DB-MAGS: Multi-Anomaly Data Generation System for Transactional Databases
Summary: DB-MAGS generates realistic transactional-database performance-anomaly data with fine-grained root causes: 5 major and 18 minor single-anomaly classes. It also models causal versus concurrent anomaly combinations, enabling comprehensive multi-anomaly reproduction. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yiqi Shen (East China Normal University)
- 2. Sijia Li (East China Normal University)
- 3. Miaodong Shen (East China Normal University)
- 4. Peng Cai (East China Normal University)
- 5. Weiyuan Xu (Meituan)
- 6. Kai Li (Meituan)
- 7. Jinlong Cai (Meituan)
BibTeX Citation
@article{shen_vldb24,
title = {{DB-MAGS: Multi-Anomaly Data Generation System for Transactional Databases}},
author = {Shen, Yiqi and Li, Sijia and Shen, Miaodong and Cai, Peng and Xu, Weiyuan and Li, Kai and Cai, Jinlong},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4497--4500},
doi = {10.14778/3685800.3685909},
url = {https://doi.org/10.14778/3685800.3685909},
year = {2024}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 798 | DBSherlock: A Performance Diagnostic Tool for Transactional Databases | 2016 | SIGMOD | 0.00013919795 |
| 5,023 | DBPA: A Benchmark for Transactional Database Performance Anomalies | 2023 | SIGMOD | 6.3979497e-05 |
| 7,506 | BALANCE: Bayesian Linear Attribution for Root Cause Localization | 2023 | SIGMOD | 5.6029996e-05 |
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