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

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

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 tr...

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Main Authors: Yong-Hyuk Kim, Yourim Yoon
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2013/106139
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spelling doaj-a161ba649b4246748f1bc84fa7d873952020-11-24T22:29:02ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/106139106139Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic ApproachYong-Hyuk Kim0Yourim Yoon1Department of Computer Science & Engineering, Kwangwoon University, 20 Kwangwoon-Ro, Nowon-Gu, Seoul 139-701, Republic of KoreaFuture IT R&D Lab., LG Electronics, Umyeon R&D Campus, 38, Baumoe-Ro, Secho-Gu, Seoul 137-724, Republic of KoreaWe 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.http://dx.doi.org/10.1155/2013/106139
collection DOAJ
language English
format Article
sources DOAJ
author Yong-Hyuk Kim
Yourim Yoon
spellingShingle Yong-Hyuk Kim
Yourim Yoon
Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach
Mathematical Problems in Engineering
author_facet Yong-Hyuk Kim
Yourim Yoon
author_sort Yong-Hyuk Kim
title Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach
title_short Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach
title_full Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach
title_fullStr Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach
title_full_unstemmed Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach
title_sort context prediction of mobile users based on time-inferred pattern networks: a probabilistic approach
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2013-01-01
description 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.
url http://dx.doi.org/10.1155/2013/106139
work_keys_str_mv AT yonghyukkim contextpredictionofmobileusersbasedontimeinferredpatternnetworksaprobabilisticapproach
AT yourimyoon contextpredictionofmobileusersbasedontimeinferredpatternnetworksaprobabilisticapproach
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