Modular Bayesian Network for Uncertainty Handling on Mobile Device

Keum-Sung Hwang, Sung-Bae Cho.

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. Most of this information has been generally ignored because of the limitations of mobile devices and the uncertainty of mobile environments in real world. In this paper, we propose an approach based on modular Bayesian networks to overcome these problems and to analyze various kinds of log data. The method adopts a probabilistic approach to manage the uncertainty and decomposes the probabilistic model automatically to decrease complexity and how to infer the model, which is called cooperative reasoning. In the experimental results, the proposed methods were evaluated with mobile log data collected in the real world.

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