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Maintaining Variance and k–Medians over Data Stream Windows

Summary: Sliding-window streaming algorithms: variance estimator with relative error ε using O((1/ε^2) log N) space. Also a constant-factor approximate k‑median for last N points using O((1/τ) k^4 N^{2τ} log^2 N) space, τ<1/2 trades space for approximation O(2^{O(1/τ)}). (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1294
Venue
PODS
Year
2003
Pagerank
8.9331834e-05
Overall Rank
2,218 | 84.79%
DOI
10.1145/773153.773176

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{babcock_pods03,
        address = {New York, NY, USA},
        series = {{PODS} '03},
        title = {{Maintaining Variance and k–Medians over Data Stream Windows}},
        url = {https://dl.acm.org/doi/10.1145/773153.773176},
        doi = {10.1145/773153.773176},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Babcock, Brian and Datar, Mayur and Motwani, Rajeev and O'Callaghan, Liadan},
        year = {2003}
}

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