The SH-Tree: A Novel and Flexible Super Hybrid Index Structure for Similarity Search on Multidimensional Data

Approaches to indexing and searching feature vectors are an indispensable factor to support similarity search effectively and efficiently. Such feature vectors extracted from real world objects are usually presented in the form of multidimensional data. As a result, many multidimensional data index techniques have been widely introduced to the research community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Although there are a variety of “mixed” index techniques, which try to inherit positive aspects from more than one index technique, the number of techniques that are derived from these two main classes is just a few. In this paper, we introduce such a “mixed” index, the SH-tree: a novel and flexible super hybrid index structure for multidimensional data. Theoretical analyses indicate that the SH-tree is a good combination of the two index technique families with respect to both the presentation and search algorithms. It overcomes shortcomings and makes use of their positive aspects to facilitate efficient similarity searches in multidimensional data spaces. Empirical experiment results with both uniformly distributed and real data sets will confirm our theoretical analyses.