This refinement reduces half of the time computation and then allows considering larger data sets. Finally, the results show the positive impact of the last refinement on reducing the complexity of the inference mechanism. Then, we evaluate our contribution over real trajectory data. In this paper, we define a refinement specifically for the application domain. In order to reduce the inference complexity, we proposed optimizations, such as domain constraints and temporal neighbor refinements. This complexity has two important factors: time computations and space storage. Experiments over our model using domain and temporal rules address an inference computation complexity. To annotate data with these rules, we need an inference mechanism over trajectory ontology. In our approach, we suggest expressing this knowledge in the form of rules. To accomplish reasoning over trajectories, the ontology must consider mobile object, domain and other knowledge. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data.
Huge trajectory data are available today. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Capture devices rise large scale trajectory data from moving objects.
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