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Stacked Filters: Learning to Filter by Structure

Summary: Stacked Filters blend workload knowledge of negatives into a hashed filter stack for fast filtering. Adaptive, they learn on the fly and outperform query-agnostic and classifier-based filters in end-to-end throughput, up to 130x within memory. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h21a1a2421ab3573a
Venue
VLDB
Year
2021
Pagerank
6.4969374e-05
Overall Rank
4,625 | 68.91%
DOI
10.14778/3436905.3436919

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{deeds_vldb21,
        title = {{Stacked Filters: Learning to Filter by Structure}},
        author = {Deeds, Kyle and Hentschel, Brian and Idreos, Stratos},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {4},
        pages = {600--612},
        doi = {10.14778/3436905.3436919},
        url = {https://doi.org/10.14778/3436905.3436919},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank Citing Paper Year Venue Pagerank
1,980 Chucky: A Succinct Cuckoo Filter for LSM-Tree 2021 SIGMOD 9.2595896e-05
4,197 Proteus: A Self-Designing Range Filter 2022 SIGMOD 6.7417716e-05
4,750 GRF: A Global Range Filter for LSM-Trees with Shape Encoding 2024 SIGMOD 6.4354422e-05
5,081 InfiniFilter: Expanding Filters to Infinity and Beyond 2023 SIGMOD 6.2854372e-05
7,358 Optimizing Collections of Bloom Filters within a Space Budget 2024 VLDB 5.5431911e-05
7,940 Aleph Filter: To Infinity in Constant Time 2024 VLDB 5.4219572e-05
9,003 Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity 2022 SIGMOD 5.2392472e-05
9,051 Adaptive Quotient Filters 2024 SIGMOD 5.2304894e-05
9,643 Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding 2026 SIGMOD 5.1453267e-05
11,863 Workload-Adaptive Filtering in Storage Engines 2022 SIGMOD 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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