Building a Better Bloom Filter

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Building a Better Bloom Filter

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Title: Building a Better Bloom Filter
Author: Kirsch, Adam; Mitzenmacher, Michael D.

Note: Order does not necessarily reflect citation order of authors.

Citation: Kirsch, Adam and Michael Mitzenmacher. Building a Better Bloom Filter. Harvard Computer Science Group Technical Report TR-02-05.
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Abstract: A technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate additional hash functions of the form gi(x) = h1(x) + ih2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false positive probability. This leads to less computation and potentially less need for randomness in practice.
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:23017278
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