Automatic Database Management System Tuning Through Large-scale Machine Learning
Summary: Automated DBMS tuning via large-scale ML (OtterTune) to select impactful knobs, map unseen workloads to known ones, and recommend settings. Evaluated on three DBMSs; achieves configurations as good as or better than existing tools or human experts. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dana Van Aken (Carnegie Mellon University)
- 2. Andrew Pavlo (Carnegie Mellon University)
- 3. Geoffrey J. Gordon (Carnegie Mellon University)
- 4. Bohan Zhang (Peking University)
BibTeX Citation
@inproceedings{aken_sigmod17,
title = {{Automatic Database Management System Tuning Through Large-scale Machine Learning}},
author = {Van Aken, Dana and Pavlo, Andrew and Gordon, Geoffrey J. and Zhang, Bohan},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3064029},
url = {https://dl.acm.org/doi/10.1145/3035918.3064029},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 155 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,498 | Proactive Resume and Pause of Resources for Microsoft Azure SQL Database Serverless | 2024 | SIGMOD | 4.9793485e-05 |
| 11,740 | Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems | 2023 | VLDB | 4.9793485e-05 |
| 11,788 | Demonstrating Waffle: A Self-driving Grid Index | 2023 | VLDB | 4.9793485e-05 |
| 11,808 | Towards Auto-Generated Data Systems | 2023 | VLDB | 4.9793485e-05 |
| 11,921 | Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications | 2022 | VLDB | 4.9793485e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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