Key points are not available for this paper at this time.
Off-line loading of R-trees is useful to improve node utilization and query performance. We present an algorithm for bulk loading R-trees which differs from previous ones in two aspects: (a) it partitions input data into subtrees in a top-down fashion (based on the fact that splits close to the root are likely to have a greater impact on performance), (b) at each tree level, it considers all cuts orthogonal to the coordinate axes that result in packed trees and greedily picks those optimizing an arbitrary cost function. Extensive experimentation with both real and synthetic data indicate that for region data our algorithm requires up to three times fewer disk accesses than other algorithms. It is the method of choice for data with skew in locations, areas, or aspect ratios. Such data is common in practice.
R et al. (Sun,) studied this question.