Querying semantic trajectory episodes

Trajectory acquisition, management, and processing are important tasks for any application that deals with spatiotemporal data. In order to perform these tasks effectively, it is important to rely on flexible structures. Many data models have been proposed for representing spatiotemporal traces. However, modeling trajectory characteristics and context information is still a challenge. In this work, we introduce the STEP ontology (Semantic Trajectory Episodes) for trajectory enrichment. In order to model this domain, we structure trajectories and related contextual data in terms of semantic episodes that allow describing various characteristics of the traces and context along time and space dimensions. We demonstrate the usage of the STEP ontology for enriching raw trajectories and show how the proposed model may be useful for trajectory analysis tasks.