On Biased Reservoir Sampling in the Presence of Stream Evolution
Summary: Biased reservoir sampling using temporal bias to emphasize recent items under evolution, without knowing stream size; memoryless bias enables efficient maintenance. Theoretical results bound the maximum reservoir size, with implications for query estimation and evolution. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,126 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.4887794e-05 |
| 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 |
| 7,928 | Accelerating Aggregation Queries on Unstructured Streams of Data | 2023 | VLDB | 4.613455e-05 |
| 9,118 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB | 4.3928288e-05 |
| 10,981 | Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality | 2024 | SIGMOD | 4.1945683e-05 |
| 11,502 | In the Land of Data Streams where Synopses are Missing, One Framework to Bring Them All | 2021 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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