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Inference on Semantic Trajectory Data Warehouse Using an Ontological Approach

  • Thouraya Sakouhi
  • Jalel Akaichi
  • Jamal Malki
  • Alain Bouju
  • Rouaa Wannous
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

Using location aware devices is getting more and more spread, generating then a huge quantity of mobility data. The latter describes the movement of mobile objects and is called as well Trajectory data. In fact, these raw trajectories lack contextual information about the moving object goals and his activity during the travel. Therefore, the former must be enhanced with semantic information to be called then Semantic Trajectory. The semantic models proposed in the literature are in many cases ontology-based, and are composed of thematic, temporal and spatial ontologies and rules to support inference and reasoning tasks on data. Thus, calculating inference on moving objects trajectories considering all thematic, spatial, and temporal rules can be very long depending on the amount of data involved in this process. On the other side, TDW is an efficient tool for analyzing and extracting valuable information from raw mobility data. For that we propose throughout this work a TDW design, inspired from an ontology model. We will emphasis the trajectory to be seen as a first class semantic concept. Then we apply the inference on the proposed model to see if we can enhance it and make the complexity of this mechanism manageable.

Keywords

Trajectory data semantic modeling ontology trajectory data warehouse inference 

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References

  1. 1.
    Wannous, R., Malki, J., Bouju, A., Vincent, C.: Modelling mobile object activities based on trajectory ontology rules considering spatial relationship rules. In: Amine, A., Mohamed, O.A., Bellatreche, L. (eds.) Modeling Approaches and Algorithms. SCI, vol. 488, pp. 249–258. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Wolfson, O., Sistla, P., Xu, B., Zhou, J., Chamberlain, S.: Domino: Databases for moving objects tracking. In: ACM SIGMOD, pp. 547–549 (1999)Google Scholar
  3. 3.
    Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Syst. 25, 1–42 (2000)CrossRefGoogle Scholar
  4. 4.
    Spaccapietra, S., Parent, C., Damiani, M., Demacedo, J., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data & Knowledge Engineering 65(1), 126–146 (2008)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Yan, Z., Macedo, J., Parent, C., Spaccapietra, S.: Trajectory ontologies and queries. Transactions in GIS 12(s1), 75–91 (2008)CrossRefGoogle Scholar
  7. 7.
    Moreno, F., Arias, J.A.E., Losada, B.: A conceptual spatio-temporal multidimensional model. Revista IngenierÃas Universidad de MedellÃn 9, 175–183 (2010)Google Scholar
  8. 8.
    Zhou, L., Bao, M., Yang, N., Lao, Y., Zhang, Y., Tian, Y.: Spatio-temporal analysis of weibo check-in data based on spatial data warehouse. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds.) GRMSE 2013 Part II. CCIS, vol. 399, pp. 466–479. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Marketos, G.D.: Data warehousing and mining techniques for moving object databases (2009)Google Scholar
  10. 10.
    Thenmozhi, M., Vivekanandan, K.: An ontology based hybrid approach to derive multidimensional schema for data warehouse. International Journal of Computer Applications 54, 36–42 (2012)CrossRefGoogle Scholar
  11. 11.
    Bellatreche, L., Khouri, S., Berkani, N.: Semantic data warehouse design: From ETL to deployment à la carte. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013, Part II. LNCS, vol. 7826, pp. 64–83. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Campora, S., Fernandes, J., Spinsanti, L.: St-toolkit: A framework for trajectory data warehousing. In: AGILE Conf. Lecture Notes in Geoinformation and Cartography. Springer (2011)Google Scholar
  13. 13.
    Khouri, S., Boukhari, I., Bellatreche, L., Sardet, E., Jean, S., Baron, M.: Ontology-based structured web data warehouses for sustainable interoperability: requirement modeling, design methodology and tool. Computers in Industry 63(8), 799–812 (2012)CrossRefGoogle Scholar
  14. 14.
    Malki, J., Bouju, A., Mefteh, W.: An ontological approach for modeling and reasoning on trajectories taking into account thematic, temporal and spatial rules. Technique et Science Informatiques 31(1), 71–96 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thouraya Sakouhi
    • 1
  • Jalel Akaichi
    • 1
  • Jamal Malki
    • 2
  • Alain Bouju
    • 2
  • Rouaa Wannous
    • 2
  1. 1.Institut Supérieur de GestionTunisTunisia
  2. 2.University of La RochelleLa RochelleFrance

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