A criteria based function for reconstructing low-sampling trajectories as a tool for analytics

Abstract

Abstract: Mobile applications equipped with Global Positioning Systems have generated a huge quantity of location data with sampling uncertainty that must be handled and analyzed. Those location data can be ordered in time to represent trajectories of moving objects. The data warehouse approach based on spatio-temporal data can help on this task. For this reason, we address the problem of personalized reconstruction of low-sampling trajectories based on criteria over a graph for including criteria of movement as a dimension in a trajectory data warehouse solution to carry out analytical tasks over moving objects and the environment where they move