We’re pleased to announce that Oracle R Enterprise (ORE) 1.4 is now available for download on all supported platforms. In addition to numerous bug fixes, ORE 1.4 introduces an enhanced high performance computing infrastructure, new and enhanced parallel distributed predictive algorithms for both scalability and performance, added support for production deployment, and compatibility with the latest R versions. These updates enable IT administrators to easily migrate the ORE database schema to speed production deployment, and statisticians and analysts have access to a larger set of analytics techniques for more powerful predictive models.
Here are the highlights for the new and upgraded features in ORE 1.4:
Upgraded R version compatibility
ORE 1.4 is certified with R-3.0.1 - both open source R and Oracle R Distribution. See the server support matrix for the complete list of supported R versions. R-3.0.1 brings improved performance and big-vector support to R, and compatibility with more than 5000 community-contributed R packages.
High Performance Computing Enhancements
Ability to specify degree of parallelism (DOP) for parallel-enabled functions (ore.groupApply, ore.rowApply, and ore.indexApply)
An additional global option, ore.parallel, to set the number of parallel threads used in embedded R execution
Data Transformations and Analytics
ore.neural now provides a highly flexible network architecture with a wide range of activation functions, supporting 1000s of formula-derived columns, in addition to being a parallel and distributed implementation capable of supporting billion row data sets
ore.glm now also prevents selection of less optimal coefficient methods with parallel distributed in-database execution
Support for weights in regression models
New ore.esm enables time series analysis, supporting both simple and double exponential smoothing for scalable in-database execution
Execute standard R functions for Principal Component Analysis (princomp), ANOVA (anova), and factor analysis (factanal) on database data
Oracle Data Mining Model Algorithm Functions
Newly exposed in-database Oracle Data Mining algorithms:
ore.odmAssocRules function for building Oracle Data Mining association models using the apriori algorithm
ore.odmNMF function for building Oracle Data Mining feature extraction models using the Non-Negative Matrix Factorization (NMF) algorithm
ore.odmOC function for building Oracle Data Mining clustering models using the Orthogonal Partitioning Cluster (O-Cluster) algorithm
New migration utility eases production deployment from development environments
"Snapshotting" of production environments for debugging in test systems
For a complete list of new features, see the Oracle R Enterprise User's Guide. To learn more about Oracle R Enterprise, check out the white paper entitled, "Bringing R to the Enterprise - A Familiar R Environment with Enterprise-Caliber Performance, Scalability, and Security.", visit Oracle R Enterprise on Oracle's Technology Network, or review the variety of use cases on the Oracle R blog.