Dependency rule modeling for multiple aspects trajectories

Trajectories of moving objects are usually modeled as sequences of space-time points or, in case of semantic trajectories, as labelled stops and moves. Data analytics methods on these kinds of trajectories tend to discover geometrical and temporal patterns, or simple semantic patterns based on the labels of stops and moves. A recent extension of semantic trajectories is called multiple aspects trajectory, i.e., a trajectory associated to different semantic dimensions called aspects. This kind of trajectory increases in a large scale the number of discovered patterns. This paper introduces the concept of dependency rule to represent patterns discovered from the analysis of trajectories with multiple aspects. They include patterns related to a trajectory, trajectory points, or the moving object. These rules are conceptually represented as an extension of a conceptual model for multiple aspects trajectories. A case study shows that our proposal is relevant as it represents the discovered rules with a concise but expressive conceptual model. Additionally, a performance evaluation shows the feasibility of our conceptual model designed over relational-based database management technologies.