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Computational Intelligence and Neuroscience
Volume 2016, Article ID 4075257, 12 pages
http://dx.doi.org/10.1155/2016/4075257
Research Article

A Novel Accuracy and Similarity Search Structure Based on Parallel Bloom Filters

1Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650051, China
2Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650051, China
3Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650051, China

Received 21 April 2016; Revised 25 September 2016; Accepted 26 October 2016

Academic Editor: Hong Man

Copyright © 2016 Chunyan Shuai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In high-dimensional spaces, accuracy and similarity search by low computing and storage costs are always difficult research topics, and there is a balance between efficiency and accuracy. In this paper, we propose a new structure Similar-PBF-PHT to represent items of a set with high dimensions and retrieve accurate and similar items. The Similar-PBF-PHT contains three parts: parallel bloom filters (PBFs), parallel hash tables (PHTs), and a bitmatrix. Experiments show that the Similar-PBF-PHT is effective in membership query and K-nearest neighbors (K-NN) search. With accurate querying, the Similar-PBF-PHT owns low hit false positive probability (FPP) and acceptable memory costs. With K-NN querying, the average overall ratio and rank-i ratio of the Hamming distance are accurate and ratios of the Euclidean distance are acceptable. It takes CPU time not I/O times to retrieve accurate and similar items and can deal with different data formats not only numerical values.