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Impressum

12.08.2019
L. Friedel

The Poster: "Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems" was accepted at AALT Workshop co-located with ECML/PKDD 2019

Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems. Katarzyna Juraszek, Nidhi Saini, Marcela Charfuelan, Holmer Hemsen and Volker Markl. 4th Workshop on Advanced Analytics and Learning on Temporal Data AALTD co-located with ECML/PKDD 2019


Abstract:
The growing number of vehicle data being constantly reported by a variety of remote sensors, such as Automatic Identification Systems (AIS), requires new data analytics methods that can operate at high data rates and are highly scalable. Based on a real-life data set from maritime transport, we propose a large scale vessels 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.

If you want to learn more about the 4th ALTD@ECML/PKDD 2019 visit: https://project.inria.fr/aaltd19/