Scalable enrichment of mobility data with weather information

More and more real-life applications for mobility analytics require the joint exploitation of positional information of moving objects together with weather data that correspond to the movement. In particular, this is evident in fleet management applications for improved routing and reduced fuel consumption, in the maritime domain for more accurate trajectory prediction, as well as in air-traffic management for predicting regulations and reducing delays. Motivated by such applications, in this paper, we present a system for the enrichment of mobility data with weather information. Our main application scenario concerns streaming positional information (such as GPS traces of vehicles) that is collected and is enriched in an online fashion with stored weather data. We present the system architecture of a centralized version that runs on a single machine and exploits caching to improve its efficiency. Also, we extend our approach to a parallel implementation on top of Apache Kafka, which can scale to hundreds of thousands of processed records when provided with more computing nodes. Furthermore, we present extensions of our system for: (a) enrichment of more complex geometries than point data, and (b) providing linked RDF data as output. Our experimental evaluation on a medium-sized cluster shows the scalability of our approach in terms of number of processed records per second.