Exploiting Structural Properties for Efficient Constraint-Aware HNSW Hyperparameter Tuning
Summary: CHAT exploits HNSW’s monotonic, unimodal, and separable parameter relationships to prune infeasible configurations and tune efficiently under quality and resource constraints. Across engines, it achieves up to 45% higher throughput or 11% higher recall, converging up to 44× faster than baselines. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Geon Choi (Seoul National University)
- 2. Hoeun Lee (Seoul National University)
- 3. Jaeyoung Do (Seoul National University)
BibTeX Citation
@inproceedings{choi_sigmod27,
title = {{Exploiting Structural Properties for Efficient Constraint-Aware HNSW Hyperparameter Tuning}},
author = {Choi, Geon and Lee, Hoeun and Do, Jaeyoung},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837111},
url = {https://dl.acm.org/doi/10.1145/3837111},
year = {2027}
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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 |
|---|---|---|---|---|
| 74 | Automatic Database Management System Tuning Through Large-scale Machine Learning | 2017 | SIGMOD | 0.00037164008 |
| 181 | Milvus: A Purpose-Built Vector Data Management System | 2021 | SIGMOD | 0.00026425782 |
| 286 | Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search | 2016 | VLDB | 0.00022090962 |
| 335 | A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search | 2021 | VLDB | 0.0002067515 |
| 411 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.0001874043 |
| 1,280 | Manu: A Cloud Native Vector Database Management System | 2022 | VLDB | 0.00011199537 |
| 4,157 | Steiner-Hardness: A Query Hardness Measure for Graph-Based ANN Indexes | 2024 | VLDB | 6.7596305e-05 |
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