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The R*-tree: an efficient and robust access method for points and rectangles

Published: 01 May 1990 Publication History

Abstract

The R-tree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design the R*-tree which incorporates a combined optimization of area, margin and overlap of each enclosing rectangle in the directory. Using our standardized testbed in an exhaustive performance comparison, it turned out that the R*-tree clearly outperforms the existing R-tree variants. Guttman's linear and quadratic R-tree and Greene's variant of the R-tree. This superiority of the R*-tree holds for different types of queries and operations, such as map overlay, for both rectangles and multidimensional points in all experiments. From a practical point of view the R*-tree is very attractive because of the following two reasons 1 it efficiently supports point and spatial data at the same time and 2 its implementation cost is only slightly higher than that of other R-trees.

References

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D Greene 'An implementation and Performance Analysis of Spatial Data Access Methods', Proc 5th I n t Conf on Data Engineering, 606-615, 1989
[2]
A Guttman 'R-trees a dynamic index structure for spatial searching', Proc ACM SIGMOD Int Conf on Management of Data, 47-57, 1984
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H P Krlegel, M Schlwletz, R Schneider, B Seeger 'Performance comparison of point and spatlal access methods', Proc Syrup on the Design and Implementation of Large Spatial Databases', Santa Barbara, 1989, Lecture Notes m Computer Science
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B Seeger, H P Krlegel 'Design and implementation of spatial access methods', Proc 14th Int Conf on Very Large Databases, 360-371, 1988
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B Seeger, HP Krlegel 'The design and implementation of the buddy tree', Computer Science Techmcal Report 3/90, Umverslty of Bremen, submitted for pubhcatlon, 1990

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 1990
Published in SIGMOD Volume 19, Issue 2

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  • (2024)FAST-LIO2: Fast Direct LiDAR-Inertial OdometrySSRN Electronic Journal10.2139/ssrn.4675561Online publication date: 2024
  • (2024)The Art of Sparsity: Mastering High-Dimensional Tensor Storage2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00094(439-446)Online publication date: 27-May-2024
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