Advertisement

Modelling Mobile Object Activities Based on Trajectory Ontology Rules Considering Spatial Relationship Rules

  • Rouaa Wannous
  • Jamal Malki
  • Alain Bouju
  • Cécile Vincent
Part of the Studies in Computational Intelligence book series (SCI, volume 488)

Abstract

Several applications use devices and capture systems to record trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Temporal, spatial and domain related information are fundamental sources used to upgrade trajectories. The objective of semantic trajectories is to help users validating and acquiring more knowledge about mobile objects. In particular, temporal and spatial analysis of semantic trajectories is very important to understand the mobile object behaviour. This article proposes an ontology based modelling approach for semantic trajectories. This approach considers different and independent sources of knowledge represented by domain and spatial ontologies. The domain ontology represents mobile object activities as a set of rules. The spatial ontology represents spatial relationships as a set of rules. To achieve this approach, we need an integration between trajectory and spatial ontologies.

Keywords

Trajectory data modelling Modelling activities Ontology rules Spatial data modelling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baglioni, M., Macedo, J., Renso, C., Wachowicz, M.: An ontology-based approach for the semantic modelling and reasoning on trajectories. In: Song, I.-Y., et al. (eds.) ER 2008 Workshops. LNCS, vol. 5232, pp. 344–353. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Bogorny, V., Heuser, C.A., Alvares, L.O.: A conceptual data model for trajectory data mining. In: Fabrikant, S.I., Reichenbacher, T., van Kreveld, M., Schlieder, C. (eds.) GIScience 2010. LNCS, vol. 6292, pp. 1–15. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Fedak, M.A., Lovell, P., Grant, S.M.: Two approaches to compressing and interpreting time-depth information as collected by time-depth recorders and satellite-linked data recorders. Mar. Mamm Sci. 17(1), 94–110 (2001)CrossRefGoogle Scholar
  4. 4.
    GeoPKDD. Geographic privacy-aware knowledge discovery and delivery. Coordinator: KDDLAB, Knowledge Discovery nad Delivery Laboratory, ISTI-CNR and University of Pisa (2005), http://www.geopkdd.eu/
  5. 5.
    Huang, B., Claramunt, C.: STOQL: An ODMG-based spatio-temporal object model and query language (2002)Google Scholar
  6. 6.
    Macedo, J., Vangenot, C., Othman, W., Pelekis, N., Frentzos, E., Kuijpers, B., Ntoutsi, I., Spaccapietra, S., Theodoridis, Y.: Trajectory data models. In: Mobility, Data Mining and Privacy, pp. 123–150. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Malki, J., Bouju, A., Mefteh, W.: An ontological approach modeling and reasoning on trajectories. taking into account thematic, temporal and spatial rules. TSI. Technique et Science Informatiques 31(1), 71–96 (2012)CrossRefGoogle Scholar
  8. 8.
    Malki, J., Wannous, R., Bouju, A., Vincent, C.: Temporal reasoning in trajectories using an ontological modelling approach. Control and Cybernetics 41, 1–16 (2012)Google Scholar
  9. 9.
    Matthew, P.: A framework to support spatial, temporal and thematic analytics over semantic web data. PhD thesis, Wright State Univ. (2008)Google Scholar
  10. 10.
    MODAP. Mobility, data mining and privacy (2009), http://www.modap.org/
  11. 11.
    Oracle. Oracle spatial developer’s guide 11g release 2 (11.2) (1996), http://docs.oracle.com/cd/E11882/
  12. 12.
    Parent, C., Spaccapietra, S., Zimanyi, E.: Spatio-temporal conceptual models: Data structures + space + time. In: Proceedings of the 7th ACM International Symposium on Advances in Geographic Information Systems, pp. 26–33. ACM (1999)Google Scholar
  13. 13.
    Perry, M., University, W.S.: A Framework to Support Spatial, Temporal and Thematic Analytics Over Semantic Web Data. Wright State University (2008)Google Scholar
  14. 14.
    Spaccapietra, S., Parent, C., Damiani, M., Demacedo, J., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data and Knowledge Engineering 65(1), 126–146 (2008)CrossRefGoogle Scholar
  15. 15.
    Wannous, R., Malki, J., Bouju, A., Vincent, C.: Time integration in semantic trajectories using an ontological modelling approach: A case study with experiments, optimization and evaluation of an integration approach. In: Pechenizkiy, M., Wojciechowski, M. (eds.) New Trends in Databases & Inform. AISC, vol. 185, pp. 187–198. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Werner, K., Martin, R.: Open GIS consortium, inc, openGIS simple features specification for SQL (1999)Google Scholar
  17. 17.
    Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: SeMiTri: A framework for semantic annotation of heterogeneous trajectories. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 259–270. ACM (2011)Google Scholar
  18. 18.
    Yan, Z., Macedo, J., Parent, C., Spaccapietra, S.: Trajectory ontologies and queries. Transactions in GIS 12(s1), 75–91 (2008)CrossRefGoogle Scholar
  19. 19.
    Yan, Z., Parent, C., Spaccapietra, S., Chakraborty, D.: A hybrid model and computing platform for spatio-semantic trajectories. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 60–75. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rouaa Wannous
    • 1
  • Jamal Malki
    • 1
  • Alain Bouju
    • 1
    • 2
  • Cécile Vincent
    • 2
  1. 1.L3i laboratoryUniv of La RochelleLa RochelleFrance
  2. 2.LIENSs laboratoryUniv of La RochelleLa RochelleFrance

Personalised recommendations