Advertisement

Modular Bayesian Network Learning for Mobile Life Understanding

  • Keum-Sung Hwang
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

Mobile devices can now handle a great deal of information thanks to the convergence of diverse functionalities. Mobile environments have already shown great potential in terms of providing customized services to users because they can record meaningful and private information continually for long periods of time. Until now, most of this information has been generally ignored because of the limitations of mobile devices in terms of power, memory capacity and speed. In this paper, we propose a novel method that efficiently infers semantic information and overcome the problems. This method uses an effective probabilistic Bayesian network model for analyzing various kinds of log data in mobile environments, which were modularized in this paper to decrease complexity. We also discuss how to discover and update the Bayesian inference model by using the proposed BN learning method with training data. The proposed methods were evaluated with artificial mobile log data generated and collected in the real world.

Keywords

Modularized Probabilistic Reasoning Mobile Application 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Schmidt, A., Takaluoma, A., Mäntyjärvi, J.: Context-aware telephony over WAP. Personal Technologies 4(4), 225–229 (2000)CrossRefGoogle Scholar
  3. 3.
    Lo, B.P.L., Thiemjarus, S., Yang, G.-Z.: Adaptive Bayesian networks for video processing. In: Int. Conf. on Image Processing, vol. 1(1), pp. 889–892 (2003)Google Scholar
  4. 4.
    Raento, M., Oulasvirta, A., Petit, R., Toivonen, H.: ContextPhone: A prototyping platform for context-aware mobile applications. IEEE Pervasive Computing 4(2), 51–59 (2005)CrossRefGoogle Scholar
  5. 5.
    Krause, A., Smailagic, A., Siewiorek, D.P.: Context-aware mobile computing: Learning context-dependent personal preferences from a wearable sensor array. IEEE Trans. on Mobile Computing 5(2), 113–127 (2006)CrossRefGoogle Scholar
  6. 6.
    Korpipaa, P., Mantyjarvi, J., Kela, J., Keranen, H., Malm, E.-J.: Managing context information in mobile devices. IEEE Pervasive Computing 2(3), 42–51 (2003)CrossRefGoogle Scholar
  7. 7.
    Dourish, P.: What we talk about when we talk about context. Personal and Ubiquitous Computing 8(1), 19–30 (2004)CrossRefGoogle Scholar
  8. 8.
    Hwang, K.-S., Cho, S.-B.: Modular Bayesian Networks for Inferring Landmarks on Mobile Daily Life. In: The 19th Australian Joint Conf. on Artificial Intelligence, pp. 929–933 (2006)Google Scholar
  9. 9.
    Krause, A., Smailagic, A., Siewiorek, D.P.: Context-aware mobile computing: Learning context-dependent personal preferences from a wearable sensor array. IEEE Trans. on Mobile Computing 5(2), 113–127 (2006)CrossRefGoogle Scholar
  10. 10.
    Horvitz, E., Dumais, S., Koch, P.: Learning predictive models of memory landmarks. In: CogSci 2004. 26th Annual Meeting of the Cognitive Science Society, pp. 1–6 (2004)Google Scholar
  11. 11.
    Marengoni, M., Hanson, A., Zilberstein, S., Riseman, E.: Decision making and uncertainty management in a 3D reconstruction system. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(7), 852–858 (2003)CrossRefGoogle Scholar
  12. 12.
    Tu, H., Allanach, J., Singh, S., Pattipati, K.R., Willett, P.: Information integration via hierarchical and hybrid Bayesian networks. IEEE Trans. On Systems, Man, and Cybernetics.Part A: Systems and Humans 36(1), 19–33 (2006)Google Scholar
  13. 13.
    Hwang, K.-S., Cho, S.-B.: Constrained learning method of Bayesian network structure for efficient context classification. In: Proc. of Korea Information Science Society (In Korean) , vol. 31(2), pp. 112–114 (2004)Google Scholar
  14. 14.
    Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)zbMATHGoogle Scholar
  15. 15.
    Su, J., Zhang, H.: Full Bayesian network classifiers. In: Proc. of international conference on Machine learning, pp. 897–904 (2006)Google Scholar
  16. 16.
    Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, 157–224 (1988)Google Scholar
  17. 17.
    Namasivayam, V.K., Prasanna, V.K.: Salable parallel implementation of exact inference in Bayesian networks. In: Int. Conf. on Parallel and Distributed Systems (July 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Keum-Sung Hwang
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

Personalised recommendations