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Partitioned Learned Bloom Filter
[article]
2020
arXiv
pre-print
Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance in terms of the rate of false positives, by using a learned model for the represented set. However, previous methods for learned Bloom filters do not take full advantage of the learned model. Here we show how to frame the problem
arXiv:2006.03176v2
fatcat:optk7vjbbfghjamqq4opa55qky