Optimizing Learned Bloom Filters: How Much Should Be Learned?
Author:
Affiliation:
1. Department of Statistics and Department of Computer Science, Rice University, Houston, TX, USA
2. Departamento de Ingeniería Telemática, Universidad Carlos III de Madrid, Leganés, Spain
Funder
EU H2020 Project PIMCITY
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Computer Science,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/4563995/9869752/09724196.pdf?arnumber=9724196
Reference14 articles.
1. Space/time trade-offs in hash coding with allowable errors
2. Theory and Practice of Bloom Filters for Distributed Systems
3. Exact and approximate membership testers
4. The Tandem Counting Bloom Filter - It Takes Two Counters to Tango
5. On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters
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1. A Critical Analysis of Classifier Selection in Learned Bloom Filters: The Essentials;Engineering Applications of Neural Networks;2023
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