Lars Arge
Abstract
We present the priority R-tree, or PR-tree, which is the first R-tree variant that always answers a window query using O((N/B)1−1/d+T/B) I/Os, where N is the number of d-dimensional (hyper-) rectangles stored in the R-tree, B is the disk block size, and T is the output size. This is provably asymptotically optimal and significantly better than other R-tree variants, where a query may visit all N/B leaves in the tree even when T = 0. We also present an extensive experimental study of the practical performance of the PR-tree using both real-life and synthetic data. This study shows that the PR-tree performs similarly to the best-known R-tree variants on real-life and relatively nicely distributed data, but outperforms them significantly on more extreme data.
- Agarwal, P. K., de Berg, M., Gudmundsson, J., Hammar, M., and Haverkort, H. J. 2002. Box-Trees and R-trees with near-optimal query time. Discr. Comput. Geom. 28, 3, 291--312.Google Scholar
Digital Library
- Agarwal, P. K., Arge, L., Procopiuc, O., and Vitter, J. S. 2001. A framework for index bulk loading and dynamization. In Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP), 115--127. Google ScholarDigital Library
- Arge, L., and Vahrenhold, J. 2004. I/O-Efficient dynamic planar point location. Comput. Geom. Theory Appl. 29, 2, 147--162. Google ScholarDigital Library
- Arge, L., Hinrichs, K. H., Vahrenhold, J., and Vitter, J. S. 2002a. Efficient bulk operations on dynamic R-trees. Algorithmica 33, 1, 104--128.Google ScholarDigital Library
- Arge, L., Procopiuc, O., and Vitter, J. S. 2002b. Implementing I/O-efficient data structures using TPIE. In Proceedings of the European Symposium on Algorithms (ESA), 88--100. Google ScholarDigital Library
- Bayer, R., and McCreight, E. 1972. Organization and maintenance of large ordered indexes. Acta Inf. 1, 173--189.Google ScholarDigital Library
- Beckmann, N., Kriegel, H.-P., Schneider, R., and Seeger, B. 1990. The R*-tree: An efficient and robust access method for points and rectangles. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 322--331. Google ScholarDigital Library
- Berchtold, S., Böhm, C., and Kriegel, H.-P. 1998. Improving the query performance of high-dimensional index structures by bulk load operations. In Proceedings of the Conference on Extending Database Technology. Lecture Notes in Computer Science, vol. 1377. Springer, 216--230. Google ScholarDigital Library
- Bozanis, P., Nanopoulos, A., and Manolopoulos, Y. 2003. LR-Tree: A logarithmic decomposable spatial index method. The Comput. J. 46, 3, 319--331.Google ScholarCross Ref
- Chazelle, B. 2001. The Discrepancy Method: Randomness and Complexity. Cambridge University Press, New York. Google ScholarDigital Library
- Comer, D. 1979. The ubiquitous B-tree. ACM Comput. Surv. 11, 2, 121--137. Google ScholarDigital Library
- DeWitt, D. J., Kabra, N., Luo, J., Patel, J. M., and Yu, J.-B. 1994. Client-Server Paradise. In Proceedings of the International Conference on Very Large Databases, 558--569. Google ScholarDigital Library
- Gaede, V., and Günther, O. 1998. Multidimensional access methods. ACM Comput. Surv. 30, 2, 170--231. Google ScholarDigital Library
- García, Y. J., López, M. A., and Leutenegger, S. T. 1998. A greedy algorithm for bulk loading R-trees. In Proceedings of the 6th ACM Symposium on Advances in Geographic Information Systems (GIS), 163--164. Google ScholarDigital Library
- Guttman, A. 1984. R-trees: A dynamic index structure for spatial searching. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 47--57. Google ScholarDigital Library
- Haverkort, H. J., de Berg, M., and Gudmundsson, J. 2002. Box-Trees for collision checking in industrial installations. In Proceedings of the ACM Symposium on Computational Geometry, 53--62. Google ScholarDigital Library
- Kamel, I., and Faloutsos, C. 1994. Hilbert R-tree: An improved R-tree using fractals. In Proceedings of the International Conference on Very Large Databases, 500--509. Google ScholarDigital Library
- Kamel, I., and Faloutsos, C. 1993. On packing R-trees. In Proceedings of the International Conference on Information and Knowledge Management (CIKM), 490--499. Google ScholarDigital Library
- Kanth, K. V. R., and Singh, A. K. 1999. Optimal dynamic range searching in non-replicating index structures. In Proceedings of the International Conference on Database Theory. Lecture Notes in Computer Science, vol. 154. Springer, 257--276. Google ScholarDigital Library
- Leutenegger, S. T., López, M. A., and Edgington, J. 1996. STR: A simple and efficient algorithm for R-tree packing. In Proceedings of the IEEE International Conference on Data Engineering, 497--506. Google ScholarDigital Library
- Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A. N., and Theodoridis, Y. 2005. R-Trees: Theory and Applications. Springer. Google ScholarDigital Library
- Procopiuc, O., Agarwal, P. K., Arge, L., and Vitter, J. S. 2003. BKD-Tree: A dynamic scalable KD-Tree. In Proceedings of the International Symposium on Spatial and Temporal Databases.Google Scholar
- Robinson, J. 1981. The K-D-B tree: A search structure for large multidimensional dynamic indexes. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 10--18. Google ScholarDigital Library
- Roussopoulos, N., and Leifker, D. 1985. Direct spatial search on pictorial databases using packed R-trees. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 17--31. Google ScholarDigital Library
- Sellis, T., Roussopoulos, N., and Faloutsos, C. 1987. The R+-Tree: A dynamic index for multi-dimensional objects. In Proceedings of the International Conference on Very Large Databases, 507--518. Google ScholarDigital Library
- TIGER. 1998. TIGER/Line#8482; files, 1997 technical documentation. Washington, DC. http://www.census.gov/geo/tiger/TIGER97D.pdf.Google Scholar
Index Terms
- The priority R-tree: A practically efficient and worst-case optimal R-tree
Recommendations
The Priority R-tree: a practically efficient and worst-case optimal R-tree
SIGMOD '04: Proceedings of the 2004 ACM SIGMOD international conference on Management of dataWe present the Priority R-tree, or PR-tree, which is the first R-tree variant that always answers a window query using O((N/B)1 1/d + T/B) I/Os, where N is the number of d-dimensional (hyper-) rectangles stored in the R-tree, B is the disk block size, ...
Join-queries between two spatial datasets indexed by a single R*-tree
SOFSEM'11: Proceedings of the 37th international conference on Current trends in theory and practice of computer scienceA spatial join, a common query in Spatial Databases and Geographical Information Systems (GIS), consists in testing every possible pair of data elements belonging to two spatial datasets against a spatial predicate. This predicate might be "intersects", ...
Temporally enhanced network-constrained (TENC) R-tree
MobiGIS '16: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information SystemsThis paper describes a new Network-constrained Moving objects indexing structure, which extends the state-of-the-art for this kind of data. The indexing structure we propose is called Temporally Enhanced Network-Constrained R-tree (TENC R-tree), which ...
Comments