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Mathematical Problems in Engineering
Volume 2013, Article ID 106139, 10 pages
Research Article

Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach

1Department of Computer Science & Engineering, Kwangwoon University, 20 Kwangwoon-Ro, Nowon-Gu, Seoul 139-701, Republic of Korea
2Future IT R&D Lab., LG Electronics, Umyeon R&D Campus, 38, Baumoe-Ro, Secho-Gu, Seoul 137-724, Republic of Korea

Received 27 May 2013; Accepted 13 July 2013

Academic Editor: Orwa Jaber Housheya

Copyright © 2013 Yong-Hyuk Kim and Yourim Yoon. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a probabilistic method of predicting context of mobile users based on their historic context data. The presented method predicts general context based on probability theory through a novel graphical data structure, which is a kind of weighted directed multigraphs. User context data are transformed into the new graphical structure, in which each node represents a context or a combined context and each directed edge indicates a context transfer with the time weight inferred from corresponding time data. We also consider the periodic property of context data, and we devise a good solution to context data with such property. Through test, we could show the merits of the presented method.