Improving Spatial neighbor Index Performance Based on Space-Filling Curves

Spatial data consists of objects in space made up of points, lines, regions and data of higher dimensions. Access method is required to support efficient manipulation of the multi-dimensional spatial objects in the secondary storage. The goal of the Space-Filling Curve (SFC) is to preserve spatial proximity; they can handle Nearest Neighbor Queries (NNQ) which involves determining the point in a dataset that is nearest to a given point. In this paper a new algorithm for finding the horizontal and vertical neighbor for RBG curve is proposed. The four direction neighbors are directly founded from the query block without depending on transformation method between Piano and RBG index. The result shows that the new algorithm has better performance than the traditional RBG neighbor index finding by reducing the time needed for transformation between RBG and Piano index.