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Selective Inference in Modular Bayesian Networks for Lightweight Context Inference in Cell Phones  

Lee, Seung-Hyun (연세대학교 컴퓨터과학과)
Lim, Sung-Soo (연세대학교 컴퓨터과학과)
Cho, Sung-Bae (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.37, no.10, 2010 , pp. 736-744 More about this Journal
Abstract
Log data collected from mobile devices contain diverse and meaningful personal information. However, it is not easy to implement a context-aware mobile agent using this personal information due to the inherent limitation in mobile platform such as memory capacity, computation power and its difficulty of analysis of the data. We propose a method of selective inference for modular Bayesian Network for context-aware mobile agent with effectiveness and reliability. Each BN module performs inference only when it can change the result by comparing to the history module which contains evidences and posterior probability, and gets results effectively using a method of influence score of the modules. We adopt memory decay theory and virtual linking method for the evaluation of the reliability and conservation of casual relationship between BN modules, respectively. Finally, we confirm the usefulness of the proposed method by several experiments on mobile phones.
Keywords
Mobile agent; Modular Bayesian network; Selective inference; Memory decay theory;
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