Bulk insertion for R-trees by seeded clustering

Authors:
Taewon Lee;Bongki Moon;Sukho Lee
Affiliations:
Seoul National University, School of Computer Science and Engineering, Kwanakgu, Shillimdong, Seoul, Republic of Korea;Department of Computer Science, University of Arizona, Tucson, AZ;Seoul National University, School of Computer Science and Engineering, Kwanakgu, Shillimdong, Seoul, Republic of Korea
Venue:
Data & Knowledge Engineering
Year:
2006

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Abstract

We propose a scalable technique called Seeded Clustering that allows us to maintain R-tree indices by bulk insertion while keeping pace with high data arrival rates. Our approach uses a seed tree, which is copied from the top k levels of a target R-tree, to classify input data objects into clusters. We then build an R-tree for each of the clusters and insert the input R-trees into the target R-tree in bulk one at a time. We present detailed algorithms for the seeded clustering and bulk insertion. The experimental results show that the bulk insertion by seeded clustering outperforms the previously known methods.