Finding Persistent Items in Data Streams
Summary: Introduces persistent-item mining, targeting long-term persistence rather than short-window frequency. PIE uses Raptor-coded item IDs and tiny per-period sketches to recover persistent items with controllable false negatives, achieving 19.5× lower FNR than adapted prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Haipeng Dai (Nanjing University)
- 2. Muhammad Shahzad (North Carolina State University)
- 3. Alex X. Liu (Nanjing University)
- 4. Yuankun Zhong (Nanjing University)
BibTeX Citation
@article{dai_vldb17,
title = {{Finding Persistent Items in Data Streams}},
author = {Dai, Haipeng and Shahzad, Muhammad and Liu, Alex X. and Zhong, Yuankun},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {4},
pages = {289--300},
doi = {10.14778/3025111.3025112},
url = {https://doi.org/10.14778/3025111.3025112},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,905 | Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing | 2018 | SIGMOD | 9.5034849e-05 |
| 3,898 | BurstSketch: Finding Bursts in Data Streams | 2021 | SIGMOD | 7.0367583e-05 |
| 7,036 | On-Off Sketch: A Fast and Accurate Sketch on Persistence | 2021 | VLDB | 5.7212447e-05 |
| 7,498 | Stingy Sketch: A Sketch Framework for Accurate and Fast Frequency Estimation | 2022 | VLDB | 5.6031077e-05 |
| 7,716 | Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent Items | 2023 | SIGMOD | 5.5605526e-05 |
| 10,673 | Pandora: An Efficient and Rapid Solution for Persistence-Based Tasks in High-Speed Data Streams | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 122 | Approximate Frequency Counts over Data Streams | 2002 | VLDB | 0.00031260115 |
| 885 | Finding Frequent Items in Data Streams | 2008 | VLDB | 0.00013419017 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,638 | Frequent Elements with Witnesses in Data Streams | 2021 | PODS |
| 2 | 13,997 | Mining Frequent Itemsets with Bit Strings and Trie | 2002 | VLDB |
| 3 | 5,538 | Mining Frequent Patterns with Differential Privacy | 2013 | VLDB |
| 4 | 122 | Approximate Frequency Counts over Data Streams | 2002 | VLDB |
| 5 | 6,305 | A Regression-Based Temporal Pattern Mining Scheme for Data Streams | 2003 | VLDB |
| 6 | 12,176 | Resource-oriented Approximation for Frequent Itemset Mining from Bursty Data Streams | 2014 | SIGMOD |
| 7 | 7,036 | On-Off Sketch: A Fast and Accurate Sketch on Persistence | 2021 | VLDB |
| 8 | 7,548 | Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory | 2024 | SIGMOD |
| 9 | 4,683 | False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams | 2004 | VLDB |
| 10 | 885 | Finding Frequent Items in Data Streams | 2008 | VLDB |