Search K Nearest Neighbors on Air
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
On efficiently processing nearest neighbor queries in a loosely coupled set of data sources
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Supporting range queries on web data using k-nearest neighbor search
W2GIS'07 Proceedings of the 7th international conference on Web and wireless geographical information systems
Integrated k-NN query processing based on geospatial data services
GCC'05 Proceedings of the 4th international conference on Grid and Cooperative Computing
Approximate search algorithm for aggregate k-nearest neighbour queries on remote spatial databases
International Journal of Knowledge and Web Intelligence
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K -Nearest Neighbor k -NN) queries are used in GIS and CAD/CAM applications to find the k spatial objects closest to some given query points. Most previous k -NN research has assumed that the spatial databases to be queried are local, and that the query processing algorithms have direct access to their spatial indices; e.g., R-trees. Clearly, this assumption does not hold when k -NN queries are directed at remote spatial databases that operate autonomously. While it is possible to replicate some or all the spatial objects from the remotedatabases in a local database and build a separate index structure for them, such an alternative is infeasible when the database is huge, or there are large number of spatial databases to be queried. In this paper, we propose a k -NN query processing algorithm that uses one or more window queries to retrieve the nearest neighbors of a given query point. We also propose two different methods to estimate the ranges to be used by the window queries. Each range estimation method requires different statistical knowledge about the spatial databases. Our experiments on the TIGER data allow us to study the behavior of the proposed algorithm using different range estimation methods. Apart from not requiring direct access to the spatial indices, the window queries used in the proposed algorithm can be easilysupported by non-spatial database systems containing spatial objects.