DBMind: A Self-Driving Platform in openGauss
Summary: DBMind is a self-driving platform for openGauss enabling autonomous monitoring, diagnosis, and optimization. It combines LSTM-based root-cause analysis with DRL knob tuning, RL-based index/view selection, and encoder-decoder view materialization, demonstrated on real scenarios. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xuanhe Zhou (Tsinghua University)
- 2. Lianyuan Jin (Tsinghua University)
- 3. Ji Sun (Tsinghua University)
- 4. Xinyang Zhao (Tsinghua University)
- 5. Xiang Yu (Tsinghua University)
- 6. Jianhua Feng (Tsinghua University)
- 7. Shifu Li (Huawei)
- 8. Tianqing Wang (Huawei)
- 9. Kun Li (Huawei)
- 10. Luyang Liu (Huawei)
BibTeX Citation
@article{zhou_vldb21,
title = {{DBMind: A Self-Driving Platform in openGauss}},
author = {Zhou, Xuanhe and Jin, Lianyuan and Sun, Ji and Zhao, Xinyang and Yu, Xiang and Feng, Jianhua and Li, Shifu and Wang, Tianqing and Li, Kun and Liu, Luyang},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2743--2746},
doi = {10.14778/3476311.3476334},
url = {https://doi.org/10.14778/3476311.3476334},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,452 | A Learned Query Rewrite System using Monte Carlo Tree Search | 2022 | VLDB | 8.5584e-05 |
| 3,662 | openGauss: An Autonomous Database System | 2021 | VLDB | 7.2166682e-05 |
| 5,169 | LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning | 2022 | SIGMOD | 6.3349035e-05 |
| 5,340 | Machine Learning for Databases | 2021 | VLDB | 6.2603359e-05 |
| 7,825 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 5.5367884e-05 |
| 9,949 | UniView: A Unified Autonomous Materialized View Management System for Various Databases | 2024 | VLDB | 5.1915905e-05 |
| 10,506 | This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! | 2026 | SIGMOD | 5.093636e-05 |
| 10,532 | DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 334 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.00020875082 |
| 465 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.0001803934 |
| 498 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.00017440583 |
| 2,812 | Query Performance Prediction for Concurrent Queries using Graph Embedding | 2020 | VLDB | 8.0979597e-05 |
| 2,888 | AI Meets Database: AI4DB and DB4AI | 2021 | SIGMOD | 7.9941489e-05 |
| 3,662 | openGauss: An Autonomous Database System | 2021 | VLDB | 7.2166682e-05 |
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