Streaming Anomaly Detection Using Randomized Matrix Sketching

Summary: One-pass, bounded-storage anomaly detection for massive streams via randomized matrix sketching that maintains an orthogonal low-rank basis. Reconstruction-error tests approximate costly global SVD updates, with randomized low-rank acceleration and strong empirical scalability. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11500
Venue: VLDB
Year: 2016
Pagerank: 6.0324341e-05
Overall Rank: 5,946 | 59.21%
DOI: 10.14778/2850583.2850593

Incoming Non-self Citations Over Time

Authors

1. Hao Huang (General Electric)
2. Shiva Prasad Kasiviswanathan (Samsung)

BibTeX Citation

@article{huang_vldb16,
        title = {{Streaming Anomaly Detection Using Randomized Matrix Sketching}},
        author = {Huang, Hao and Kasiviswanathan, Shiva Prasad},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {3},
        pages = {192},
        doi = {10.14778/2850583.2850593},
        url = {https://doi.org/10.14778/2850583.2850593},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,814	METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection	2024	VLDB	6.4955135e-05
5,335	Clustering Stream Data by Exploring the Evolution of Density Mountain	2018	VLDB	6.2618226e-05
8,268	Efficient Matrix Sketching over Distributed Data	2017	PODS	5.4574671e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 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
1,252	Streaming Pattern Discovery in Multiple Time-Series	2005	VLDB	0.00011483752

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