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Proteus: A Self-Designing Range Filter
Summary: Self-designing approximate range filter Proteus tunes itself from samples to minimize FPR under a fixed space. CPFPR unifies probabilistic and deterministic design spaces; in RocksDB it yields up to 5.3x end-to-end gains over SuRF/Rosetta with low modeling cost and robust workloads.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6478
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.8849277e-05
- Overall Rank
- 4,836 | 66.40%
- DOI
-
10.1145/3514221.3526167
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 22 of 22 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,455 |
Grafite: Taming Adversarial Queries with Optimal Range Filters |
2024 |
SIGMOD |
5.4965299e-05 |
| 5,749 |
InfiniFilter: Expanding Filters to Infinity and Beyond |
2023 |
SIGMOD |
5.3420354e-05 |
| 5,769 |
Oasis: An Optimal Disjoint Segmented Learned Range Filter |
2024 |
VLDB |
5.3326049e-05 |
| 5,868 |
GRF: A Global Range Filter for LSM-Trees with Shape Encoding |
2024 |
SIGMOD |
5.2928769e-05 |
| 7,623 |
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads |
2023 |
SIGMOD |
4.6890662e-05 |
| 8,003 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6049527e-05 |
| 8,011 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
4.6022693e-05 |
| 8,333 |
How to Grow an LSM-tree? Towards Bridging the Gap Between Theory and Practice |
2025 |
SIGMOD |
4.5390511e-05 |
| 8,524 |
Aleph Filter: To Infinity in Constant Time |
2024 |
VLDB |
4.4893996e-05 |
| 8,722 |
Memento Filter: A Fast, Dynamic, and Robust Range Filter |
2024 |
SIGMOD |
4.4558244e-05 |
| 8,804 |
ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads |
2026 |
VLDB |
4.4424232e-05 |
| 9,069 |
Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space |
2024 |
SIGMOD |
4.3983078e-05 |
| 9,221 |
Diva: Dynamic Range Filter for Var-Length Keys and Queries |
2025 |
VLDB |
4.366098e-05 |
| 9,322 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
4.351469e-05 |
| 9,390 |
Rethinking The Compaction Policies in LSM-trees |
2025 |
SIGMOD |
4.341433e-05 |
| 9,467 |
Disco: A Compact Index for LSM-trees |
2025 |
SIGMOD |
4.3309383e-05 |
| 9,986 |
A Multi-tenant Relational OLTP Database at Salesforce |
2026 |
CIDR |
4.1905499e-05 |
| 10,021 |
Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,137 |
Aeris Filter: A Strongly and Monotonically Adaptive Range Filter |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,176 |
Improving Range Scan Performance in LSM-trees with Group Caching |
2026 |
SIGMOD |
4.1905499e-05 |
| 11,078 |
LavaStore: ByteDance's Purpose-built, High-performance, Cost-effective Local Storage Engine for Cloud Services |
2024 |
VLDB |
4.1905499e-05 |
| 11,224 |
A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams |
2023 |
SIGMOD |
4.1905499e-05 |
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.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 679 |
Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems |
2012 |
SIGMOD |
0.00018211621 |
| 1,169 |
SuRF: Practical Range Query Filtering with Fast Succinct Tries |
2018 |
SIGMOD |
0.00013530267 |
| 1,249 |
Don't Thrash: How to Cache Your Hash on Flash |
2012 |
VLDB |
0.00013040265 |
| 1,437 |
AsterixDB: A Scalable, Open Source BDMS |
2014 |
VLDB |
0.00011973401 |
| 1,438 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011965956 |
| 1,468 |
Adaptive Range Filters for Cold Data: Avoiding Trips to Siberia |
2013 |
VLDB |
0.00011831457 |
| 1,513 |
vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases |
2019 |
SIGMOD |
0.00011580425 |
| 1,669 |
Amazon DynamoDB: A Seamlessly Scalable Non-relational Datastore |
2012 |
SIGMOD |
0.00010945299 |
| 2,153 |
The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models |
2018 |
SIGMOD |
9.418541e-05 |
| 3,545 |
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores |
2020 |
SIGMOD |
6.9831585e-05 |
| 4,993 |
Stacked Filters: Learning to Filter by Structure |
2021 |
VLDB |
5.7749409e-05 |
| 5,313 |
Key-Value Storage Engines |
2020 |
SIGMOD |
5.5711707e-05 |
| 7,173 |
Coconut Palm: Static and Streaming Data Series Exploration Now in your Palm |
2019 |
SIGMOD |
4.8068454e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 6,788 |
Proteus: Autonomous Adaptive Storage for Mixed Workloads |
2022 |
SIGMOD |
4.9207259e-05 |
| 4,993 |
Stacked Filters: Learning to Filter by Structure |
2021 |
VLDB |
5.7749409e-05 |
| 8,722 |
Memento Filter: A Fast, Dynamic, and Robust Range Filter |
2024 |
SIGMOD |
4.4558244e-05 |
| 10,137 |
Aeris Filter: A Strongly and Monotonically Adaptive Range Filter |
2026 |
SIGMOD |
4.1905499e-05 |
| 4,319 |
Fast Queries Over Heterogeneous Data Through Engine Customization |
2016 |
VLDB |
6.2823814e-05 |
| 5,769 |
Oasis: An Optimal Disjoint Segmented Learned Range Filter |
2024 |
VLDB |
5.3326049e-05 |
| 5,455 |
Grafite: Taming Adversarial Queries with Optimal Range Filters |
2024 |
SIGMOD |
5.4965299e-05 |
| 3,614 |
SNARF: A Learning-Enhanced Range Filter |
2022 |
VLDB |
6.9124805e-05 |
| 1,169 |
SuRF: Practical Range Query Filtering with Fast Succinct Tries |
2018 |
SIGMOD |
0.00013530267 |
| 3,545 |
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores |
2020 |
SIGMOD |
6.9831585e-05 |