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)
Incoming Non-self Citations Over Time
Authors
- 1. Eric R. Knorr (Harvard University)
- 2. Baptiste Lemaire (Harvard University)
- 3. Andrew Lim (Harvard University)
- 4. Siqiang Luo (Nanyang Technological University)
- 5. Huanchen Zhang (Tsinghua University)
- 6. Stratos Idreos (Harvard University)
- 7. Michael Mitzenmacher (Harvard University)
BibTeX Citation
@inproceedings{knorr_sigmod22,
title = {{Proteus: A Self-Designing Range Filter}},
author = {Knorr, Eric R. and Lemaire, Baptiste and Lim, Andrew and Luo, Siqiang and Zhang, Huanchen and Idreos, Stratos and Mitzenmacher, Michael},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526167},
url = {https://dl.acm.org/doi/10.1145/3514221.3526167},
year = {2022}
}
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