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
- 2. Simon Scherrer
- 3. Adrian Perrig
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076161096 |
| 3,751 | BurstSketch: Finding Bursts in Data Streams | 2021 | SIGMOD | 6.7888099e-05 |
| 4,905 | Randomized Error Removal for Online Spread Estimation in Data Streaming | 2021 | VLDB | 5.8398332e-05 |
| 6,905 | PR-Sketch: Monitoring Per-key Aggregation of Streaming Data with Nearly Full Accuracy | 2021 | VLDB | 4.8925595e-05 |
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