An efficient data management method for spatial objects using MD-tree-experimental evaluation and comparisons

An efficient spatial data management method for non-zero size spatial objects, called R(Region)MD-tree, is proposed. In this method, a graphic object in an N-dimensional space is represented as a point in a 2×N dimensional space by the centroid and the extension in each axial direction. The point data are managed by the MD-tree after applying the coordinate transformation. The point location problem or range searching in the original N-dimensional space are carried out by the hyper-rectangular range searching to the point data in the 2×N dimensional space. In this representation and data management, data objects are sorted and classified according to not only the location but also the size of the data. By the simulation experiments, the performances and efficiencies of the RMD-tree are compared with the conventional MD-tree and the R-tree. As the data objects become larger, the RMD-tree is superior in the searching speed by 2~4 times to the conventional ones, and the performances are almost comparable even if the objects are points or small graphic data