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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/7876

Title: Dynamic Modeling of Trajectory Patterns using Data Mining and Reverse Engineering
Authors: ALVARES, Luis Otavio
Issue Date: 2007
Publisher: ACS
Citation: Grundy, John & Hartmann, Sven & Laender, Alberto H. F & Maciaszek, Leszek & Roddick, John F. (Ed.) In Proc. Tutorials, posters, panels and industrial contributions at the 26th International Conference on Conceptual Modeling - ER 2007: vol. 83. p. 149-154.
Series/Report: CRPIT
Abstract: 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.
URI: http://hdl.handle.net/1942/7876
Link to publication: http://www.crpit.com/abstracts/CRPITV83Alvares.html
Category: C2
Type: Proceedings Paper
Appears in Collections: Research publications

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