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Open Source Authors: Sematext Blog, Carmen Gonzalez, Elizabeth White, Roger Strukhoff, Liz McMillan

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What's new in Revolution R Enterprise 6.1

We're pleased to announce that the latest update to Revolution R Enterprise is available today! Existing subscribers will soon receive an email with update instructions, and the free academic distribution will be updated later today. Version 6.1 adds a frequently-requested big-data statistical modeling algorithm, adds new connectivity option for Hadoop, improves performance, and provides new security and installation options for IT. Here's a summary of the new features: Decision Trees for Big Data. The new “rxDTree” function is a powerful tool for fitting classification and regression trees, which are among the most frequently used algorithms for data analysis and data mining. The implementation provided in Revolution Analytics’ RevoScaleR package is parallelized, scalable, distributable and designed with big data in mind. Revolution R Enterprise continues to offer a wide range of other big-data analysis algorithms, including summary statistics, crosstabs, regression, generalized linear models and K-means clustering.  Improved performance for ‘Big Data’ files. RevoScaleR’s ‘XDF’ file format provides fast access to big data. With new compression technology the size of XDF files can be reduced, allowing for higher-performance analytics throughput and faster transfers into clusters or cloud processing systems. Improved Linux installer. The installation process on Linux servers has been streamlined to meet stringent IT requirements, especially for non-root installs. SiteMinder single-sign for applications: Authorized users of applications built on Revolution R Enterprise deployed via the RevoDeployR Web Services API may authenticate using CA SiteMinder®. Analyze data from Hadoop Distributed File System (HDFS). With more and more data stored in Hadoop, this new option lets data scientists read data from HDFS and apply big-data statistical models from Revolution R Enterprise. I'm especially excited about this last feature, which makes it possible to feed structured data files in Hadoop directly to the big-data statistical algorithms in the RevoScaleR package, as demonstrated in the video below. It pairs well with the RHadoop project: if you don't have structured data in Hadoop already, use the rmr package and map-reduce to create a structured data file in HDFS, and then analyze it with RevoScaleR.   You can find more details about the features in Revolution R Enterprise in this table.

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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