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SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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Communications of the ACM
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Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
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IEEE Transactions on Knowledge and Data Engineering
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EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
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ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Topological relationship query processing for complex regions in Oracle Spatial
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Interactive data mining with 3D-parallel-coordinate-trees
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Geographic data have become abundantly available in the recent years due to the widespread deployment of GPS devices for example in mobile phones. At the same time, the data covered are no longer restricted to the local area of a single application, but often span the whole world. However, we do still use very rough approximations when indexing these data, which are usually stored and indexed using an equirectangular projection. When distances are measured using Euclidean distance in this projection, a non-neglibile error may occur. Databases are lacking good support for accelerated nearest neighbor queries and range queries in such datasets for the much more appropriate geodetic (great-circle) distance. In this article, we will show two approaches how a widely known spatial index structure --- the R-tree --- can be easily used for nearest neighbor queries with the geodetic distance, with no changes to the actual index structure. This allows existing database indexes immediately to be used with low distortion and highly efficient nearest neighbor queries and radius queries as well as window queries.