Post-optimization and Incremental Refinement of R-trees

We develop an R-tree node restructuring algorithm that performs post-optimization of existing R-trees and improves current dynamic insertion algorithms. On realistic data and relative to state-of-the-art R-trees, our post optimization technique improves point query performance by 36% -- 43% on average (and up to a factor of 2 in some cases), while only incurring an optimization cost (measured in disk I/Os) equal to STR loading. When used to modify existing dynamic insertion algorithms, our technique results in trees that require between 25% and 45% fewer disk accesses on average (up to 70% in some cases), relative to R -trees and Hilbert R-trees, respectively, at only an additional 10% insertion cost. 1 Introduction R-trees [5] are a common indexing technique for multidimensional data and are widely used in spatial and multidimensional databases. A significant amount of research has focused on the efficient construction of good quality Rtrees [1, 2, 3, 4, 6, 7, 9, 13, 14]. This rese..