2014

Conférence

In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_47

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.

@InProceedings{10.1007/978-3-319-08326-1_47,

author="Sakouhi, Thouraya

and Akaichi, Jalel

and Malki, Jamal

and Bouju, Alain

and Wannous, Rouaa",

editor="Andreasen, Troels

and Christiansen, Henning

and Cubero, Juan-Carlos

and Ra{\'{s}}, Zbigniew W.",

title="Inference on Semantic Trajectory Data Warehouse Using an Ontological Approach",

booktitle="Foundations of Intelligent Systems",

year="2014",

publisher="Springer International Publishing",

address="Cham",

pages="466--475",

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.",

isbn="978-3-319-08326-1"

}