METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

Summary: METER: base detector trained on historical central concepts and a hypernetwork that generates parameter shifts for rapid adaptation to new concepts, avoiding costly retraining. Lightweight evidential‑DL drift controller supplies interpretable, robust drift signals and yields improved OAD accuracy and latency. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13943
Venue: VLDB
Year: 2024
Pagerank: 6.4955135e-05
Overall Rank: 4,814 | 66.98%
DOI: 10.14778/3636218.3636233

Incoming Non-self Citations Over Time

Authors

1. Jiaqi Zhu (Beijing Institute of Technology)
2. Shaofeng Cai (National University of Singapore)
3. Fang Deng (Beijing Institute of Technology)
4. Beng Chin Ooi (National University of Singapore)
5. Wenqiao Zhang (Zhejiang University)

BibTeX Citation

@article{zhu_vldb24,
        title = {{METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection}},
        author = {Zhu, Jiaqi and Cai, Shaofeng and Deng, Fang and Ooi, Beng Chin and Zhang, Wenqiao},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {4},
        pages = {794--807},
        doi = {10.14778/3636218.3636233},
        url = {https://doi.org/10.14778/3636218.3636233},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,189	Adaptive Outlier Detection over Data Stream	2026	SIGMOD	5.093636e-05
10,506	This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!	2026	SIGMOD	5.093636e-05
10,856	Optimized Batch Prompting for Cost-effective LLMs	2025	VLDB	5.093636e-05
10,961	BURST: Rendering Clustering Techniques Suitable for Evolving Streams	2025	VLDB	5.093636e-05
11,215	OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams	2024	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
142	LOF: Identifying Density-Based Local Outliers	2000	SIGMOD	0.0002962566
881	Meaningful Change Detection in Structured Data	1997	SIGMOD	0.00013430652
1,029	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00012557065
1,557	Distance-based Outlier Detection in Data Streams	2016	VLDB	0.00010367451
1,805	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	9.7116108e-05
2,004	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.3207067e-05
3,135	Real-Time Distance-Based Outlier Detection in Data Streams	2021	VLDB	7.7214053e-05
3,376	Robust and Transferable Log-based Anomaly Detection	2023	SIGMOD	7.4605908e-05
4,390	ARM-Net: Adaptive Relation Modeling Network for Structured Data	2021	SIGMOD	6.7290926e-05
5,020	Unsupervised Contextual Anomaly Detection for Database Systems	2022	SIGMOD	6.3987603e-05
5,946	Streaming Anomaly Detection Using Randomized Matrix Sketching	2016	VLDB	6.0324341e-05
7,587	PROUD: PaRallel OUtlier Detection for streams	2020	SIGMOD	5.5908113e-05
8,073	Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries	2021	SIGMOD	5.4932762e-05
11,391	Regularized Pairwise Relationship based Analytics for Structured Data	2023	SIGMOD	5.093636e-05
11,789	TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications	2020	SIGMOD	5.093636e-05

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