Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Spatial joins using seeded trees
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Partition based spatial-merge join
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 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
IEEE Transactions on Knowledge and Data Engineering
Efficient Computation of Spatial Joins
Proceedings of the Ninth International Conference on Data Engineering
A Cost Model for Estimating the Performance of Spatial Joins Using R-trees
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
A New Algorithm for Processing Joins Using the Multilevel Grid File
Proceedings of the 4th International Conference on Database Systems for Advanced Applications (DASFAA)
Algorithms for processing K-closest-pair queries in spatial databases
Data & Knowledge Engineering
Cost models for distance joins queries using R-trees
Data & Knowledge Engineering
ACM Transactions on Database Systems (TODS)
A performance comparison of distance-based query algorithms using R-trees in spatial databases
Information Sciences: an International Journal
Efficient k-Closest-Pair Range-Queries in Spatial Databases
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Closest Pair Query on Spatial Data Sets without Index
SCCC '10 Proceedings of the 2010 XXIX International Conference of the Chilean Computer Science Society
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We provide in this article a branch-and-bound algorithm that solves the problem of finding the k closest pairs of points (p,q), p驴驴驴P,q驴驴驴Q, considering two sets of points in the euclidean plane P,Q stored in external memory assuming that only one of the sets has a spatial index. This problem arises naturally in many scenarios, for instance when the set without an index is the answer to a spatial query. The main idea of our algorithm is to partition the space occupied by the set without an index into several cells or subspaces and to make use of the properties of a set of metrics defined on two Minimum Bounding Rectangles (MBRs). We evaluated our algorithm for different values of k by means of a series of experiments that considered both synthetical and real world datasets. We compared the performance of our algorithm with that of techniques that either assume that both datasets have a spatial index or that none has an index. The results show that our algorithm needs only between a 0.3 and a 35 % of the disk accesses required by such techniques. Our algorithm also shows a good scalability, both in terms of k and of the size of the data set.