Stable Learned Bloom Filters for Data Streams
Summary: Introduces Stable Learned Bloom Filters (SLBF) to stabilize updates in learned Bloom filters for data streams. Proposes s-SLBF and g-SLBF; theory: FPR constant under insertions; experiments: comparable FNR with improved FPR/storage vs non-learned Bloom filters. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Qiyu Liu (Hong Kong University of Science and Technology)
- 2. Libin Zheng (Hong Kong University of Science and Technology)
- 3. Yanyan Shen (Shanghai Jiao Tong University)
- 4. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@article{liu_vldb20,
title = {{Stable Learned Bloom Filters for Data Streams}},
author = {Liu, Qiyu and Zheng, Libin and Shen, Yanyan and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {11},
pages = {2355--2367},
doi = {10.14778/3407790.3407830},
url = {https://doi.org/10.14778/3407790.3407830},
year = {2020}
}
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