A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams

Summary: Introduces Learned Cuckoo Filter (LCF) for approximate membership queries on data streams with variable sliding windows, using a trained oracle to adaptively maintain cuckoo filters. Offers compact LCF_C and theoretical LCF_O with space-accuracy guarantees; experiments show 61% space savings and 12x accuracy gains at the same space. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6827
Venue: SIGMOD
Year: 2023
Pagerank: 5.093636e-05
Overall Rank: 11,423 | 21.63%
DOI: 10.1145/3626758

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Authors

1. Yao Tian (Hong Kong University of Science and Technology)
2. Tingyun Yan (Guangdong University of Technology)
3. Ruiyuan Zhang (Hong Kong University of Science and Technology)
4. Kai Huang (Hong Kong University of Science and Technology)
5. Bolong Zheng (Huazhong University of Science and Technology)
6. Xiaofang Zhou (Hong Kong University of Science and Technology)

BibTeX Citation

@inproceedings{tian_sigmod23,
        title = {{A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams}},
        author = {Tian, Yao and Yan, Tingyun and Zhang, Ruiyuan and Huang, Kai and Zheng, Bolong and Zhou, Xiaofang},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626758},
        url = {https://dl.acm.org/doi/10.1145/3626758},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

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.

Rank	Cited Paper	Year	Venue	Pagerank
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254
789	Don't Thrash: How to Cache Your Hash on Flash	2012	VLDB	0.0001401724
880	SuRF: Practical Range Query Filtering with Fast Succinct Tries	2018	SIGMOD	0.00013432693
2,698	Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores	2020	SIGMOD	8.2450522e-05
2,919	SNARF: A Learning-Enhanced Range Filter	2022	VLDB	7.9628657e-05
3,533	Approximately Detecting Duplicates for Streaming Data using Stable Bloom Filters	2006	SIGMOD	7.3366144e-05
4,073	Stable Learned Bloom Filters for Data Streams	2020	VLDB	6.9242783e-05
4,119	Proteus: A Self-Designing Range Filter	2022	SIGMOD	6.8907395e-05
4,960	InfiniFilter: Expanding Filters to Infinity and Beyond	2023	SIGMOD	6.4280133e-05
4,971	Persistent Bloom Filter: Membership Testing for the Entire History	2018	SIGMOD	6.4195676e-05
8,020	Streaming Quotient Filter: A Near Optimal Approximate Duplicate Detection Approach for Data Streams	2013	VLDB	5.5059287e-05

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8	10,910	Cuckoo Heavy Keeper and the balancing act of maintaining heavy hitters in stream processing	2025	VLDB
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