Efficient Approximate Nearest Neighbor Search in Multi-dimensional Databases
Summary: tau-monotonic graph (tau-MG) for ANN in multi-dimensional databases; exploits a tau-monotonic property to guarantee exact NN when dist(q,NN) ≤ tau and to beat existing PG-based search times. Approximate tau-monotonic neighborhood graph (tau-MNG) lowers construction cost by restricting monotonicity to neighborhoods and adds a distance-reduction optimization; experiments on real datasets show gains over prior PG-based ANN methods. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yun Peng (Guangdong University of Technology; Hong Kong Baptist University)
- 2. Byron Choi (Hong Kong Baptist University)
- 3. Tsz Nam Chan (Hong Kong Baptist University)
- 4. Jianye Yang (Guangdong University of Technology)
- 5. Jianliang Xu (Hong Kong Baptist University)
BibTeX Citation
@inproceedings{peng_sigmod23,
title = {{Efficient Approximate Nearest Neighbor Search in Multi-dimensional Databases}},
author = {Peng, Yun and Choi, Byron and Chan, Tsz Nam and Yang, Jianye and Xu, Jianliang},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588908},
url = {https://dl.acm.org/doi/10.1145/3588908},
year = {2023}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 93 | Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph | 2019 | VLDB | 0.00034701237 |
| 398 | A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search | 2021 | VLDB | 0.00019194947 |
| 926 | Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination | 2020 | SIGMOD | 0.00013181732 |
| 1,572 | LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index | 2016 | SIGMOD | 0.00010329197 |
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