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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
12578
Venue
VLDB
Year
2021
Pagerank
5.78027e-05
Overall Rank
4,994 | 65.26%
DOI
10.14778/3436905.3436919

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank Citing Paper Year Venue Pagerank
2,798 Chucky: A Succinct Cuckoo Filter for LSM-Tree 2021 SIGMOD 8.1080111e-05
4,835 Proteus: A Self-Designing Range Filter 2022 SIGMOD 5.8905445e-05
5,739 InfiniFilter: Expanding Filters to Infinity and Beyond 2023 SIGMOD 5.3471718e-05
5,863 GRF: A Global Range Filter for LSM-Trees with Shape Encoding 2024 SIGMOD 5.2979639e-05
7,663 Optimizing Collections of Bloom Filters within a Space Budget 2024 VLDB 4.6857816e-05
8,525 Aleph Filter: To Infinity in Constant Time 2024 VLDB 4.4937074e-05
8,720 Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity 2022 SIGMOD 4.4609699e-05
8,957 Adaptive Quotient Filters 2024 SIGMOD 4.4211093e-05
10,021 Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding 2026 SIGMOD 4.1945683e-05
11,356 Workload-Adaptive Filtering in Storage Engines 2022 SIGMOD 4.1945683e-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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