Fine-Tuned Compressed Representations of Vessel Trajectories

Fikioris G., Patroumpas K., Artikis A., Paliouras G. and Pitsikalis M. - International Conference on Information and Knowledge Management (CIKM) - 2020


In the maritime domain, vessels typically maintain straight, predictable routes at open sea, except in the rare cases of adverse weather conditions, accidents and traffic restrictions. Consequently, large amounts of streaming positional updates from vessels can hardly contribute additional knowledge about their actual motion patterns. We have been developing a system for vessel trajectory compression discarding a significant part of the original positional updates, with minimal trajectory reconstruction error. In this work, we present an extension of this system, that allows the user to fine-tune trajectory compression according to the requirements of a given application. The extended system avoids the issues of hyper-parameter tuning, supports incremental optimization and facilitates composite maritime event recognition. Finally, we report empirical results from a comprehensive empirical evaluation against two real-world datasets of vessel positions.

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This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 825070.

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