An ever-increasing number of real-life applications produce spatio-temporal data that record the position of moving objects (persons, cars, vessels, aircrafts, etc.). In order to provide integrated views with other relevant data sources (e.g., weather, vessel databases, etc.), this data is represented in RDF and stored in knowledge bases with the following notable features: (a) the data is dynamic, since new spatio-temporal data objects are recorded every second, and (b) the size of the data is vast and can easily lead to scalability issues. As a result, this raises the need for efficient management of large-scale, dynamic, spatio-temporal RDF data. In this paper, we propose boosting the performance of spatio-temporal RDF queries by compressing the spatio-temporal information of each RDF entity into a unique integer value. We exploit this encoding in a filter-and-refine framework for processing of spatio-temporal RDF data efficiently. By means of an extensive evaluation on real-life data sets, we demonstrate the merits of our framework
