A complex event processing approach to detect abnormal behaviours in the marine environment

Over the last years, many data-sources have become available to monitor the marine traffic. This has motivated the development of support systems to automatically detect vessels’ behaviours of interest. The present work states a novel approach in this domain following the Complex Event Processing (CEP) paradigm. As a proof of concept, a CEP-based system has been developed to timely detect a set of vessel’s abnormal behaviours by performing an event-based processing of Automatic Identification System data. Experiments based on real-world and synthetic data proved the suitability and feasibility of the proposal.

[1]  Jean Roy,et al.  Rule-based expert system for maritime anomaly detection , 2010, Defense + Commercial Sensing.

[2]  Bradley J. Rhodes,et al.  Adaptive Mixture-Based Neural Network Approach for Higher-Level Fusion and Automated Behavior Monitoring , 2009, 2009 IEEE International Conference on Communications.

[3]  A. Gómez-Skarmeta,et al.  Unified Management Of Heterogeneous SensorsFor Complex Event Processing , 2008 .

[4]  Gregoris Mentzas,et al.  Dynamic event subscriptions in distributed event based architectures , 2013, Expert Syst. Appl..

[5]  Gregoris Mentzas,et al.  A goal driven dynamic event subscription approach , 2012, DEBS.

[6]  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.

[7]  M. Vespe,et al.  Unsupervised learning of maritime traffic patterns for anomaly detection , 2012 .

[8]  Leto Peel,et al.  Maritime anomaly detection using Gaussian Process active learning , 2012, 2012 15th International Conference on Information Fusion.

[9]  Leto Peel,et al.  Fast Maritime Anomaly Detection using KD Tree Gaussian Processes , 2011 .

[10]  Sven Meister.  Telemedical Events: Intelligent Delivery of Telemedical Values Using CEP and HL7 , 2011, BIR Workshops.

[11]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[12]  Antonio F. Gómez-Skarmeta,et al.  A Cooperative Approach to Traffic Congestion Detection With Complex Event Processing and VANET , 2012, IEEE Transactions on Intelligent Transportation Systems.

[13]  Mark R. Morelande,et al.  Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction , 2008, 2008 11th International Conference on Information Fusion.

[14]  Antonio F. Gómez-Skarmeta,et al.  A complex event processing approach to perceive the vehicular context , 2015, Inf. Fusion.

[15]  Thomas Brinkhoff,et al.  A Framework for Generating Network-Based Moving Objects , 2002, GeoInformatica.

[16]  Richard O. Lane,et al.  Maritime anomaly detection and threat assessment , 2010, 2010 13th International Conference on Information Fusion.

[17]  D. Luckham.  Event Processing for Business: Organizing the Real-Time Enterprise , 2011 .

[18]  Paula Savioja,et al.  Challenges of developing the complex socio-technical system: realising the present, acknowledging the past, and envisaging the future of vessel traffic services. , 2007, Applied ergonomics.

[19]  Max Krüger,et al.  A generic Bayesian Network for identification and assessment of objects in maritime surveillance , 2012, 2012 15th International Conference on Information Fusion.

[20]  Hein Meling,et al.  AdScorer: an event-based system for near real-time impact analysis of television advertisements (industry article) , 2012, DEBS.

[21]  Ralf Hartmut Güting,et al.  Moving Objects Databases , 2005 .

[22]  Allen M. Waxman,et al.  Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.

[23]  Aldo Napoli,et al.  The automatic identification system of maritime accident risk using rule-based reasoning , 2012, 2012 7th International Conference on System of Systems Engineering (SoSE).

[24]  Opher Etzion,et al.  Event Processing in Action , 2010 .

[25]  Kevin B. Korb,et al.  Learning Abnormal Vessel Behaviour from AIS Data with Bayesian Networks at Two Time Scales , 2010 .

[26]  Jean Roy,et al.  Anomaly detection in the maritime domain , 2008, SPIE Defense + Commercial Sensing.