A Practical Approach to Faster Spatial Queries with Keywords?

In database search the similar data with multimedia databases requires support of near-neighbor to access high dimensional data for accessing of individual queries. The respondents of voronoi based accessing based cell to allow the high datasets. In the proposed mechanism the efficiency of the data and distributed data can access the cells, in the R-tree Patterns. In this method the data can be verified and classified in to the root node to child node for identifying the neighbor node for the similar multimedia datasets. In our new approach, we therefore pre compute the result of any nearest-neighbor search which corresponds to a computation of the voronoi cell of each data point. In a second step, we store the voronoi cells in an index structure efficient for high-dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precipitation of the solution space, it is dynamic, i.e. it supports insertions of new data points. This kind of spatial query or nearest neighbor query is hard to achieve. This is the open problem that can be addressed.