Abstract
Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This work presents an approach for location prediction using a probabilistic model and data mining techniques over mobility data streams. We evaluate the method over 5 real-world datasets. The results show the usefulness of the proposal in comparison with other-well-known approaches.