BibTeX
@MISC{Vasaitis_mergingr-trees,
author = {Vasilis Vasaitis and Alexandros Nanopoulos and Panayiotis Bozanis},
title = {Merging R-trees},
year = {}
}
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Abstract
R-trees, since their introduction in 1984, have been proven to be one of the most well-behaved practical data structures for accommodating dynamic massive sets of geometric objects and conducting a diverse set of queries on such data-sets in real-world applications. In this paper we introduce a new technique for merging two R-trees into a new one of very good quality. Our method avoids both the employment of bulk insertions and the solution of bulkloading, from scratch, the new tree using the data of the original trees. Additionally, unlike previous approaches, it does not make any assumptions about data-set distributions. Experimental results provide evidence on the runtime efficiency of our method and illustrate the good query performance of the produced indices. 1.
Keyphrases
geometric object new technique produced index bulk insertion method avoids well-behaved practical data structure dynamic massive set diverse set good quality original tree previous approach data-set distribution good query performance experimental result real-world application new tree runtime efficiency
