Policies of Trajectory Clustering in Index based on R-trees for Moving Objects 


Vol. 12,  No. 4, pp. 507-520, Aug.  2005
10.3745/KIPSTD.2005.12.4.507


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  Abstract

The R-trees are usually used for an index of trajectories in moving-objects databases. However, they need to access a number of nodes to trace same trajectories because of considering only a spatial proximity. Overlaps and dead spaces should be minimized to enhance the performance of range queries in moving-objects indexes. Trajectories of moving-objects should be preserved to enhance the performance of the trajectory queries. In this paper, we propose the TP3DR-tree(Trajectory Preserved 3DR-tree) using clusters of trajectories for range and trajectory queries. The TP3DR-tree uses two split policies : one is a spatial splitting that splits the same trajectory by clustering and the other is a time splitting that increases space utilization. In addition, we use connecting information in non-leaf nodes to enhance the performance of combined-queries. Our experiments show that the new index outperforms the others in processing queries on various datasets.

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[IEEE Style]

C. H. Ban, J. G. Kim, B. G. Jun, B. H. Hong, "Policies of Trajectory Clustering in Index based on R-trees for Moving Objects," The KIPS Transactions:PartD, vol. 12, no. 4, pp. 507-520, 2005. DOI: 10.3745/KIPSTD.2005.12.4.507.

[ACM Style]

Chae Hoon Ban, Jin Gon Kim, Bong Gi Jun, and Bong Hee Hong. 2005. Policies of Trajectory Clustering in Index based on R-trees for Moving Objects. The KIPS Transactions:PartD, 12, 4, (2005), 507-520. DOI: 10.3745/KIPSTD.2005.12.4.507.

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