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 remote databases 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
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