Manolis Pitsikalis
ABSTRACT
Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.
- Sten Andler, Mikael Fredin, Per Gustavsson, Joeri Laere, Maria Nilsson, and Pontus Svenson. 2009. SMARTracIn--A concept for spoof resistant tracking of vessels and detection of adverse intentions.Google Scholar
- Alexander Artikis and Marek J. Sergot. 2010. Executable specification of open multi-agent systems. Logic Journal of the IGPL 18, 1 (2010), 31--65.Google ScholarCross Ref
- Alexander Artikis, Marek J. Sergot, and Georgios Paliouras. 2015. An Event Calculus for Event Recognition. IEEE Trans. Knowl. Data Eng. 27, 4 (2015), 895--908.Google ScholarDigital Library
- Elena Camossi, Anne-Laure Jousselme, Cyril Ray, Melita Hadzagic, Richard Dreo, and Christophe Claramunt. 2017. Maritime Experiments Specification, H2020 datAcron project deliverable D5.3. (2017). http://datacron-project.eu/.Google Scholar
- Konstantinos Chatzikokolakis, Dimitrios Zissis, Giannis Spiliopoulos, and Konstantinos Tserpes. 2018. Mining Vessel Trajectory Data for Patterns of Search and Rescue. Zenodo.Google Scholar
- Keith L. Clark. 1978. Negation as Failure. In Logic and Databases, H. Gallaire and J. Minker (Eds.). Plenum Press, 293--322.Google Scholar
- Harm Greidanus, Marlene Alvarez, Carlos Santamaria, François-Xavier Thoorens, Naouma Kourti, and Pietro Argentieri. 2017. The SUMO Ship Detector Algorithm for Satellite Radar Images. Remote Sensing 9, 3 (2017). http://www.mdpi.com/2072-4292/9/3/246Google Scholar
- Bilal Idiri and Aldo Napoli. 2012. The automatic identification system of maritime accident risk using rule-based reasoning. In 2012 7th International Conference on System of Systems Engineering (SoSE). 125--130.Google ScholarCross Ref
- Anne-Laure Jousselme, Elena Camossi, Melita Hadzagic, Cyril Ray, Christophe Claramunt, Eric Reardon, Karna Bryan, and Michael Ilteris. 2016. A Fishing Monitoring Use Case in Support to Collaborative Research. In In Proceedings of Maritime Knowledge Discovery and Anomaly Detection Workshop. 57--61.Google Scholar
- Anne-Laure Jousselme, Cyril Ray, Elena Camossi, Melita Hadzagic, Christophe Claramunt, Karna Bryan, Eric Reardon, and Michael Ilteris. 2016. Maritime Use Case description, H2020 datAcron project deliverable D5.1. (2016). http://datacron-project.eu/.Google Scholar
- Nikos Katzouris, Alexander Artikis, and Georgios Paliouras. 2015. Incremental learning of event definitions with Inductive Logic Programming. Machine Learning 100, 2-3 (2015), 555--585. Google ScholarDigital Library
- Nikos Katzouris, Alexander Artikis, and Georgios Paliouras. 2016. Online learning of event definitions. TPLP 16, 5-6 (2016), 817--833.Google Scholar
- Robert A. Kowalski and Marek J. Sergot. 1986. A Logic-based Calculus of Events. New Generation Comput. 4, 1 (1986), 67--95. Google ScholarDigital Library
- L. Le Masson and J. Barre. 2018. Complex Event Recognition support for maritime monitoring in the context of European datAcron project. Final year project. French naval academy.Google Scholar
- Fabrizio Natale, Maurizio Gibin, Alfredo Alessandrini, Michele Vespe, and Anton Paulrud. 2015. Mapping Fishing Effort through AIS Data. PLOS ONE 10, 6 (06 2015), 1--16.Google Scholar
- Kostas Patroumpas, Elias Alevizos, Alexander Artikis, Marios Vodas, Nikos Pelekis, and Yannis Theodoridis. 2017. Online event recognition from moving vessel trajectories. GeoInformatica 21, 2 (2017), 389--427. Google ScholarDigital Library
- M. Pitsikalis and A. Artikis. 2019. Composite Maritime Events (Version 0.1) {Data set}. https://zenodo.org/record/2557290.Google Scholar
- Manolis Pitsikalis, Ioannis Kontopoulos, Alexander Artikis, Elias Alevizos, Paul Delaunay, Jules-Edouard Pouessel, Richard Dreo, Cyril Ray, Elena Camossi, Anne-Laure Jousselme, and Melita Hadzagic. 2018. Composite Event Patterns for Maritime Monitoring. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN '18). ACM, New York, NY, USA, Article 29, 4 pages.Google ScholarDigital Library
