Advertisement

Deploying Spatial-Stream Query Answering in C-ITS Scenarios

  • Thomas Eiter
  • Ryutaro Ichise
  • Josiane Parreira Xavier
  • Patrik SchneiderEmail author
  • Lihua Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11313)

Abstract

Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X between vehicles and the infrastructure, which will be used for the deployment of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. We show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, events detection, and advanced driving assistance systems. We define for them requirements derived from necessary domain-specific features and report, based on them, on the extension of our query language with temporal relations, delaying, numeric predictions and trajectory predictions. An experimental evaluation of queries that reflect the requirements, using the real-world traffic simulation tool provides evidence for the feasibility/efficiency of our approach in the new scenarios.

Notes

Acknowledgements

This work has been supported by the Austrian Research Promotion Agency project LocTraffLog (FFG 5886550) and DynaCon (FFG 861263).

References

  1. 1.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Com. ACM 26(11), 832–843 (1983)CrossRefGoogle Scholar
  2. 2.
    Andreone, L., Brignolo, R., Damiani, S., Sommariva, F., Vivo, G., Marco, S.: Safespot final report. Technical report D8.1.1 (2010). Available onlineGoogle Scholar
  3. 3.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: EP-SPARQL: a unified language for event processing and stream reasoning. Proc. WWW 2011, 635–644 (2011)Google Scholar
  4. 4.
    Artale, A., Kontchakov, R., Kovtunova, A., Ryzhikov, V., Wolter, F., Zakharyaschev, M.: First-order rewritability of temporal ontology-mediated queries. Proc. IJCAI 2015, 2706–2712 (2015)Google Scholar
  5. 5.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-SPARQL: a continuous query language for RDF data streams. Int. J. Semant. Comput. 4(1), 3–25 (2010)CrossRefGoogle Scholar
  6. 6.
    Beck, H., Dao-Tran, M., Eiter, T., Fink, M.: LARS: a logic-based framework for analyzing reasoning over streams. Proceedings of AAAI 2015, pp. 1431–1438 (2015)Google Scholar
  7. 7.
    Borgwardt, S., Lippmann, M., Thost, V.: Temporalizing rewritable query languages over knowledge bases. J. Web Sem. 33, 50–70 (2015)CrossRefGoogle Scholar
  8. 8.
    Brandt, S., Kalayci, E.G., Kontchakov, R., Ryzhikov, V., Xiao, G., Zakharyaschev, M.: Ontology-based data access with a horn fragment of metric temporal logic. In: Proceedings of AAAI 2017, pp. 1070–1076 (2017)Google Scholar
  9. 9.
    Calbimonte, J.-P., Mora, J., Corcho, O.: Query Rewriting in RDF Stream Processing. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 486–502. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-34129-3_30CrossRefGoogle Scholar
  10. 10.
    Calvanese, D., Giacomo, G.D., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: The DL-Lite family. J. Autom. Reasoning 39(3), 385–429 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Calvanese, D., Kharlamov, E., Nutt, W., Thorne, C.: Aggregate queries over ontologies. In: Proceedings of ONISW 2008, pp. 97–104 (2008)Google Scholar
  12. 12.
    Dell’Aglio, D., Valle, E.D., van Harmelen, F., Bernstein, A.: Stream reasoning: a survey and outlook. Data Sci. 1(1–2), 59–83 (2017). IOS PressGoogle Scholar
  13. 13.
    Eiter, T., Parreira, J.X., Schneider, P.: Detecting mobility patterns using spatial query answering over streams. In: Proceedings of Stream Reasoning Workshop (2017)Google Scholar
  14. 14.
    Eiter, T., Parreira, J.X., Schneider, P.: Spatial ontology-mediated query answering over mobility streams. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 219–237. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58068-5_14CrossRefGoogle Scholar
  15. 15.
    Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25073-6_24CrossRefGoogle Scholar
  16. 16.
    Lécué, F., Tallevi-Diotallevi, S., Hayes, J., Tucker, R., Bicer, V., Sbodio, M.L., Tommasi, P.: Smart traffic analytics in the semantic web with STAR-CITY: scenarios, system and lessons learned in Dublin city. J. Web Sem. 27, 26–33 (2014)CrossRefGoogle Scholar
  17. 17.
    Maier, D.: The Theory of Relational Databases. Computer Science Press, Rockville (1983)Google Scholar
  18. 18.
    Netten, B., Kester, L., Wedemeijer, H., Passchier, I., Driessen, B.: Dynamap: A dynamic map for road side its stations. In: Proceedings of ITS World Congress (2013)Google Scholar
  19. 19.
    Quoc, H.N.M., Le Phuoc, D.: An elastic and scalable spatiotemporal query processing for linked sensor data. In: Proceedings of SEMANTICS 2015, pp. 17–24. ACM (2015)Google Scholar
  20. 20.
    Rodríguez-Muro, M., Kontchakov, R., Zakharyaschev, M.: Ontology-based data access: Ontop of databases. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 558–573. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41335-3_35CrossRefGoogle Scholar
  21. 21.
    Shimada, H., Yamaguchi, A., Takada, H., Sato, K.: Implementation and evaluation of local dynamic map in safety driving systems. J. Transp. Technol. 5(2), 102–112 (2015)CrossRefGoogle Scholar
  22. 22.
    Stocker, M., Smith, M.: Owlgres: a scalable OWL reasoner. In: Proceedings of OWLED 2008 (2008)Google Scholar
  23. 23.
    Zhao, L., Ichise, R., Liu, Z., Mita, S., Sasaki, Y.: Ontology-based driving decision making: a feasibility study at uncontrolled intersections. IEICE Trans. Inf. Syst. 100(D(7)), 1425–1439 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Thomas Eiter
    • 1
  • Ryutaro Ichise
    • 2
    • 3
  • Josiane Parreira Xavier
    • 4
  • Patrik Schneider
    • 1
    • 4
    Email author
  • Lihua Zhao
    • 3
  1. 1.Vienna University of TechnologyViennaAustria
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.National Institute of Advanced Industrial Science and TechnologyTokyoJapan
  4. 4.Siemens AG ÖsterreichViennaAustria

Personalised recommendations