pmml

Predictive Analytics Deployment to Mainframe or Hadoop – Upcoming Webinar

Learn how to achieve a standards-based, efficient approach to analytics deployment from open source like R and commercial analytics products to a wide range of platforms including mainframes like IBM zSystems and new data infrastructure like Hadoop. Thursday, March 3, 2016 11:00:00 AM PST – 12:00:00 PM PST Register here. The big challenge for analytics-driven […]

Decision Management Market Highlights Q1 2014

We published our Decision Management Market Highlights Q1 2014 today. Here is an excerpt: There have been interesting developments in the Decision Management market this quarter. In particular the focus is clearly shifting from Decision Management simply as a better way to manage business rules to one where analytics, especially predictive analytics, is central. This quarter […]

Standards in Predictive Analytics: A White Paper

To wrap up the series I have been writing on standards in predictive analytics, here’s the report I have been working on. This report discusses each of the topics in the series – R, Hadoop and PMML – in more detail and pulls it all together in a single paper. You can get the Standards in Predictive Analytics  paper here.

Thanks to our sponsors for this research, Revolution Analytics, Zementis and the Data Mining Group.

Standards in Predictive Analytics: R

The third post in my series on standards in Predictive Analytics is on R, a hot topic in analytic circles these days. R is fundamentally an interpreted language for statistical computing and for the graphical display of results associated with these statistics. Highly extensible, it is available as free and open source software. The core environment provides standard programming capabilities as well as specialized capabilities for data ingestion, data handling, mathematical analysis and visualization. The core contains support for linear and generalized linear models, nonlinear regression, time series, clustering, smoothing and more. The language has been in development and use since 1997 with the 1.0 release coming in 2000. The core is now at release 3.0. New capabilities can be added by creating packages typically written in the R language itself. Over 5,000 packages have been added through the open source community.

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Build Your Big Data Analytics Capability with Decision Management

The old way: Big data is a hot topic. Big Data was coined as a term by Gartner to mean data that has Volume (more data), Variety (of many types) and Velocity (that arrives more rapidly). Value is extracted from big data using advanced analytics such as data mining and predictive analytics. This data can […]