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The increasing use of location-aware devices has led to generate a huge volume of data from satellite images and mobile sensors; these data can be classified into geographical data. And traces generated by objects moving on geographical territory, these traces are usually modeled as streams of spatiotemporal points called trajectories. Integrating trajectory sample points with geographical and contextual data before applying mining techniques can be more gainful for the application users. It contributes to produce significant knowledge about movements and provide applications with richer and more meaningful patterns. Trajectory Outliers are a sort of patterns that can be extracted from trajectories. However, the majority of algorithms proposed for discovering outliers are based on the geometric side of trajectories; our approach extends these works to produce outliers based on semantic trajectories in order to give meaning to the outliers extracted, and to understand the unusual behaviors that can be detected. To prove the efficiency of the approach proposed we show some experimental results.

A Semantic Interpretation of Unusual Behaviors Extracted from Outliers of Moving Objects Trajectories