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Variance Estimation over Sliding Windows

Summary: First work to close the complexity gap for ε-approximate variance over sliding windows, improving on prior O(1/ε^2 · log N) space vs. the Ω(1/ε · log N) lower bound. Achieves optimal O(1/ε · log N) space and O(1) worst-case update time. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1431
Venue
PODS
Year
2007
Pagerank
5.093636e-05
Overall Rank
12,629 | 13.36%
DOI
10.1145/1265530.1265562

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Authors

BibTeX Citation

@inproceedings{zhang_pods07,
        address = {New York, NY, USA},
        series = {{PODS} '07},
        title = {{Variance Estimation over Sliding Windows}},
        url = {https://dl.acm.org/doi/10.1145/1265530.1265562},
        doi = {10.1145/1265530.1265562},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Zhang, Linfeng and Guan, Yong},
        year = {2007}
}

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