On querying and mining semantic-aware mobility timelines

The explosion of available positioning information associated with the inferred or user-declared semantics of the respective locations has contributed in what is called the Big Data era by posing new challenges to the mobility data management and mining research community. Motivated by a series of challenges posed in Pelekis et al. (SIGKDD Explor 15(1):23–32, 2013 ), in this paper, we present a unified framework for the management and the analysis of LifeSteps, i.e., data objects about human mobility including both (raw) spatio-temporal trajectories and their semantic counterpart. In particular, we provide solutions for developing real-world semantic-aware moving object database and trajectory data warehouse systems and we devise respective query processing algorithms. Our experimental study on realistic synthetic data including synchronized raw (i.e., GPS log) and semantic (i.e., diaries) information verifies the effectiveness and efficiency of the proposed framework.