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Revolution Analytics Advances Big Data Predictive Analytics for Enterprise-class Deployments

Dedicated Teradata Connection and New Stepwise Regression for Big Data Linear Models Reduces Time-to-Insight to Drive Bigger Return on Big Data Investment

Revolution Analytics, the leading commercial provider of software, services and support for the open source R project, today announced a significant upgrade to its commercial-grade analytics software built upon the world's most powerful open source R statistics language for R-based enterprise-class data analytics. Available now, Revolution R Enterprise 6.2 introduces several new advances in high-performance predictive analytics. Customers now have the ability to quickly and easily conduct stepwise regression for big data linear models and perform predictive analytics on data that is stored in Teradata(r) databases--two key features among others that yield significant time savings when working with large datasets.

"Enterprises today are seeking to take their analytics capabilities to a new level through predictive modeling that helps uncover hidden patterns and insight from big data," said Chris Twogood, vice president, product and services marketing, Teradata Labs. "The Revolution Analytics high-speed data connector into the Teradata Unified Data Architecture(tm) enables our joint customers to leverage the power of Revolution R Enterprise for advanced predictive analytics to glean more insight from data and achieve a greater return on their big data investment."

Revolution R Enterprise 6.2 includes new, high-demand capabilities that companies require to extend predictive analytics across an organization, while also optimizing the performance of sophisticated analytic models on big data sets. Companies with predictive models in production systems routinely build thousands of models every week, and at that scale of activity, traditional model-fitting techniques are simply too slow. Starting with a feature set of hundreds of candidate variables, Revolution R Enterprise users can now use feature selection techniques to reduce the number of variables, and then run stepwise regression to finalize the model for automated scoring.

Revolution R Enterprise 6.2 includes the following new capabilities:

  • High Speed Teradata Data Connection.Teradata is the first database for which Revolution R Enterprise has a dedicated parallel connection. Customers can seamlessly extract data from a Teradata database using the Teradata Parallel Transporter and write it to a high performance XDF format file, or simply analyze the data directly. The increased speed with which Revolution R Enterprise users can move the data saves a significant amount of time when working with a large dataset.
  • Stepwise Regression for 'Big Data' Linear Models. This feature allows users to automate the process of building a model by using a rigorous method to test and select from among a range of variables that are available for use in the model. The result is a dramatic reduction in the total time needed to fit a model.
  • Parallel Random Number Generation. This new functionality provides an R interface to the parallel random number generators supplied with the Intel® Math Kernel Library. This provides high quality parallel random numbers for use in distributed computations.
  • Updated RevoDeployR Web Deployment Framework. New APIs for script management and new priority scheduling features improve the management and operation of deployed R routines. Updated Java, JavaScript and .NET client libraries provide additional support for application developers, making it easier to integrate on-demand R-based computations with desktop, web-based and mobile apps.

This update also includes the following improvements:

  • A faster way to import or analyze fixed format text data;
  • Improvements to the linear, logistic and generalized linear modeling functions for enhanced control over interaction terms model fitting;
  • Sort, merge, and split enhancements that now by default make better use of available memory for improved performance;
  • Options to write by-group counts or summary statistics directly to a high performance XDF file for further analysis. Users have more control over the summary statistics that are reported.

The new release is based on open source R 2.15.3, the latest stable version. Revolution R Enterprise users benefit from 89 new features, 11 performance enhancements and 139 bug fixes from the open source R project.

"Revolution Analytics brings high-performance predictive analytics capabilities to organizations and R developers grappling with large datasets," said David Rich, Revolution Analytics CEO. "Revolution R Enterprise 6.2 reflects our commitment to listening to customer needs and expanding support for our partner network. Our new release reinforces user experience and productivity with several new time-saving features for big data analysis work that help achieve fast, actionable business insights."

Revolution R Enterprise 6.2 is available now. For more information, please visit, or join a webinar at 11:00 a.m. EST on May 1 introducing the new features. Register for the webinar here.

About Revolution Analytics

Revolution Analytics is the leading commercial provider of software and services based on the open source R project for statistical computing. The company brings high performance, productivity and enterprise readiness to R, the most powerful statistics language in the world. The company's flagship Revolution R Enterprise product is designed to meet the production needs of large organizations in industries such as finance, life sciences, retail, manufacturing and media. Used by over two million analysts in academia and at cutting-edge companies such as Google, Bank of America and Acxiom, R has emerged as the standard of innovation in statistical analysis. Revolution Analytics is committed to fostering the continued growth of the R community through sponsorship of the community site, funding worldwide R user groups and offering free licenses of Revolution R Enterprise to everyone in academia.

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