Advertisement

Enhancing Alignment Based Context Prediction by Using Multiple Context Sources: Experiment and Analysis

  • Immanuel König
  • Christian Voigtmann
  • Bernd Niklas Klein
  • Klaus David
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6967)

Abstract

Context aware applications are reactive, they adapt to an entity’s context when the context has changed. In order to become proactive and act before the context actually has changed future contexts have to be predicted. This will enable functionalities like preloading of content or detection of future conflicts. For example if an application can predict where a user is heading to it can also check for train delays on the user’s way. So far research concentrates on context prediction algorithms that only use a history of one context to predict the future context. In this paper we propose a novel multidimensional context prediction algorithm and we show that the use of multidimensional context histories increases the prediction accuracy. We compare two multidimensional prediction algorithms, one of which is a new approach; the other was not yet experimentally tested. In theory, simulation and a real world experiment we verify the feasibility of both algorithms and show that our new approach has at least equal or better reasoning accuracy.

Keywords

context context prediction multidimensional context prediction alignment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Weiser, M.: The computer for the 21st century. Scientific American 3 (1991)Google Scholar
  2. 2.
    Mayrhofer, R.M.: An Architecture for Context Prediction. PhD thesis, Johannes Kepeler University of Linz, Linz, Austria (2004)Google Scholar
  3. 3.
    Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T.: Prediction of indoor movements using bayesian networks. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 211–222. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Sigg, S., Haseloff, S., David, K.: A novel approach to context prediction in Ubicomp environments. In: PIMRC 2006, Helsinki, Finland (2006)Google Scholar
  5. 5.
    Mayrhofer, R.: Context prediction based on context histories: Expected benets, issues and current state-of-the-art. In: Proceedings of the 1st International Workshop on Exploiting Context Histories in Smart Environments (ECHISE) at the 3rd Int. Conference on Pervasive Computing (2005)Google Scholar
  6. 6.
    Chen, G., Kotz, D.: A survey of context-aware mobile computing research. Tech. Report TR2000-381, Dept. of Computer Science, Dartmouth College (2000)Google Scholar
  7. 7.
    Sigg, S.: Development of a novel context prediction algorithm and analysis of context prediction schemes. PhD thesis, University of Kassel, Germany (2007)Google Scholar
  8. 8.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology 215(3) (October 1990)Google Scholar
  9. 9.
    Siafu - An Open Source Context Simulator (May 2011), http://siafusimulator.sourceforge.net

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Immanuel König
    • 1
  • Christian Voigtmann
    • 1
  • Bernd Niklas Klein
    • 1
  • Klaus David
    • 1
  1. 1.Communication Technology (ComTec)University of KasselKasselGermany

Personalised recommendations