Oracle R Enterprise 1.3 released

< size="2" face="arial,helvetica,sans-serif">We’re pleased to announce the latest release of Oracle R Enterprise, now available for download. Oracle R Enterprise 1.3 features new predictive analytics interfaces for in-database model building and scoring, support for in-database sampling and partitioning techniques, and transparent support for Oracle DATE and TIMESTAMP data types to facilitate data preparation for time series analysis and forecasting. Oracle R Enterprise further enables transparent access to Oracle Database tables from R by enabling integer indexing and ensuring consistent ordering between data in R data frames and Oracle Database tables. The latest release also includes improved programming efficiencies and performance improvements.< >
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< size="2" face="arial,helvetica,sans-serif">The key additions in version 1.3 include: < >

< size="2" face="arial,helvetica,sans-serif">Enhanced Model Scoring: The new package OREpredict enables in-database scoring of R-generated models. Supported models include linear regression (lm) and generalized linear models (glm), hierarchical clustering (hclust), k-means clustering (kmeans), multinomial log-linear models (multinom), neural networks (nnet), and recursive partitioning and regression trees (rpart). < >

< size="2" face="arial,helvetica,sans-serif">Oracle Data Mining Support: The new package OREdm provides an R interface for in-database Oracle Data Mining predictive analytics and data mining algorithms. Supported models include attribute importance, decision trees, generalized linear models, k-means clustering, naive bayes and support vector machines. < >

< size="2" face="arial,helvetica,sans-serif">Neural Network Modeling: A new feed-forward neural network algorithm with in-database execution.
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< size="2" face="arial,helvetica,sans-serif">Date and Time Support: Support for Oracle DATE and TIMESTAMP data types and analytic capabilities that allow date arithmetic, aggregations, percentile calculations and moving window calculations for in-database execution. < >

< size="2" face="arial,helvetica,sans-serif">Sampling Methods: Enables in-database sampling and partitioning techniques for use against database-resident data. Techniques include simple random sampling, systematic sampling, stratified sampling, cluster sampling, quota sampling and accidental sampling. < >

< size="2" face="arial,helvetica,sans-serif">Object Persistence: New capabilities for saving and restoring R objects in an Oracle Database “datastore”, which supports not only in-database persistence of R objects, but the ability to easily pass any type of R objects to embedded R execution functions. < >

< size="2" face="arial,helvetica,sans-serif">Database Auto-Connection:  New functionality for automatically establishing database connectivity using contextual credentials inside embedded R scripts, < >< size="2" face="arial,helvetica,sans-serif">allowing convenient and secure connections to Oracle Database.< >

< size="2" face="arial,helvetica,sans-serif">When used in conjunction with Oracle Exadata Database Machine and Oracle Big Data Appliance, Oracle R Enterprise and Oracle R Connector for Hadoop provide a full set of engineered systems to access and analyze big data. < >< size="2" face="arial,helvetica,sans-serif">With Oracle R Enterprise, IT organizations can rapidly deploy advanced analytical solutions, while providing the knowledge to act on critical decisions. < >

< size="2" face="arial,helvetica,sans-serif">Stay tuned for blogs about the new ORE 1.3 features in upcoming posts. You can find more details about the features in Oracle R Enterprise 1.3 in our New Features Guide and Reference Manual.< >