Bulk Insertion for R-Tree by Seeded Clustering

Abstract

In many scientific and commercial applications such as Earth Observation System (EOSDIS) and mobile phone services tracking a large number of clients, it is a daunting task to archive and index ever increasing volume of complex data that are continuously added to databases. To efficiently manage multidimensional data in scientific and data warehousing environments, R-tree based index structures have been widely used. In this paper, we propose a scalable technique called Seeded Clustering that allows us to maintain R-tree indexes 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 as well as the results from our extensive experimental study. The experimental results show that the bulk insertion by seeded clustering outperforms the previously known methods in terms of insertion cost and the quality of target R-trees measured by their query performance.

This work was sponsored in part by the BK 21 Project from the Government of Korea. It was also sponsored in part by National Science Foundation CAREER Award (IIS-9876037), Grant No. IIS-0100436, and Research Infrastructure program EIA-0080123. The authors assume all responsibility for the contents of the paper.