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Dynamic Modeling of Trajectory Patterns using Data Mining and Reverse Engineering
Alvares, L.O., Bogorny, V., Fernandes de Macedo, J.A., Moelans, B. and Spaccapietra, S.
The constant increase of moving object data imposes
the need for modeling, processing, and mining trajectories,
in order to find and understand the patterns
behind these data. Existing works have mainly
focused on the geometric properties of trajectories,
while the semantics and the background geographic
information has rarely been addressed. We claim that
meaningful patterns can only be extracted from trajectories
if the geographic space where trajectories are
located is considered. In this paper we propose a reverse
engineering framework for mining and modeling
semantic trajectory patterns. Since trajectory patterns
are data dependent, they may not be modeled in
conceptual geographic database schemas before they
are known. Therefore, we apply data mining to extract
general trajectory patterns, and through a new
kind of relationships, we model these patterns in the
geographic database schema. A case study shows the
power of the framework for modeling semantic trajectory
patterns in the geographic space. |
Cite as: Alvares, L.O., Bogorny, V., Fernandes de Macedo, J.A., Moelans, B. and Spaccapietra, S. (2007). Dynamic Modeling of Trajectory Patterns using Data Mining and Reverse Engineering. In Proc. Tutorials, posters, panels and industrial contributions at the 26th International Conference on Conceptual Modeling - ER 2007 Auckland, New Zealand. CRPIT, 83. Grundy, J., Hartmann, S., Laender, A. H. F., Maciaszek, L. and Roddick, J. F., Eds. ACS. 149-154. |
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