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Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events

  • G. A. Vouros
  • A. Vlachou
  • G. Santipantakis
  • C. Doulkeridis
  • N. Pelekis
  • H. Georgiou
  • Y. Theodoridis
  • K. Patroumpas
  • E. Alevizos
  • A. Artikis
  • G. Fuchs
  • M. Mock
  • G. Andrienko
  • N. Andrienko
  • C. Claramunt
  • C. Ray
  • E. Camossi
  • A.-L. Jousselme
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10819)

Abstract

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results.

Keywords

Big Spatio-temporal data Moving objects Trajectory detection Data integration Events recognition/forecasting 

Notes

Acknowledgments

This work was supported by project datACRON, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 687591.

References

  1. 1.
    Alevizos, E., Artikis, A., Paliouras, G.: Event forecasting with pattern markov chains. In: Proceedings of DEBS, pp. 146–157 (2017)Google Scholar
  2. 2.
    Jousselme, A.-L., Ray, C., Camossi, E., Hadzagic, M., Claramunt, C., Bryan, K., Reardon, E., Ilteris, M.: Maritime Use Case and Scenarios, H2020 datAcron Deliverable D5.1 (2016). http://www.datacron-project.eu/
  3. 3.
    Dimou, A., Vander Sande, M., Colpaert, P., Verborgh, R., Mannens, E., de Walle, R.: RML: a generic language for integrated RDF mappings of heterogeneous data. In: Proceedings of LDOW (2014)Google Scholar
  4. 4.
    Kyzirakos, K., Vlachopoulos, I., Savva, D., Manegold, S., Koubarakis, M.: GeoTriples: a tool for publishing geospatial data as RDF graphs using R2RML mappings. In: Proceedings of the ISCW 2014, Posters & Demonstrations Track, Riva del Garda, Italy, vol. 1272, pp. 393–396 (2014)Google Scholar
  5. 5.
    Lefrançois, M., Zimmermann, A., Bakerally, N.: A SPARQL extension for generating RDF from heterogeneous formats. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 35–50. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58068-5_3CrossRefGoogle Scholar
  6. 6.
    Lin, X., Ma, S., Zhang, H., Wo, T., Huai, J.: One-pass error bounded trajectory simplification. PVLDB 10(7), 841–852 (2017)Google Scholar
  7. 7.
    Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B., Jurdak, R.: Bounded quadrant system: error-bounded trajectory compression on the go. In: Proceedings of ICDE, pp. 987–998 (2015)Google Scholar
  8. 8.
    Long, C., Wong, R.C.-W., Jagadish, H.V.: Trajectory simplification: on minimizing the direction-based error. PVLDB 8(1), 49–60 (2014)Google Scholar
  9. 9.
    Marz, N., Warren, J.: Big Data - Principles and Best Practices of Scalable Real-Time Data Systems. Manning Publications, Greenwich (2015)Google Scholar
  10. 10.
    Ngonga Ngomo, A.-C.: ORCHID – reduction-ratio-optimal computation of geo-spatial distances for link discovery. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 395–410. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41335-3_25CrossRefGoogle Scholar
  11. 11.
    Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N., Theodoridis, Y.: Online event recognition from moving vessel trajectories. GeoInformatica 21(2), 389–427 (2017)CrossRefGoogle Scholar
  12. 12.
    Santipantakis, G., Vouros, G., Doulkeridis, C., Vlachou, A., Andrienko, G., Andrienko, N., Fuchs, G., Garcia, J.M.C., Martinez, M.G.: Specification of semantic trajectories supporting data transformations for analytics: the datAcron ontology. In: Proceedings of SEMANTICS, pp. 17–24 (2017)Google Scholar
  13. 13.
    Sherif, M.A., Dreßler, K., Smeros, P., Ngomo, A.N.: Radon - rapid discovery of topological relations. In: Proceedings of AAAI 2017, pp. 175–181 (2017)Google Scholar
  14. 14.
    Smeros, P., Koubarakis, M.: Discovering spatial and temporal links among RDF data. In: Proceedings of LDOW (2016)Google Scholar
  15. 15.
    Bertrand, F., Bouju, A., Claramunt, C., Devogele, T., Ray, C.: Web architecture for monitoring and visualizing mobile objects in maritime contexts. In: Ware, J.M., Taylor, G.E. (eds.) W2GIS 2007. LNCS, vol. 4857, pp. 94–105. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76925-5_7CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • G. A. Vouros
    • 1
  • A. Vlachou
    • 1
  • G. Santipantakis
    • 1
  • C. Doulkeridis
    • 1
  • N. Pelekis
    • 1
  • H. Georgiou
    • 1
  • Y. Theodoridis
    • 1
  • K. Patroumpas
    • 1
  • E. Alevizos
    • 2
  • A. Artikis
    • 1
    • 2
  • G. Fuchs
    • 3
  • M. Mock
    • 3
  • G. Andrienko
    • 3
  • N. Andrienko
    • 3
  • C. Claramunt
    • 4
  • C. Ray
    • 4
  • E. Camossi
    • 5
  • A.-L. Jousselme
    • 5
  1. 1.University of PiraeusPiraeusGreece
  2. 2.NCSR ‘D’IITAgia ParaskeviGreece
  3. 3.Fraunhofer Institute IAISSankt AugustinGermany
  4. 4.Naval Academy Research InstituteBrestFrance
  5. 5.CMRELa SpeziaItaly

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