A regression-based approach for mining user movement patterns from random sample data

Abstract

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.