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
- 2. Yong Guan
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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 |
|---|---|---|---|---|
| 43 | Models and Issues in Data Stream Systems | 2002 | PODS | 0.00072723062 |
| 785 | StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time | 2002 | VLDB | 0.00016664156 |
| 848 | Approximate Counts and Quantiles over Sliding Windows | 2004 | PODS | 0.0001597308 |
| 2,404 | Maintaining Variance and k–Medians over Data Stream Windows | 2003 | PODS | 8.8837279e-05 |
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