Abstract
In the maritime domain, electronic sensors such as AIS receivers and radars collect large amounts of data about the vessels in a certain geographical area. We investigate the use of process mining techniques for analyzing the behavior of the vessels based on these data. In the context of maritime safety and security, the goal is to support operators in identifying suspicious behavior that may indicate accidents or undesired activities such as smuggling and piracy. Our approach consists of two phases. In the first phase, process mining is used offline to extract from historical data a reference model of the normal vessel behavior, which can be adapted by experienced operators and domain experts. In the second phase, process mining is used online to verify whether the current vessel behavior is compliant with the reference model, thus allowing for the identification of suspicious behavior.
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Notes
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All the experiments illustrated in this chapter use AIS data registered along the Dutch coast in the first week of June 2007
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Acknowledgements
This research has been carried out as a part of the Poseidon project at Thales under the responsibilities of the Embedded Systems Institute (ESI). This project is partially supported by the Dutch Ministry of Economic Affairs under the BSIK program.
The authors wish to thank Marco Montali and Michael Westergaard for their contribution in the development of the approach to monitor Declare models described in this chapter.
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Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P. (2013). Analyzing Vessel Behavior Using Process Mining. In: van de Laar, P., Tretmans, J., Borth, M. (eds) Situation Awareness with Systems of Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6230-9_9
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