- Teodor Przymusinski. 1987. On the Declarative Semantics of Stratified Deductive Databases and Logic Programs. In Foundations of Deductive Databases and Logic Programming. Morgan. Google ScholarDigital Library
- Cyril Ray, Elena Camossi, Richard Dréo, Anne-Laure Jousselme, Clément Iphar, Maximilian Zocholl, and Melita Hadzagic. 2019. Use case design and big data analytics evaluation for fishing monitoring. In OCEANS 2019.Google Scholar
- Cyril Ray, Elena Camossi, Anne-Laure Jousselme, Melita Hadzagic, Christophe Claramunt, and Ernie Batty. 2016. Maritime Data Preparation and Curation, H2020 datAcron project deliverable D5.2. (2016). http://datacron-project.eu/.Google Scholar
- Cyril Ray, Richard Dréo, Elena Camossi, and Anne-Laure Jousselme. 2018. Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance (Version 0.1). https://zenodo.org/record/1167595.Google Scholar
- Maria Riveiro. 2011. Visual Analytics for Maritime Anomaly Detection. Ph.D. Dissertation. University of SkövdeUniversity of Skövde, School of Humanities and Informatics, The Informatics Research Centre.Google Scholar
- Jean Roy and Michael Davenport. 2010. Exploitation of maritime domain ontologies for anomaly detection and threat analysis. In 2010 International WaterSide Security Conference. 1--8.Google ScholarCross Ref
- Georgios M. Santipantakis, Akrivi Vlachou, Christos Doulkeridis, Alexander Artikis, Ioannis Kontopoulos, and George A. Vouros. 2018. A Stream Reasoning System for Maritime Monitoring. In 25th International Symposium on Temporal Representation and Reasoning, TIME 2018, Warsaw, Poland, October 15-17, 2018. 20:1--20:17.Google Scholar
- Hamed Yaghoubi Shahir, Uwe Glasser, Amir Yaghoubi Shahir, and Hans Wehn. 2015. Maritime situation analysis framework: Vessel interaction classification and anomaly detection. In 2015 IEEE International Conference on Big Data (Big Data). 1279--1289.Google ScholarDigital Library
- Lauro Snidaro, Ingrid Visentini, and Karna Bryan. 2015. Fusing Uncertain Knowledge and Evidence for Maritime Situational Awareness via Markov Logic Networks. Inf. Fusion 21 (Jan. 2015), 159--172. Google ScholarDigital Library
- Fernando Terroso-Saenz, Mercedes Valdes-Vela, and Antonio F. Skarmeta-Gomez. 2016. A complex event processing approach to detect abnormal behaviours in the marine environment. Information Systems Frontiers 18, 4 (01 Aug 2016), 765--780. Google ScholarDigital Library
- Joeri van Laere and Maria Nilsson. 2009. Evaluation of a workshop to capture knowledge from subject matter experts in maritime surveillance. In Proceedings of FUSION. 171--178.Google Scholar
- Arnaud Vandecasteele and Aldo Napoli. 2012. An enhanced spatial reasoning ontology for maritime anomaly detection. In 2012 7th International Conference on System of Systems Engineering (SoSE). 1--6.Google ScholarCross Ref
- Michele Vespe, Maurizio Gibin, Alfredo Alessandrini, Fabrizio Natale, Fabio Mazzarella, and Giacomo C. Osio. 2016. Mapping EU fishing activities using ship tracking data. Journal of Maps 12, sup1 (2016), 520--525.Google Scholar
Index Terms
- Composite Event Recognition for Maritime Monitoring
Recommendations
Maritime Event Recognition
DEBS '19: Proceedings of the 13th ACM International Conference on Distributed and Event-based SystemsIn present times, where the shipping industry has an important role in the global trade, there is an increased need for safer and more secure shipping. Maritime Monitoring systems allow the real-time detection of dangerous, suspicious and illegal vessel ...
Composite Event Patterns for Maritime Monitoring
SETN '18: Proceedings of the 10th Hellenic Conference on Artificial IntelligenceMaritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We have been developing a composite event recognition system for maritime monitoring in the Event ...
National and international maritime situational awareness model examples and the effects of North Stream Pipelines sabotage
AbstractWhile many countries and international organizations with maritime security interests and rights at sea have developed new security strategies or policies in the wake of the 9/11 terrorist attacks in the United States of America, they ...
Comments