Meeting Arrangement Based on Patterns of Movement

Smartphones become the part of our life, in developed countries almost everyone has a mobile computer in his pocket at all times. One of the main differences between these devices and traditional PCs is that we always carry our phone with us, which allows a new family of application areas called Location-Based Services (LBS). Currently the functionality of a typical LBS is limited to pinpointing the location of the user and attach this information to a post, or list places near him. However the constant tracking of geographical position allows creating more complex and sophisticated services. Our research focuses on mining patterns of human movement, machine learning repeating tracks and leveraging this database in various ways. In this paper we introduce a novel method which offers the best times and locations for a group of people to meet in person, based on their movement habits. The biggest value of our algorithm is that the number of participants is not limited, while most existing methods works for exactly two users.