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Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines by Mu, Cun; Zhao, Jun; Yang, Guang; Yang, Binwei; Yan, Zheng is a scholarly article available to read on EtoBox.

What is Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines about?

A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and being immediately ready for production deployment. In this paper, we continue the journey to explore specifically how to empower full-text search engines with fast and exact NNS in Hamming space (i.e., the set of binary codes). By revisiting three techniques (bit operation, subs-code filtering and data preprocessing with permutation) in information retrieval literature, we develop a novel engineering solution for full-text search engines to efficiently accomplish this special but important NNS task. In the experiment, we show that our proposed approach enables full-text search engines to achieve significant speed-ups over its state-of-the-art term match approach for NNS within binary codes.

Author

Mu, Cun; Zhao, Jun; Yang, Guang; Yang, Binwei; Yan, Zheng

Published

2019

Language

EN