Nowadays, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics.
In many application, such as maritime or air-traffic data management, data analysis of mobility data requires weather information related to the movement of objects, as this has significant effects on various characteristics of its trajectory (route, speed, and fuel consumption).
Unfortunately, mobility databases do not contain weather information, thus hindering the joint analysis of mobility and weather data. Motivated by this evident need of many real-life applications, in this paper, we develop a system for integrating mobility data with external weather sources. Our system is designed to operate at the level of a spatiotemporal position and can be used to efficiently produce weather integrated data sets from raw positions of moving objects. Salient features of our approach include operating in an online manner and being reusable across diverse mobility data (urban, maritime, air-traffic).
Further, we extend our approach to:
- handle more complex geometries than simple positions (e.g., associating weather with a 3D sector),
- produce output in RDF, thus generating linked data.
We demonstrate the efficiency of our system using experiments on large, real-life data sets.
