A practical divide-and-conquer algorithm for the rectangle intersection problem
Information Sciences: an International Journal
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Multi-step processing of spatial joins
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
LEDA: a platform for combinatorial and geometric computing
Communications of the ACM
SIGMOD '95 Proceedings of the 1995 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
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
A Raster Approximation For Processing of Spatial Joins
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Implementation of the ROSE Algebra: Efficient Algorithms for Realm-Based Spatial Data Types
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
A General and Efficient Implementation of Geometric Operators and Predicates
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Improving Spatial Intersect Joins Using Symbolic Intersect Detection
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Optimal Algorithms for the Intersection and the Minimum Distance Problems Between Planar Polygons
IEEE Transactions on Computers
Hardware acceleration for spatial selections and joins
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Fast computation of spatial selections and joins using graphics hardware
Information Systems
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A class of commonly asked queries in a spatial database is known as buffer queries. An example of such a query is to "find house-power line pairs that are within 50 meters of each other." A buffer query involves two spatial data sets and a distance d. The answer to this query are pairs of objects from the two input sets that are within distance d of each other. This paper addresses the problem of how to evaluate this class of queries effigciently. Geometric objects are used to denote the shape and location of spatial objects. Two objects are within distance d precisely when their minimum distance (minDist) is. A fundamental problem with buffer query evaluation is to find an effective algorithm for solving the minDist problem. Such an algorithm is found and its desirability is demonstrated. Finding a fast minDist algorithm is the first step to evaluate a buffer query efficiently. It is observed that many, and even most, candidates can be determined to be in the answer without resorting to the relatively expensive minDist operation. A candidate is first evaluated with the least expensive technique - called 0-object filtering. If it fails, a more costly operation, called 1-object filtering, is applied. Finally, if both filterings fail, the most expensive minDist algorithm is invoked. To show the effectiveness of these techniques, they are incorporated into the tree join algorithm and tested with real-life as well as synthetic data sets. Extensive experiments show that the proposed algorithm outperforms existing techniques by a wide margin in both the execution time as well as IO accesses. More importantly, the performance gain improves drastically with the increase of distance values.