DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs
Summary: DBAIOps combines heterogeneous knowledge graphs, 800+ anomaly models, and reasoning LLMs for DBA-style diagnosis, capturing troubleshooting expertise beyond rules or RAG. Automatic path exploration fills missing links and produces root-cause reports across 25 DB systems. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Wei Zhou (Shanghai Jiao Tong University)
- 2. Peng Sun (Baisheng (Shenzhen) Technology Co., Ltd.)
- 3. Xuanhe Zhou (Shanghai Jiao Tong University)
- 4. Qianglei Zang (Baisheng (Shenzhen) Technology Co., Ltd.)
- 5. Ji Xu (Baisheng (Shenzhen) Technology Co., Ltd.)
- 6. Tieying Zhang (Bytedance)
- 7. Guoliang Li (Tsinghua University)
- 8. Fan Wu (Shanghai Jiao Tong University)
BibTeX Citation
@article{zhou_vldb26,
title = {{DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs}},
author = {Zhou, Wei and Sun, Peng and Zhou, Xuanhe and Zang, Qianglei and Xu, Ji and Zhang, Tieying and Li, Guoliang and Wu, Fan},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {6},
pages = {1319--1331},
doi = {10.14778/3797919.3797937},
url = {https://doi.org/10.14778/3797919.3797937},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,200 | Automating Database-Native Function Code Synthesis with LLMs | 2026 | SIGMOD | 5.093636e-05 |
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