Towards an analysis of range query performance in spatial data structures
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
CIKM '93 Proceedings of the second international conference on Information and knowledge management
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Direct spatial search on pictorial databases using packed R-trees
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
A Generic Approach to Bulk Loading Multidimensional Index Structures
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The priority R-tree: A practically efficient and worst-case optimal R-tree
ACM Transactions on Algorithms (TALG)
A revised r*-tree in comparison with related index structures
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
The World in a Nutshell: Concise Range Queries
IEEE Transactions on Knowledge and Data Engineering
A class of R-tree histograms for spatial databases
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Sort-based parallel loading of R-trees
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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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.