A multi-layer data representation of trajectories in social networks based on points of interest

In conjunction with the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012)International audienceSocial networking, and sophisticated wireless and positioning systems are fast developing and ever increasing technologies. Mobile social applications have the ability to increase the social connectivity by capturing automatically users' daily routines with Global Positioning System (GPS) receivers. These applications allow to record users' trajectories based on daily travel routes as well as to share experiences and interests among friends. However, there is always an increasing demand for providing an easy way to manipulate trajectory data, to generate and compare user profiles. Effective analysis of spatial trajectories has become an essential requirement to explore and understand the behavior of moving objects. In this paper, we highlight the importance of capturing users' daily routines in the form of trajectories in order to strengthen social connectivity. We also present the conceptual approach to multi-layer data representation in order to extract points of interest of correlated trajectories. Finally, we show how the data model could provide mobile social applications with direct support for trajectories at different abstraction levels