Cyberguide: a mobile context-aware tour guide
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
The String-to-String Correction Problem
Journal of the ACM (JACM)
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
Going wireless: behavior & practice of new mobile phone users
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
SenSay: A Context-Aware Mobile Phone
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
A Mobility Prediction Architecture Based on Contextual Knowledge and Spatial Conceptual Maps
IEEE Transactions on Mobile Computing
Predicting the location of mobile users: a machine learning approach
Proceedings of the 2009 international conference on Pervasive services
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
Clustering and prediction of mobile user routes from cellular data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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We propose a probabilistic method for context prediction of mobile users based on their historic context data. The proposed method predicts general context based on the probability theory through a novel graphical data structure, which is a kind of weighted directed multi-graphs. 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. The periodic property of context data is also considered. We bring a nice solution to context data with such property. Through simulation, we could show the merits of the proposed method.