LadderFilter: Filtering Infrequent Items with Small Memory and Time Overhead
Summary: Filters infrequent items in data streams with memory-friendly multi-LRU queues and SIMD-based LRU to cut time overhead. Applied to four sketch types, LadderFilter yields up to 60.6× accuracy and 1.37× throughput while keeping memory low; open-source. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuanpeng Li (Peking University; Peng Cheng Laboratory)
- 2. Feiyu Wang (Peking University)
- 3. Xiang Yu (Peking University)
- 4. Yilong Yang (Xidian University)
- 5. Kaicheng Yang (Peking University)
- 6. Tong Yang (Peking University; Peng Cheng Laboratory)
- 7. Zhuo Ma (Xidian University)
- 8. Bin Cui (Peking University)
- 9. Steve Uhlig (Queen Mary University of London)
BibTeX Citation
@inproceedings{li_sigmod23,
title = {{LadderFilter: Filtering Infrequent Items with Small Memory and Time Overhead}},
author = {Li, Yuanpeng and Wang, Feiyu and Yu, Xiang and Yang, Yilong and Yang, Kaicheng and Yang, Tong and Ma, Zhuo and Cui, Bin and Uhlig, Steve},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588690},
url = {https://dl.acm.org/doi/10.1145/3588690},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,296 | ChainedFilter: Combining Membership Filters by Chain Rule | 2023 | SIGMOD | 5.0430473e-05 |
| 10,534 | SieveSketch: A Fine-grained and Adaptive Sketch Framework for Accurate Frequency Estimation | 2026 | SIGMOD | 4.9793485e-05 |
| 11,114 | Pandora: An Efficient and Rapid Solution for Persistence-Based Tasks in High-Speed Data Streams | 2025 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 124 | Approximate Frequency Counts over Data Streams | 2002 | VLDB | 0.00030600691 |
| 1,319 | Augmented Sketch: Faster and More Accurate Stream Processing | 2016 | SIGMOD | 0.00011045888 |
| 1,970 | Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing | 2018 | SIGMOD | 9.2902522e-05 |
| 2,930 | Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation | 2018 | SIGMOD | 7.8415815e-05 |
| 3,980 | BurstSketch: Finding Bursts in Data Streams | 2021 | SIGMOD | 6.8788724e-05 |
| 4,979 | Pyramid Sketch: a Sketch Framework for Frequency Estimation of Data Streams | 2017 | VLDB | 6.3300966e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
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| 2 | 6,547 | Memento Filter: A Fast, Dynamic, and Robust Range Filter | 2024 | SIGMOD |
| 3 | 10,534 | SieveSketch: A Fine-grained and Adaptive Sketch Framework for Accurate Frequency Estimation | 2026 | SIGMOD |
| 4 | 3,703 | Stable Learned Bloom Filters for Data Streams | 2020 | VLDB |
| 5 | 4,979 | Pyramid Sketch: a Sketch Framework for Frequency Estimation of Data Streams | 2017 | VLDB |
| 6 | 1,319 | Augmented Sketch: Faster and More Accurate Stream Processing | 2016 | SIGMOD |
| 7 | 11,863 | Workload-Adaptive Filtering in Storage Engines | 2022 | SIGMOD |
| 8 | 11,737 | A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams | 2023 | SIGMOD |
| 9 | 1,970 | Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing | 2018 | SIGMOD |
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