Exerting spatial join and KNN queries on spatial database

Spatial database system as a database system that offers spatial data types in its data model and query language and supports spatial data types in its implementation, providing at least spatial indexing and spatial join methods. Spatial database applications, such as Geographical Information Systems (GIS), typically use R-tree variants to index geographical data. Spatial Joins are important operations in applications such as GIS, Cartography and CAD/CAM. Spatial Join is very useful technique for wide spread implementation of R-trees as Spatial index structures. Proposed an algorithm based on R-tree to perform the operation of spatial join for spatial objects in multi-user environment. 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 point. Quickly executing k-Nearest-Neighbor (kNN) in spatial database applications requires an informative and efficient index structure that can effectively reduce the search space. Proposed method implements extension to R-trees that uses object classifications to reduce the search space of kNN queries in multi-user environment.