{"id":4962,"date":"2012-12-04T09:45:00","date_gmt":"2012-12-04T09:45:00","guid":{"rendered":"http:\/\/cloudcomputing.sys-con.com\/node\/2471650"},"modified":"2012-12-04T09:45:00","modified_gmt":"2012-12-04T09:45:00","slug":"big-data-trees-with-hadoop-hdfs","status":"publish","type":"post","link":"https:\/\/icloud.pe\/blog\/big-data-trees-with-hadoop-hdfs\/","title":{"rendered":"Big Data Trees with Hadoop HDFS"},"content":{"rendered":"<p>Last month&#8217;s release of Revolution R Enterprise 6.1 added the capability to fit decision and regression trees on large data sets (using a new parallel external memory algorithm included in the RevoScaleR package). It also introduced the possibility of applying this and the other big-data statistical methods of RevoScaleR to data files distributed in in Hadoop&#8217;s HDFS file system*, using the Hadoop nodes themselves as the compute engine (with Revolution R Enterprise installed). Revolution Analytics&#8217; VP of Development Sue Ranney explained how this works in a recent webinar. I&#8217;ve embedded the slides below, and you can also watch the webinar&#8230;<\/p>\n<p>            David Smith<\/p>\n<p><a href=\"http:\/\/cloudcomputing.sys-con.com\/node\/2471650\" >read more<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Last month&#8217;s release of Revolution R Enterprise 6.1 added the capability to fit decision and regression trees on large data sets (using a new parallel external memory algorithm included in the RevoScaleR package). It also introduced the possibility of &#8230;<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-4962","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/4962","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/comments?post=4962"}],"version-history":[{"count":0,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/4962\/revisions"}],"wp:attachment":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/media?parent=4962"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/categories?post=4962"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/tags?post=4962"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}