Spatiotemporal Moving Pattern Discovery using Location Generalization of Moving Objects


The KIPS Transactions:PartD, Vol. 10, No. 7, pp. 1103-1114, Dec. 2003
10.3745/KIPSTD.2003.10.7.1103,   PDF Download:

Abstract

Currently, one of the most critical issues in developing the service support system for various spatio-temporal applications is the discoverying of meaningful knowledge from the large volume of moving object data. This sort of knowledge refers to the spatiotemporal moving pattern. To discovery such knowledge, various relationships between moving objects such as temporal, spatial and spatiotemporal topological relationships needs to be considered in knowledge discovery. In this paper, we proposed an efficient method, MPMine, for discoverying spatiotemporal moving patterns. The method not only has considered both temporal constraint and spatial constrain but also performs the spatial generalization using a spatial topological operation, contain(). Different from the previous temporal pattern methods, the proposed method is able to save the search space by using the location summarization and generalization of the moving object data. Therefore, Efficient discoverying of the useful moving patterns is possible.


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Cite this article
[IEEE Style]
L. J. Ug and N. G. U, "Spatiotemporal Moving Pattern Discovery using Location Generalization of Moving Objects," The KIPS Transactions:PartD, vol. 10, no. 7, pp. 1103-1114, 2003. DOI: 10.3745/KIPSTD.2003.10.7.1103.

[ACM Style]
Lee Jun Ug and Nam Gwang U. 2003. Spatiotemporal Moving Pattern Discovery using Location Generalization of Moving Objects. The KIPS Transactions:PartD, 10, 7, (2003), 1103-1114. DOI: 10.3745/KIPSTD.2003.10.7.1103.