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Martin Pagel

Mo 25.11.2019, 16:00 Uhr TU Berlin, EN building, seminar room EN 719 (7th floor), Einsteinufer 17, 10587 Berlin: "Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems" (Katarzyna Juraszek, TU Berlin)

The growing number of vehicle data being constantly reported by a variety of remote sensors, such as Automatic Identication 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.

Short Bio:
Katarzyna Juraszek has recently graduated with a B.S in Computer Science from Technische Universität Berlin. Her final thesis was focusing on developing an implementation of the Extended Kalman Filter algorithm and putting it into use within stream-processing framework Flink. After working on this subject with other co-authors from DFKI, this work was accepted to ECML PKDD conference in Würzburg, Germany this year, giving her the opportunity to present it there in a form of poster. She holds a Master's degree in Network and Information Economics as well as Business Intelligence from Maastricht University in the Netherlands. In the past 4 years she was working in the field of data analytics and data engineering at Zalando in Berlin, where she was working with Big Data problems in a more commercial setting.