Recent Posts by Sherry LaMonica

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...

Oracle R Distribution, Oracle's distribution of Open Source R, improves performance by dynamically linking to optimized, multi-threaded BLAS libraries. Unlike open source R, Oracle R Distribution uses all available cores and processors when dynamically linked against optimized BLAS, resulting in increased performance. Thus, the more cores available to Oracle R Distribution, the higher performance for many operations. How is this possible?  Standard R's internal BLAS library wa...

Oracle R Enterprise uses external procedures in Oracle Database to support embedded R execution. An external procedure, or extproc, is a procedure stored in a shared library that is called to perform special-purpose processing.  When ORE invokes an external procedure, Oracle Database starts an extproc agent and passes instructions to the agent for executing the procedure. The agent loads an ORE shared library or DLL, runs the external procedure in the database, and passes...

We are excited to introduce support for Oracle R Distribution 3.0.1 on Windows 64-bit versions. Previous releases are available on Solaris x86, Solaris SPARC, AIX and Linux 64-bit platforms. Oracle R Distribution (ORD) continues to support these platforms and now expands support to Windows 64-bit platforms. ORD is Oracle's free distribution of the open source R environment that adds support for dynamically loading the Intel Math Kernel Library (MKL) installed onyour system....

Oracle R Enterprise is commonly used to apply parallel resources to R computations in Oracle's Exadata Database Machine, To take advantage of Exadata's massively parallel grid infrastructure, Oracle R Distribution and the Oracle R Enterprise server components must be installed on each node. We've now streamlined the installation of Oracle R on Exadata, allowing users to get up and running quickly. This is where Exadata's distributed command line interface utility (DCLI) comes...

Oracle R Enterprise graphics are returned as raster, or bitmap graphics. Raster images consist of tiny squares of color information referred to as pixels that form points of color to create a complete image. Plots that contain raster images render quickly in R and create small, high-quality exported image files in a wide variety of formats. However, it is a known issue that the rendering of raster images can be problematic whencreating graphics using a Remote...

We're pleased to announce that the Oracle R Distribution 3.0.1 Linux RPMs are now available on Oracle's public yum. R-3.0.1, code-named "Good Sport", is the second release in the R-3.0.x series. This new series in R doesn't announce new features, but indicates that the code base has developed to a new level of maturity. However, there are some significant improvements in the 3.0 series worth mentioning.  R-3.0.0 introduces the use of large vectors in R, and eliminates some...

The most common Oracle R Enterprise configuration is to connect directly to a database schema that contains tables you wish to analyze. However, users may occasionally need to access tables that exist in other schemas. Oracle R Enterprise allows several options when accessing tables from another schema is desired. Database tables and views are currently supported, and these form the basis for our recommendations. Named Schema Access If you have SELECT TABLE or SELECT ANY TABLE pri...

Oracle R Connector for Hadoop (ORCH), a collection of R packages that enables Big Data analytics using HDFS, Hive, and Oracle Database from a local R environment, continues to make advancements. ORCH 2.1.0 is now available, providing a flexible framework while remarkably improving performance and adding new analytics based on the ORCH framework.Previous releases enabled users to write MapReduce tasks in the R language and run them in HDFS. The API was then expanded to...