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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)

Paper ID
6375
Venue
SIGMOD
Year
2022
Pagerank
6.3987603e-05
Overall Rank
5,020 | 65.56%
DOI
10.1145/3514221.3517861

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{li_sigmod22,
        title = {{Unsupervised Contextual Anomaly Detection for Database Systems}},
        author = {Li, Sainan and Yin, Qilei and Li, Guoliang and Li, Qi and Liu, Zhuotao and Zhu, Jinwei},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517861},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517861},
        year = {2022}
}

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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
962 DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation 2015 SIGMOD 0.00012936472
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