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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)

Paper ID
14468
Venue
VLDB
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,532 | 27.75%
DOI
10.14778/3797919.3797937

Incoming Non-self Citations Over Time

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Authors

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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Outgoing Citations (Sorted by Pagerank)

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