DBScholar

Back to papers

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)

Paper ID
12309
Venue
VLDB
Year
2020
Pagerank
6.9242783e-05
Overall Rank
4,073 | 72.06%
DOI
10.14778/3407790.3407830

Incoming Non-self Citations Over Time

Authors

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers