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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)

Paper ID
6575
Venue
SIGMOD
Year
2023
Pagerank
5.5302333e-05
Overall Rank
7,860 | 46.08%
DOI
10.1145/3588690

Incoming Non-self Citations Over Time

Authors

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}
}

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