{"id":38,"date":"2018-06-07T10:49:14","date_gmt":"2018-06-07T15:49:14","guid":{"rendered":"http:\/\/www.jmjatlanta.com\/?p=38"},"modified":"2018-06-07T11:42:59","modified_gmt":"2018-06-07T16:42:59","slug":"bloom-filter-types","status":"publish","type":"post","link":"https:\/\/www.jmjatlanta.com\/index.php\/2018\/06\/07\/bloom-filter-types\/","title":{"rendered":"Bloom Filter Types"},"content":{"rendered":"<p>In the implementation I am working with, a bloom filter allows us to determine if a particular item has been selected from a set. Both the set and subset can be large.<\/p>\n<p>While speed is a benefit, the real benefit for us is the memory footprint. We can quickly determine if the item is (probably) selected, without maintaining a list of (large) keys.<\/p>\n<p>I am researching various types of bloom filters that must fit within the following parameters:<\/p>\n<ul>\n<li>Low false positive<\/li>\n<li>Memory efficient<\/li>\n<li>Deletable<\/li>\n<li>No false negative<\/li>\n<\/ul>\n<p>The last two pose the challenge. A standard implementation of a bloom filter does not have false negatives, but items cannot be deleted. Deletions without knowing if an item was previously inserted can cause false negatives in the following implementations:<\/p>\n<ul>\n<li>counting bloom filter<\/li>\n<li>cuckoo filter<\/li>\n<li>dlbf (possibly, not tested)<\/li>\n<\/ul>\n<p>Research links:<\/p>\n<ul>\n<li>False negative research (2010): <a href=\"http:\/\/ieeexplore.ieee.org\/abstract\/document\/5374398\/\">http:\/\/ieeexplore.ieee.org\/abstract\/document\/5374398\/<\/a><\/li>\n<li>DlBF: A deletable bloom filter with supposed 0 false positives (2010): <a href=\"https:\/\/arxiv.org\/pdf\/1005.0352.pdf\">https:\/\/arxiv.org\/pdf\/1005.0352.pdf<\/a><\/li>\n<li>Wikipedia Bloom filter alternatives: <a href=\"https:\/\/en.wikipedia.org\/wiki\/Bloom_filter#Alternatives\">https:\/\/en.wikipedia.org\/wiki\/Bloom_filter#Alternatives<\/a><\/li>\n<li>An optimal bloom filter replacement (2005) : <a href=\"http:\/\/www.it-c.dk\/people\/pagh\/papers\/bloom.pdf\">http:\/\/www.it-c.dk\/people\/pagh\/papers\/bloom.pdf<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>In the implementation I am working with, a bloom filter allows us to determine if a particular item has been selected from a set. Both the set and subset can be large. While speed is a benefit, the real benefit for us is the memory footprint. We can quickly determine if the item is (probably) [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-38","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/posts\/38","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/comments?post=38"}],"version-history":[{"count":9,"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/posts\/38\/revisions"}],"predecessor-version":[{"id":47,"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/posts\/38\/revisions\/47"}],"wp:attachment":[{"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/media?parent=38"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/categories?post=38"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.jmjatlanta.com\/index.php\/wp-json\/wp\/v2\/tags?post=38"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}