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More Optimal Bloom Filters
The Bloom filter, conceived by Burton H. Bloom in 1970, is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. False positives are possible, but false negatives ar...
About This Episode
More Optimal Bloom Filters is an episode from Scale Cast – A podcast about big data, distributed systems, and scalability. The Bloom filter, conceived by Burton H. Bloom in 1970, is a space-efficient probabilistic data structure that is use...
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This episode belongs to Scale Cast – A podcast about big data, distributed systems, and scalability.
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Episode Details
Published Apr 18, 2008, audio available.
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Questions About This Episode
What is More Optimal Bloom Filters about?
The Bloom filter, conceived by Burton H. Bloom in 1970, is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. False positives are possible, but false negatives are not. Elements can be added to the set, but not removed (though this can be addressed with […]
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Which podcast is More Optimal Bloom Filters from?
More Optimal Bloom Filters is an episode from Scale Cast – A podcast about big data, distributed systems, and scalability.
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When was this episode published?
This episode was published on Apr 18, 2008.
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Are there related episodes from Scale Cast – A podcast about big data, distributed systems, and scalability?
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Where can I listen to More Optimal Bloom Filters?
You can listen to More Optimal Bloom Filters on this page when the episode audio is available from the podcast feed.
Which podcast is this episode from?
More Optimal Bloom Filters is from Scale Cast – A podcast about big data, distributed systems, and scalability.
What are the episode details?
Published Apr 18, 2008