Modular Bayesian network learning for mobile life understanding

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. © 2008 Springer Berlin Heidelberg.