Event Processing for Maritime Situational Awareness

Numerous illegal and dangerous activities take place at sea, including violations of ship emission rules, illegal fishing, illegal discharges of oil and garbage, smuggling, piracy and more. We present our efforts to combine two stream reasoning technologies for detecting such activities in real time: a formal, computational framework for composite maritime event recognition, based on the Event Calculus, and an industry-strong maritime anomaly detection service, capable of processing daily real-world data volumes.

[1]  Naouma Kourti,et al.  The SUMO Ship Detector Algorithm for Satellite Radar Images , 2017, Remote. Sens..

[2]  Alexander Artikis,et al.  An Event Calculus for Event Recognition , 2015, IEEE Transactions on Knowledge and Data Engineering.

[3]  Nikos Pelekis,et al.  Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events , 2018, W2GIS.

[4]  Keith L. Clark,et al.  Negation as Failure , 1987, Logic and Data Bases.

[5]  Cyril Ray,et al.  Composite Event Recognition for Maritime Monitoring , 2019, DEBS.

[6]  Antonio F. Gómez-Skarmeta,et al.  A complex event processing approach to detect abnormal behaviours in the marine environment , 2016, Inf. Syst. Frontiers.

[7]  George A. Vouros,et al.  A Stream Reasoning System for Maritime Monitoring , 2018, TIME.

[8]  Luca Cazzanti,et al.  A distributed approach to estimating sea port operational regions from lots of AIS data , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[9]  Joeri van Laere,et al.  Evaluation of a workshop to capture knowledge from subject matter experts in maritime surveillance , 2009, 2009 12th International Conference on Information Fusion.

[10]  Nikos Pelekis,et al.  Big Data Analytics for Time Critical Mobility Forecasting: Recent Progress and Research Challenges. , 2018 .

[11]  Marios Vodas,et al.  A distributed lightning fast maritime anomaly detection service , 2019, OCEANS 2019 - Marseille.

[12]  Evangelos Michelioudakis,et al.  Semi-supervised online structure learning for composite event recognition , 2019, Machine Learning.

[13]  Nikos Pelekis,et al.  Online event recognition from moving vessel trajectories , 2016, GeoInformatica.

[14]  Alexander Artikis,et al.  Online learning of event definitions , 2016, Theory and Practice of Logic Programming.

[15]  Nathan Marz,et al.  Big Data: Principles and best practices of scalable realtime data systems , 2015 .

[16]  Zaphkiel Fleitas Ruiz Long Range Identification and Tracking System , 2017 .

[17]  Simon Jennings,et al.  Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data , 2007 .