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With the pervasive use of mobile devices with location sensing and positioning functions, such as Wi-Fi and GPS, people now are able to acquire present locations and collect their movement. As the availability of trajectory data prospers, mining activities hidden in raw trajectories becomes a hot research problem. Given a set of trajectories, prior works(More)
Location prediction has attracted a significant amount of research effort. Given an object's recent movements and a future time, the goal of location prediction is to predict the location of this object at the future time specified. Prior works have elaborated on mining association relationships among regions, in which objects frequently appear, to predict(More)
Predicting Apps usage has become an important task due to the proliferation of Apps, and the complex of Apps. However, the previous research works utilized a considerable number of different sensors as training data to infer Apps usage. To save the energy consumption for the task of predicting Apps usages, only the temporal information is considered in this(More)
Due to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage.(More)
Rapid growth in location data acquisition techniques has led to a proliferation of trajectory data related to moving objects. This large body of data has expanded the scope for trajectory research and made it applicable to a more diverse range of fields. However, data uncertainty, which is naturally inherent in the trajectory data, brings the challenge in(More)
Dummy-based anonymization techniques for protecting the location privacy of mobile users have appeared in the literature. By generating dummies that move in human-like trajectories, this approach shows that the location privacy of mobile users can be preserved. However, the trajectories of mobile users can still be exposed by monitoring the long-term(More)
Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., The social influence of friends. We propose a new social influence-based user(More)
Location prediction is a crucial need for location-aware services and applications. Given an object’s recent movement and a future time, the goal of location prediction is to predict the location of the object at the future time specified. Different from traditional location prediction using motion function, some research works have elaborated on mining(More)
Due to the proliferation of mobile applications(abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Appsby swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage.(More)
  • Po-Ruey Lei
  • 2013
As security requirements in coastal water and sea ports, maritime surveillance increases the duty. In this research, we focus on the maritime trajectory data to explore movement behavior for anomaly detection in maritime traffic. Trajectory data records the moving objects' true movement and provides the opportunity to discover the movement behavior for(More)