Our understanding of how individual mobility patterns shape and impact the social network
is limited, but is essential for a deeper understanding of network dynamics and evolution …

The pervasiveness of mobile devices and location based services is leading to an
increasing volume of mobility data. This side eect provides the opportunity for innovative …

Research on predicting movements of mobile users has attracted a lot of attentions in recent
years. Many of those prediction techniques are developed based only on geographic …

Advances in wireless and mobile technology flood us with amounts of moving object data
that preclude all means of manual data processing. The volume of data gathered from …

In recent years, research on location predictions by mining trajectories of users has attracted
a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of …

Forecasting the future positions of mobile users is a valuable task allowing us to operate
efficiently a myriad of different applications which need this type of information. We propose …

The extensive use of location-based social networks (LBSNs) allows for the collection of
huge amount of geo-tagged data about people activities and costumes within urban context …

The tremendous growth of positioning technologies and GPS enabled devices has
produced huge volumes of tracking data during the recent years. This source of information …

Due to the recent explosive growth of location-aware services based on mobile devices,
predicting the next places of a user is of increasing importance to enable proactive …

Path prediction is useful in a wide range of applications. Most of the existing solutions,
however, are based on eager learning methods where models and patterns are extracted …