Abstract
Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The computational time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The analyzed data are considered as the first knowledge base. The secondary filter then computes the inference over the filtered trajectory data and yields to the final knowledge base, that the user can query.
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References
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Alvares, L.O., Bogorny, V., Kuijpers, B., Macedo, A.F., Moelans, B., Vaisman, A.: A model for enriching trajectories with semantic geographical information. In: Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, pp. 22:1–22:8. ACM (2007)
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 Workshops 2008. LNCS, vol. 5232, pp. 344–353. Springer, Heidelberg (2008)
Boulmakoul, A., Karim, L., Lbath, A.: Moving object trajectories meta-model and spatio-temporal queries. International Journal of Database Management Systems (IJDMS), 35–54 (2012)
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., 94–110 (2001)
Güting, R., Schneider, M.: Moving Objects Databases. Morgan Kaufmann (2005)
Halevy, A.Y.: Structures, semantics and statistics. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB 2004, vol. 30, pp. 4–6. VLDB Endowment (2004)
Hillairet, G., Bertrand, F., Lafaye, J.Y.: MDE for publishing data on the semantic web. In: International Workshop on Transformation and Weaving Ontologies and Model Driven Engineering (TWOMDE) at MODELS 2008 (2008)
ISO/TC_211. Geographic information – temporal schema, ISO 19108 (2002)
Jerry, R.H., Feng, P.: An ontology of time for the semantic web. ACM Transactions on Asian Language Information Processing, 66–85 (2004)
Malki, J., Bouju, A., Mefteh, W.: An ontological approach modeling and reasoning on trajectories. taking into account thematic, temporal and spatial rules. In: Technique et Science Informatiques, TSI, vol. 31, pp. 71–96 (2012)
Malki, J., Wannous, R., Bouju, A., Vincent, C.: Temporal reasoning in trajectories using an ontological modelling approach. Control and Cybernetics, 1–16 (2012)
Matthew, P.: A framework to support spatial, temporal and thematic analytics over semantic web data. PhD thesis, Wright State University (2008)
Oracle. Oracle Database Semantic Technologies Developer’s guide 11g release 2 (2012)
Predoiu, L., Stuckenschmidt, H.: Probabilistic Extensions of Semantic Web Languages - A Survey
Spaccapietra, S., Parent, C., Damiani, M., Demacedo, J., Porto, F., Vangenot, C.: A conceptual view on trajectories. In: Data and Knowledge Engineering, pp. 126–146 (2008)
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)
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)
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)
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)
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Wannous, R., Malki, J., Bouju, A., Vincent, C. (2014). Analysis of Trajectory Ontology Inference Complexity over Domain and Temporal Rules. In: Ait Ameur, Y., Bellatreche, L., Papadopoulos, G.A. (eds) Model and Data Engineering. MEDI 2014. Lecture Notes in Computer Science, vol 8748. Springer, Cham. https://doi.org/10.1007/978-3-319-11587-0_18
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DOI: https://doi.org/10.1007/978-3-319-11587-0_18
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