Computational geometry: an introduction
Computational geometry: an introduction
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
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Advanced database indexing
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Join-Index-Based Spatial-Join Processing: A Clustering Approach
IEEE Transactions on Knowledge and Data Engineering
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
Algorithms for processing K-closest-pair queries in spatial databases
Data & Knowledge Engineering
ACM Transactions on Database Systems (TODS)
Performance Comparison of the {\rm R}^{\ast}-Tree and the Quadtree for kNN and Distance Join Queries
IEEE Transactions on Knowledge and Data Engineering
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A 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", "contains", "is enclosed by", "distance", "northwest", "adjacent", "meets", etc. The large size of datasets that appears in industrial and commercial modern applications (e.g. GIS applications, where multiple instances of the datasets are kept) raises the cost of join processing and the importance of the choice of the data indexing method and the query processing technique. The family of R-trees is considered a good choice (especially the R*-tree) for indexing a spatial dataset. When joining two datasets, a common assumption is that each dataset is indexed by a different R*-tree and the join is processed by a synchronous traversal of the two trees. In this paper, we assume that both datasets are indexed by a single R*-tree, so that spatial locality between different datasets is embedded in data indexing, facilitating the evaluation of join queries between the two datasets. We experimentally compare the I/O and Response Time performance of join queries, using this single tree indexing approach against the usual approach of indexing each dataset by a different tree.