Approximate Frequency Counts over Data Streams
Summary: Tracing the Approximate Frequency Counts work and its influence on data-stream sketching, heavy-hitter detection, and sublinear memory algorithms. Also surveys a recent trend: personal data streams for quality-of-life gains. (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 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,884 | BPTree: an ℓ2 Heavy Hitters Algorithm Using Constant Memory | 2017 | PODS | 7.9620506e-05 |
| 8,250 | Stingy Sketch: A Sketch Framework for Accurate and Fast Frequency Estimation | 2022 | VLDB | 4.5506131e-05 |
| 10,659 | Cuckoo Heavy Keeper and the balancing act of maintaining heavy hitters in stream processing | 2025 | VLDB | 4.1945683e-05 |
| 10,901 | Streaming Algorithms with Few State Changes | 2024 | PODS | 4.1945683e-05 |
| 11,320 | Truly Perfect Samplers for Data Streams and Sliding Windows | 2022 | PODS | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 166 | Approximate Frequency Counts over Data Streams | 2002 | VLDB | 0.00039361552 |
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