Modular Bayesian network learning for mobile life understanding

Keum Sung Hwang, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540889051, 9783540889052
Publication statusPublished - 2008
Event9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008 - Daejeon, Korea, Republic of
Duration: 2008 Nov 22008 Nov 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5326 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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