DB-MAGS: Multi-Anomaly Data Generation System for Transactional Databases
Summary: DB-MAGS: a data-generation system producing unified, realistic transactional DB anomaly datasets with fine-grained root-cause labels (5 major → 18 minor categories). Models causal and concurrent multi-anomaly compositions to enable comprehensive, diverse training/evaluation of data-driven root-cause diagnosis. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Yiqi Shen
- 2. Sijia Li
- 3. Miaodong Shen
- 4. Peng Cai
- 5. Weiyuan Xu
- 6. Kai Li
- 7. Jinlong Cai
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,022 | DBSherlock: A Performance Diagnostic Tool for Transactional Databases | 2016 | SIGMOD | 0.00014614917 |
| 4,868 | DBPA: A Benchmark for Transactional Database Performance Anomalies | 2023 | SIGMOD | 5.8629636e-05 |
| 6,901 | BALANCE: Bayesian Linear Attribution for Root Cause Localization | 2023 | SIGMOD | 4.8925595e-05 |
Previous
Page 1 / 1
Next