Thesis Type: Postgraduate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Computer Engineering, Turkey
Approval Date: 2014
Student: MERT ÖZER
Co-Supervisor: PINAR KARAGÖZ, İSMAİL HAKKI TOROSLUAbstract:
Predicting the location of people from their mobile phone logs has become an active 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 spatial and the temporal attributes, especially with low granularity level it becomes much more complicated to define common user behaviour patterns. For the location prediction problem domain, we focused on 3 sub-problems and proposed 3 different methods for these problems. The idea in all of the three methods follows these two steps; cluster the spatial data into the regions and group temporal data into the time intervals to get higher granularity level, and apply sequential pattern mining techniques to extract frequent movement patterns to predict accordingly. 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 location of mobile phone users.