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)
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Authors
- 1. Linfeng Zhang (Iowa State University)
- 2. Yong Guan (Iowa State University)
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}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 26 | Models and Issues in Data Stream Systems | 2002 | PODS | 0.00052982574 |
| 668 | StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time | 2002 | VLDB | 0.00015166017 |
| 691 | Approximate Counts and Quantiles over Sliding Windows | 2004 | PODS | 0.00014927798 |
| 2,218 | Maintaining Variance and k–Medians over Data Stream Windows | 2003 | PODS | 8.9331834e-05 |
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