Abstract

In this paper, we propose a new method for index-ing large amounts of point and spatial data in high-dimensional space. An analysis shows that index structures such as the R*-tree are not adequate for indexing high-dimensional data sets. The major problem of R-tree-based index structures is the overlap of the bounding boxes in the directory, which increases with growing dimension. To avoid this problem, we introduce a new organization of the directory which uses a split algorithm minimiz-ing overlap and additionally utilizes the concept of supernodes. The basic idea of overlap-minimizing split and supernodes is to keep the directory as hi-erarchical as possible, and at the same time to avoid splits in the directory that would result in high over-lap. Our experiments show that for high-dimen-sional data, the X-tree outperforms the well-known R*-tree and the TV-tree by up to two orders of magnitude. 1.