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 4, 2628CD, The Netherlands

    Alexandru Iosup

  2. Computer Science Department, Faculty of Automatic Control and Computer Science, Politehnica University of Bucharest, Spl. Independentei 311, Bucharest, Romania

    Adrian Lăscăteu

Authors
  1. Alexandru Iosup
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  2. Adrian Lăscăteu
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Editor information

Editors and Affiliations

  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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Iosup, A., Lăscăteu, A. (2011). Clouds and Continuous Analytics Enabling Social Networks for Massively Multiplayer Online Games. 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_12

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  • DOI: https://doi.org/10.1007/978-3-642-20344-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20343-5

  • Online ISBN: 978-3-642-20344-2

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