Mining Long, Sharable Patterns in Trajectories of Moving Objects

The efficient analysis of spatio–temporal data, generated by moving objects, is an es-
sential requirement for intelligent locationbased services. Spatio-temporal rules can be
found by constructing spatio–temporal baskets, from which traditional association rule
mining methods can discover spatio–temporal rules. When the items in the baskets are
spatio–temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable requent routes.
The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of subroutes of long routes. A SQL–based implementation is described, and experiments on real life data show the effectiveness of the method.