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[SVM+12] | Diego V. Simões De Sousa,
Henrique Viana,
Nicolas Markey, and
Jose Antônio F. de Macêdo.
Querying Trajectories through Model Checking based
on Timed Automata.
In SBBD'12,
pages 33-40.
Sociedade Brasileira de Computação, October 2012.
@inproceedings{sbbd2012-SVMM, author = {Sim{\~o}es{ }De{~}Sousa, Diego V. and Viana, Henrique and Markey, Nicolas and de Mac{\^e}do, Jose Ant{\^o}nio F.}, title = {Querying Trajectories through Model Checking based on Timed Automata}, editor = {Casanova, Marco A.}, booktitle = {{P}roceedings of the 27th {B}razilian {S}ymposium on {D}atabases ({SBBD}'12)}, acronym = {{SBBD}'12}, publisher = {Sociedade Brasileira de Computa{\c c}{\~a}o}, pages = {33-40}, year = {2012}, month = oct, abstract = {The popularization of geographical position devices (e.g.~GPS) creates new opportunities for analyzing behavior of moving objects. However, such analysis are hindered by a lack of semantic information associated to the basic information provided by~GPS. Previous works propose semantic enrichment of trajectories. Through the semantic enrichment, we~could check which trajectories have a given moving sequence in an application. Often, this~sequence is expressed according to the semantic application, using the approach of semantic trajectories proposed in the literature. This~trajectory can be represented as a sequence of predicates that holds in some time interval. However, the solutions for querying moving sequence proposed by previous works have a high computational cost. In~this paper, we~propose an expressive query language to semantic trajectories that allows temporal constraints. To~evaluate a query we will use model checking based on timed automata, that can be performed in polynomial time. As~this model checking algorithm is not implemented yet, we propose to use UPPAAL tool, that can be more expensive theoretically, but we expected that will be ecient for our approach. In addition, we will present a query example that demonstrates the expressive power of our language. Although in this paper we will focus on semantic trajectories data, our approach is general enough for being applied to other purposes.}, } |
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