A Class of Region-preserving Space Transformations for Indexing High-dimensional Data

Abstract

This study introduces a class of region preserving space transformation (RPST) schemes for accessing high-dimensional data. The access methods in this class differ with respect to their space-partitioning strategies. The study develops two new static partitioning schemes that can split each dimension of the space within linear space complexity. They also support an effective mechanism for handling skewed data in heavily sparse spaces. The techniques are experimentally compared to the Pyramid Technique, which is another example of static partitioning designed for high-dimensional data. On real high-dimensional data, the proposed RPST schemes outperform the Pyramid Technique by a significant margin.