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Adaptive threshold sampling

Summary: Adaptive threshold sampling: changing probabilities via thresholds. Shows adaptive thresholds can emulate fixed sampling, enabling simple estimators; introduces samplers for memory budgets, stratified/multi-objective sampling, and sliding windows, including adaptive top-K. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6436
Venue
SIGMOD
Year
2022
Pagerank
4.2294678e-05
Overall Rank
9,962 | 30.70%
DOI
10.1145/3514221.3526122

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,981 Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality 2024 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
2,789 Optimal Sampling from Sliding Windows 2009 PODS 8.1249652e-05
2,878 Sampling Time-Based Sliding Windows in Bounded Space 2008 SIGMOD 7.9706235e-05
3,271 Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation 2018 SIGMOD 7.2968732e-05
5,594 Time-Decaying Aggregates in Out-of-order Streams 2008 PODS 5.4192122e-05
6,244 Approximate Distinct Counts for Billions of Datasets 2019 SIGMOD 5.139669e-05
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