TY - GEN
T1 - Modular Bayesian network learning for mobile life understanding
AU - Hwang, Keum Sung
AU - Cho, Sung Bae
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=58149089797&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58149089797&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-88906-9-29
DO - 10.1007/978-3-540-88906-9-29
M3 - Conference contribution
AN - SCOPUS:58149089797
SN - 3540889051
SN - 9783540889052
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 225
EP - 232
BT - Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings
T2 - 9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008
Y2 - 2 November 2008 through 5 November 2008
ER -