Bayesian Sketches for Volume Estimation in Data Streams
Summary: Three sketch algorithms combining Bayesian estimation, counter-cardinality signals, and lightweight ML to deliver highly accurate per-key volume estimates in data streams. Achieves <4% average relative error with sketch-level runtime, breaking the accuracy/efficiency trade-off. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Francesco Da Dalt (ETH Zurich)
- 2. Simon Scherrer (ETH Zurich)
- 3. Adrian Perrig (ETH Zurich)
BibTeX Citation
@article{dalt_vldb23,
title = {{Bayesian Sketches for Volume Estimation in Data Streams}},
author = {Da Dalt, Francesco and Scherrer, Simon and Perrig, Adrian},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {4},
pages = {657--669},
doi = {10.14778/3574245.3574252},
url = {https://doi.org/10.14778/3574245.3574252},
year = {2023}
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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 |
|---|---|---|---|---|
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 3,898 | BurstSketch: Finding Bursts in Data Streams | 2021 | SIGMOD | 7.0367583e-05 |
| 5,385 | Randomized Error Removal for Online Spread Estimation in Data Streaming | 2021 | VLDB | 6.2369655e-05 |
| 7,110 | PR-Sketch: Monitoring Per-key Aggregation of Streaming Data with Nearly Full Accuracy | 2021 | VLDB | 5.7000212e-05 |
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