Sort-based query-adaptive loading of R-trees

Abstract

Bulk-loading of R-trees has been an important problem in academia and industry for more than twenty years. Current algorithms create R-trees without any information about the expected query profile. However, query profiles are extremely useful for the design of efficient indexes. In this paper, we address this deficiency and present query-adaptive algorithms for building R-trees optimally designed for a given query profile. Since optimal R-tree loading is NP-hard (even without tuning the structure to a query profile), we provide efficient, easy to implement heuristics. Our sort-based algorithms for query-adaptive loading consist of two steps: First, sorting orders are identified resulting in better R-trees than those obtained from standard space-filling curves. Second, for a given sorting order, we propose a dynamic programming algorithm for generating R-trees in linear runtime. Our experimental results confirm that our algorithms generally create significantly better R-trees than the ones obtained from standard sort-based loading algorithms, even when the query profile is unknown.