MASTER: a multiple aspects view on trajectories

For many years trajectory data have been treated as sequences of space-time points or stops and moves. However, with the explosion of the Internet of Things (IoT) and the ood of Big Data generated on the Internet, like weather channels and social network interactions, which can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions that can be integrated with trajectories. In this paper we introduce multiple aspect trajectories and we propose a robust conceptual and logical data model and a storage solution for efficient multiple aspect trajectory queries. The main strength of our data model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario.