The growing number of vessel data being constantly reported by a variety of remote sensors, such as the Automatic Identification System (AIS), requires new data analytics that can operate at high data rates and are highly scalable. Based on a real-world dataset from maritime transport, we propose a large scale vessel trajectory tracking application implemented in the distributed stream processing system Apache Flink. By implementing a state-space model (SSM) - the Extended Kalman Filter (EKF) - we firstly demonstrate that an implementation of SSMs is feasible in modern distributed data flow systems and secondly we show that we can reach a high performance by leveraging the inherent parallelization of the distributed system. In our experiments we show that the distributed tracking system is able to handle a throughput of several hundred vessels per ms. Moreover, we show that the latency to predict the position of a vessel is well below 500 ms on average, allowing for real-time applications.
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