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 (University of California Los Angeles)
- 2. Rafail Ostrovsky (University of California Los Angeles)
BibTeX Citation
@inproceedings{felber_pods16,
address = {New York, NY, USA},
series = {{PODS} '16},
title = {{Variability in Data Streams}},
url = {https://dl.acm.org/doi/10.1145/2902251.2902277},
doi = {10.1145/2902251.2902277},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Felber, David and Ostrovsky, Rafail},
year = {2016}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| 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 |
|---|---|---|---|---|
| 3,832 | Optimal Tracking of Distributed Heavy Hitters and Quantiles | 2009 | PODS | 7.0856664e-05 |
| 3,856 | Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks | 2012 | PODS | 7.0690791e-05 |
| 6,871 | Continuous Distributed Counting for Non-monotonic Streams | 2012 | PODS | 5.7496345e-05 |
| 7,936 | Logging Every Footstep: Quantile Summaries for the Entire History | 2010 | SIGMOD | 5.5181056e-05 |
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