Variability in Data Streams
Summary: Introduce a stream parameter “variability” v for distributed non‑monotonic update streams and show how to adapt monotone-stream algorithms to track integer functions with small relative error so communication scales as Õ(v) instead of Θ(n). Prove v=O(log f(n)) for monotone streams and v=o(n) for nearly‑monotone or random-walk inputs, implying practical, smoothly degrading communication bounds and minor algorithmic modifications. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. David Felber
- 2. Rafail Ostrovsky
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | 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 |
|---|---|---|---|---|
| 4,190 | Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks | 2012 | PODS | 6.3739017e-05 |
| 4,249 | Optimal Tracking of Distributed Heavy Hitters and Quantiles | 2009 | PODS | 6.3245666e-05 |
| 6,716 | Continuous Distributed Counting for Non-monotonic Streams | 2012 | PODS | 4.9507254e-05 |
| 7,515 | Logging Every Footstep: Quantile Summaries for the Entire History | 2010 | SIGMOD | 4.7180617e-05 |
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