Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1774088.1774302 | SAC |
Keywords | Field | DocType |
corresponding time data,probabilistic context prediction,context data,time-inferred multiple pattern network,novel graphical data structure,historic context data,context prediction,user context data,context transfer,combined context,general context,data structure,probabilistic method,probability theory,data mining | Data mining,Data structure,Time data,Computer science,Context based,Probabilistic method,Theoretical computer science,Context model,Probabilistic logic,Probability theory,Periodic graph (geometry) | Conference |
Citations | PageRank | References |
2 | 0.42 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong-Hyuk Kim | 1 | 355 | 40.27 |
| Wonkook Kim | 2 | 36 | 4.61 |
| Kyungsub Min | 3 | 3 | 1.11 |
| Yourim Yoon | 4 | 185 | 17.18 |