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Oracle R Enterprise 1.5 Released

Mark Hornick
Senior Director, Data Science and Machine Learning
We’re pleased to announce that Oracle R Enterprise (ORE) 1.5 is now available for download on all supported platforms with Oracle R Distribution 3.2.0 / R-3.2.0. ORE 1.5 introduces parallel distributed implementations of Random Forest, Singular Value Decomposition (SVD), and Principal Component Analysis (PCA) that operate on ore.frame objects. Performance enhancements are included for ore.summary summary statistics.
In addition, ORE 1.5 enhances embedded R execution with CLOB/BLOB data column support to enable larger text and non-character data to be transferred between Oracle Database and R. CLOB/BLOB support is also enabled for functions ore.push and ore.pull. The ORE R Script Repository now supports finer grained R script access control across schemas. Similarly, the ORE Datastore enables finer grained R object access control across schemas. For ore.groupApply in embedded R execution, ORE 1.5 now supports multi-column partitioning of data using the INDEX argument. Multiple bug fixes are also included in this release.
Here are the highlights for the new and upgraded features in ORE 1.5:
Upgraded R version compatibility
ORE 1.5 is certified with R-3.2.0 - both open source R and Oracle R Distribution. See the server support matrix for the complete list of supported R versions. R-3.2.0 brings improved performance and big in-memory data objects, and compatibility with more than 7000 community-contributed R packages.
For supporting packages, ORE 1.5 includes one new package, randomForest, with upgrades to other packages:
arules 1.1-9
cairo 1.5-8
DBI 0.3-1
png 0.1-7
ROracle 1.2-1
statmod 1.4.21
randomForest 4.6-10
Parallel and distributed algorithms
While the Random Forest algorithm provides high accuracy, performance and scalability can be issues for large data sets. ORE 1.5 introduces Random Forest in Oracle R Distribution with two enhancements: first, a revision to reduce memory requirements of the open source randomForest algorithm; and second, the function ore.randomForest that executes in parallel for model building and scoring while using the underlying randomForest function either from Oracle R Distribution or R’s randomForest package 4.6-10. ore.randomForest uses ore.frame objects allowing data to remain in the database server.
The functions svd and prcomp have been overloaded to execute in parallel and accept ore.frame objects. Users now get in-database execution of this functionality to improve scalability and performance – no data movement.
Performance enhancements
ore.summary performance enhancements supports executions that are 30x faster than previous releases.
Capability enhancements
ore.grant and ore.revoke functions enable users to grant other users read access to their R scripts in the R script repository and individual datastores.
The database data types CLOB and BLOB are now supported for embedded R execution invocations input and output, as well as for the functions ore.pull and ore.push.
For embedded R execution ore.groupApply, users can now specify multiple columns for automatically partitioning data via the INDEX argument.
For a complete list of new features, see the Oracle R Enterprise User's Guide. To learn more about Oracle R Enterprise, visit Oracle R Enterprise on Oracle's Technology Network, or review the variety of use cases on the Oracle R Technologies blog.

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