Unsupervised Contextual Anomaly Detection for Database Systems
Summary: Unsupervised anomaly detection in database access via semantic-context comparison. Trans-DAS learns operation semantics with attention and bidirectional contexts; UCAD combines a preprocessing stage and a semantic-based detector to identify stealthy anomalies. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sainan Li
- 2. Qilei Yin
- 3. Guoliang Li
- 4. Qi Li
- 5. Zhuotao Liu
- 6. Jinwei Zhu
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,762 | METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection | 2024 | VLDB | 5.9395463e-05 |
| 6,394 | Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data | 2024 | SIGMOD | 5.0829207e-05 |
| 10,365 | Agree to Disagree: Robust Anomaly Detection with Noisy Labels | 2025 | SIGMOD | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 961 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00015001792 |
Previous
Page 1 / 1
Next