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BALANCE: Bayesian Linear Attribution for Root Cause Localization

Summary: BALANCE applies Bayesian linear attribution (BMFS) to predict KPIs from candidate root causes, promoting sparsity while handling multicollinearity. It performs backward attribution with KPI merging for RCA, delivering real-time localization and measurable accuracy gains across diverse RCA tasks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6660
Venue
SIGMOD
Year
2023
Pagerank
5.6029996e-05
Overall Rank
7,506 | 48.51%
DOI
10.1145/3588949

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{chen_sigmod23,
        title = {{BALANCE: Bayesian Linear Attribution for Root Cause Localization}},
        author = {Chen, Chaoyu and Yu, Hang and Lei, Zhichao and Li, Jianguo and Ren, Shaokang and Zhang, Tingkai and Hu, Silin and Wang, Jianchao and Shi, Wenhui},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588949},
        url = {https://dl.acm.org/doi/10.1145/3588949},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
11,331 DB-MAGS: Multi-Anomaly Data Generation System for Transactional Databases 2024 VLDB 5.093636e-05
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