Name
Abstract | ||
|---|---|---|
Predicting the location of people from their mobile phone logs is becoming an attractive research area. Due to two main reasons this problem is very challenging: the log data is very large and there is a variety of granularity levels both for specifying the location and the time, especially with low granularity level it becomes much more complicated to define common user behaviour patterns. In this work, rather than determining the next location of a person, we focus on the predicting the location of a person when it changes. We employed a two phase method; which first clusters the data to obtain a higher granularity level, and then extracts frequent sequential patterns corresponding to location changes. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results in predicting the change of location of mobile phone users. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2534190.2534196 | MobiGIS |
Keywords | Field | DocType |
mobile phone log,location change,largest mobile phone operator,granularity level,low granularity level,next location,log data,mobile phone user,higher granularity level | Data mining,Computer science,Timing advance,Operator (computer programming),Mobile phone,Granularity,Location prediction | Conference |
Citations | PageRank | References |
4 | 0.54 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mert Ozer | 1 | 44 | 5.85 |
| Ilkcan Keles | 2 | 25 | 4.39 |
| Ismail Hakki Toroslu | 3 | 456 | 102.80 |
| Pinar Karagoz | 4 | 154 | 28.34 |