Efficient processing of hamming-distance-based similarity-search queries over MapReduce

Abstract

Similarity search is crucial to many applications. Of particular interest are two flavors of the Hamming distance range query, namely, the Hamming select and the Hamming join (Hamming-select and Hamming-join, respectively). Hamming distance is widely used in approximate near neighbor search for high dimensional data, such as images and document collections. For example, using predefined similarity hash functions, high-dimensional data is mapped into one-dimensional binary codes that are, then linearly scanned to perform Hamming-distance comparisons. These distance comparisons on the binary codes are usually costly and, often involves excessive redundancies. This paper introduces a new index, termed the HA-Index, that speeds up distance comparisons and eliminates redundancies when performing the two flavors of Hamming distance range queries. An efficient search algorithm based on the HA-index is presented. A distributed version of the HA-index is introduced and algorithms for realizing Hamming distance-select and Hamming distance-join operations on a MapReduce platform are prototyped. Extensive experiments using real datasets demonstrates that the HA-index and the corresponding search algorithms achieve up to two orders of magnitude speedup over existing state-of-the-art approaches, while saving more than ten times in memory space.