Analyzing Vessel Behavior Using Process Mining

  • Fabrizio M. MaggiEmail author
  • Arjan J. Mooij
  • Wil M. P. van der Aalst


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.


Reference Model Business Process Management Process Instance Maritime Safety Declare Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


  1. 1.
    Bauer A, Leucker M, Schallhart C (2010) Comparing LTL semantics for runtime verification. J Log Comput 20(3):651–674Google Scholar
  2. 2.
    Beer I, Ben-David S, Eisner C, Rodeh Y (2001) Efficient detection of vacuity in temporal model checking. Form Methods Syst Des 18(2):141–163Google Scholar
  3. 3.
    Eisner C, Fisman D, Havlicek J, Mcisaac A, Lustig Y, van Campenhout D (2003) Reasoning with temporal logic on truncated paths. In: International conference on computer aided verification, vol 2725. Springer, Berlin/New York, pp 27–40Google Scholar
  4. 4.
    Günther CW, van der Aalst WMP (2007) Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Business process management conference, vol 4714. Springer, Berlin/New York, pp 328–343Google Scholar
  5. 5.
    International Telecommunications Union (2001) Technical characteristics for a universal shipborne Automatic Identification System using time division multiple access in the VHF maritime mobile band, Recommendation ITU-R M.1371-1Google Scholar
  6. 6.
    Jagadeesh Chandra Bose RP, van der Aalst WMP (2009) Abstractions in process mining: a taxonomy of patterns. In: Business process management conference. Springer, Berlin/New York, pp 159–175Google Scholar
  7. 7.
    Kupferman O, Vardi MY (2003) Vacuity detection in temporal model checking. Int J Softw Tools Technol Transf 4(2):224–233Google Scholar
  8. 8.
    Leucker M, Schallhart C (2009) A brief account of runtime verification. J Log Algebraic Program 78(5):293–303Google Scholar
  9. 9.
    Maggi FM, Montali M, Westergaard M, van der Aalst WMP (2011) Monitoring business constraints with linear temporal logic: an approach based on colored automata. In: Rinderle-Ma R, Toumani F, Wolf K (eds) International conference on business process management (BPM 2011). Lecture notes in computer science, vol 6896. Springer, Berlin/New York, pp 132–147Google Scholar
  10. 10.
    Maggi FM, Mooij AJ, van der Aalst WMP (2011) User-guided discovery of declarative process models. In: IEEE symposium on computational intelligence and data mining (CIDM). IEEE Computer Society, Piscataway, pp 192–199Google Scholar
  11. 11.
    Maggi FM, Westergaard M, Montali M, van der Aalst WMP (2012) Runtime verification of LTL-based declarative process models. In: Khurshid S, Sen K (eds) International conference on runtime verification (RV 2011). Lecture notes in computer science, vol 7186. Springer, Berlin/New York, pp 131–146Google Scholar
  12. 12.
    Pesic M (2008) Constraint-based workflow management systems: shifting Controls to users. Ph.D. thesis, Beta Research School for Operations Management and Logistics, Eindhoven University of TechnologyGoogle Scholar
  13. 13.
    Pesic M, Schonenberg H, van der Aalst WMP (2007) Declare: full support for loosely-structured processes. In: IEEE international EDOC conference. IEEE, Los Alamitos, pp 287–300Google Scholar
  14. 14.
    Pnueli A (1977) The temporal logic of programs. In: Annual IEEE symposium on foundations of computer science, Providence, pp 46–57Google Scholar
  15. 15.
    Simmonds J, Davies J, Gurfinkel A, Chechik M (2010) Exploiting resolution proofs to speed up LTL vacuity detection for BMC. Int J Softw Tools Technol Transf 12(5):319–335Google Scholar
  16. 16.
    van der Aalst WMP (2011) Process mining: discovery, conformance and enhancement of business processes. Springer, Berlin Heidelberg/New YorkGoogle Scholar
  17. 17.
    van der Aalst WMP, Pesic M, Schonenberg H (2009) Declarative workflows: balancing between flexibility and support. Comput Sci Res Dev 23(2):99–113Google Scholar
  18. 18.
    Weske M (2007) Business process management: concepts, languages, architectures. Springer, Berlin/New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Fabrizio M. Maggi
    • 1
    Email author
  • Arjan J. Mooij
    • 1
  • Wil M. P. van der Aalst
    • 1
  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands

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