{"action":"closed","issue":{"url":"https://api.github.com/repos/require-lx/community/issues/88","labels_url":"https://api.github.com/repos/require-lx/community/issues/88/labels{/name}","comments_url":"https://api.github.com/repos/require-lx/community/issues/88/comments","events_url":"https://api.github.com/repos/require-lx/community/issues/88/events","html_url":"https://github.com/require-lx/community/issues/88","id":81946845,"number":88,"title":"TALK: Probabilistic Data Structures","user":{"login":"mping","id":88425,"avatar_url":"https://avatars.githubusercontent.com/u/88425?v=3","gravatar_id":"","url":"https://api.github.com/users/mping","html_url":"https://github.com/mping","followers_url":"https://api.github.com/users/mping/followers","following_url":"https://api.github.com/users/mping/following{/other_user}","gists_url":"https://api.github.com/users/mping/gists{/gist_id}","starred_url":"https://api.github.com/users/mping/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mping/subscriptions","organizations_url":"https://api.github.com/users/mping/orgs","repos_url":"https://api.github.com/users/mping/repos","events_url":"https://api.github.com/users/mping/events{/privacy}","received_events_url":"https://api.github.com/users/mping/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/require-lx/community/labels/talk","name":"talk","color":"009800"}],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":4,"created_at":"2015-05-28T14:15:29Z","updated_at":"2015-09-20T13:34:53Z","closed_at":"2015-09-20T13:34:53Z","body":"# intro\r\nIn this talk I want to describe a bit some data structures that can give very approximate answers with a much lower footprint than what would be required to give an exact value.\r\n\r\n# description\r\nSome data structures and algorithms such as HyperLogLog, Bloom Filters, etc can provide an interesting tradeoff between accuracy and memory, being ideal candidates for a range of applications.\r\n\r\nIn this talk I want to expose some of the problems of digging through large volumes of data, as well as some of the data structures that can give an approximate answer to queries such as cardinality estimation, membership, quantile, etc.\r\n\r\n----\r\n\r\nAlthough the language is dense, the talk will be light!"}}