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References

    1. 1)
      • 1. Zissis, D., Xidias, E.K., Lekkas, D.: ‘Real-time vessel behaviour prediction’, Evol. Syst, 2016, 7, (1), pp. 2940.
    2. 2)
      • 2. Riveiro, M., Falkman, G.: ‘Interactive visualization of normal behavioural models and expert rules for maritime anomaly detection’. Sixth Int. Conf. on Computer Graphics, Imaging and Visualization, Tianjin, China, August 2009, pp. 459466.
    3. 3)
      • 3. Liu, Z., Liu, J., Li, Z., et al: ‘Characteristics analysis of traffic flow and ITS mathematical model’, J. Mar. Sci. Technol., 2017, 25, (2), pp. 230241.
    4. 4)
      • 4. Lei, P. R.: ‘A framework for anomaly detection in maritime trajectory behaviour’, Knowl. Inf. Syst., 2016, 47, (1), pp. 189214.
    5. 5)
      • 5. Lee, J.G., Han, J., Li, X.: ‘Trajectory outlier detection: a partition-and-detect framework’. IEEE 24th Int. Conf. on Data Engineering, Cancun, Mexico, April 2008, pp. 140149.
    6. 6)
      • 6. Gruber, T. R.: ‘A translation approach to portable ontology specifications’, Knowl. Acquis., 1993, 5, (2), pp. 199220.
    7. 7)
      • 7. Ye, J., Dasiopoulou, S., Stevenson, G., et al: ‘Semantic web technologies in pervasive computing: a survey and research roadmap’, Pervasive Mob. Comput., 2015, 23, pp. 125.
    8. 8)
      • 8. Sun, Y., Jara, A. J.: ‘An extensible and active semantic model of information organizing for the internet of things’, Pers. Ubiquitous Comput., 2014, 18, (8), pp. 18211833.
    9. 9)
      • 9. Nogueira, T.P., Braga, R.B., de Oliveira, C.T., et al: ‘FrameSTEP: a framework for annotating semantic trajectories based on episodes’, Expert Syst. Appl., 2018, 92, pp. 533545.
    10. 10)
      • 10. Vandecasteele, A., Devillers, R., Napoli, A.: ‘From movement data to objects behaviour using semantic trajectory and semantic events’, Mar. Geod., 2014, 37, (2), pp. 126144.
    11. 11)
      • 11. Darter, M., Lasky, T., Ravani, B.: ‘Transportation asset management and visualization using semantic models and Google Earth’, Transp. Res. Rec., J. Transp. Res. Board, 2008, 2024, (1), pp. 2734.
    12. 12)
      • 12. Zhang, C., Peng, Z.R., Zhao, T., et al: ‘Transformation of transportation data models from unified modelling language to web ontology language’, Transp. Res. Rec., J. Transp. Res. Board, 2008, 2064, (1), pp. 8189.
    13. 13)
      • 13. Spaccapietra, S., Parent, C., Damiani, M.L., et al: ‘A conceptual view on trajectories’, Data Knowl. Eng., 2008, 65, (1), pp. 126146.
    14. 14)
      • 14. Renso, C., Baglioni, M., de Macedo, J.A.F., et al: ‘How you move reveals who you are: understanding human behaviour by analyzing trajectory data’, Knowl. Inf. Syst., 2013, 37, pp. 132.
    15. 15)
      • 15. Nogueira, T.P., Martin, H.: ‘Querying semantic trajectory episodes’. Proc. of the 4th ACM SIGSPATIAL Int. Workshop on Mobile Geographic Information Systems, Washington, USA, November 2015, pp. 2330.
    16. 16)
      • 16. Baglioni, M., Macedo, J., Renso, C., et al: ‘An ontology-based approach for the semantic modelling and reasoning on trajectories’, Adv. Conceptual Model.–Chall. Oppor., 2008, 5232, pp. 344353.
    17. 17)
      • 17. Orellana, D., Renso, C.: ‘Developing an interactions ontology for characterizing pedestrian movement behaviour’, in ‘Movement-aware applications for sustainable mobility: technologies and approaches’ (IGI Global, Hershey, 2010), pp. 6286.
    18. 18)
      • 18. Fileto, R., May, C., Renso, C., et al: ‘The Baquara 2 knowledge-based framework for semantic enrichment and analysis of movement data’, Data Knowl. Eng., 2015, 98, pp. 104122.
    19. 19)
      • 19. Van Hage, W.R., Malaisé, V., de Vries, G., et al: ‘Combining ship trajectories and semantics with the simple event model (sem)’. Proc. of the 1st ACM Int. Workshop on Events in Multimedia, Beijing, China, October 2009, pp. 7380.
    20. 20)
      • 20. Vouros, G.A., Doulkeridis, C., Santipantakis, G., et al: ‘Taming big maritime data to support analytics’, in Popovich, V., Schrenk, M., Thill, J.-C., et al: ‘Information fusion and intelligent geographic information systems (IF&IGIS'17)’ (Springer Press, New York, 2018, 1st edn.), pp. 1527.
    21. 21)
      • 21. Arenas, H., Harbelot, B., Cruz, C.: ‘A semantic analysis of moving objects, using as a case study maritime voyages from eighteenth and nineteenth centuries’. The Sixth Int. Conf. on Advanced Geographic Information Systems, Applications, and Services, Barcelona, Spain, March 2014, pp. 4550.
    22. 22)
      • 22. Patroumpas, K., Alevizos, E., Artikis, A., et al: ‘Online event recognition from moving vessel trajectories’, Geoinformatica., 2017, 21, (2), pp. 389427.
    23. 23)
      • 23. Vandecasteele, A., Napoli, A.: ‘Spatial ontologies for detecting abnormal maritime behaviour’. Oceans 2012 MTS/IEEE Yeosu Conf.: The Living Ocean and Coast – Diversity of Resources and Sustainable Activities, Yeosu, North Korea, May 2012, pp. 17.
    24. 24)
      • 24. Nogueira, T. P.: ‘A framework for automatic annotation of semantic trajectories’. PhD thesis, Grenoble Alpes University, 2017.
    25. 25)
      • 25. SPARQL query language for RDF’. Available at: https://www.w3.org/TR/rdf-sparql-query/, accessed 15 January 2008.
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