Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
A qualitative comparison study of data structures for large line segment databases
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
ACM Computing Surveys (CSUR)
Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Benchmarking Spatial Join Operations with Spatial Output
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Performance Comparison of the {\rm R}^{\ast}-Tree and the Quadtree for kNN and Distance Join Queries
IEEE Transactions on Knowledge and Data Engineering
Nearest Neighbor Algorithms Using xBR-Trees
PCI '11 Proceedings of the 2011 15th Panhellenic Conference on Informatics
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Processing of spatial queries has been studied extensively in the literature. In most cases, it is accomplished by indexing spatial data by an access method. For queries involving a single dataset, like the Point Location Query, the Window (Distance Range) Query, the (Constrained) K Nearest Neighbor Query, the R*-tree (a data-driven structure) is a very popular choice of such a method. In this paper, we compare the performance of the R*-tree for processing single dataset spatial queries to the performance of a disk based structure that belongs to the Quadtree family, the xBR-tree (a space-driven structure). We demonstrate performance results (I/O efficiency and execution time) of extensive experimentation that was based on real datasets, using these two index structures. The winner depends on several parameters and the results show that the xBR-tree is a promising alternative for these spatial operations.