Context-Aware Telephony Over WAP
Personal and Ubiquitous Computing
Decision Making and Uncertainty Management in a 3D Reconstruction System
IEEE Transactions on Pattern Analysis and Machine Intelligence
What we talk about when we talk about context
Personal and Ubiquitous Computing
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
IEEE Transactions on Mobile Computing
Full Bayesian network classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Scalable Parallel Implementation of Exact Inference in Bayesian Networks
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Modular bayesian networks for inferring landmarks on mobile daily life
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Information integration via hierarchical and hybrid bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mobile context inference using two-layered Bayesian networks for smartphones
Expert Systems with Applications: An International Journal
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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.