Oracle Open World/ RODM package

From the press release, here comes Oracle Open World. They really have an excellent rock concert in that as well.

.NET and Windows @ Oracle Develop and Oracle OpenWorld 2010

Oracle Develop will again feature a .NET track for Oracle developers. Oracle Develop is suited for all levels of .NET developers, from beginner to advanced. It covers introductory Oracle .NET material, new features, deep dive application tuning, and includes three hours of hands-on labs apply what you learned from the sessions.

To register, go to Oracle Develop registration site.

Oracle OpenWorld will include several sessions on using the Oracle Database on Windows and .NET.

Session schedules and locations for Windows and .NET sessions at Oracle Develop and OpenWorld are now available.

Download: 32-bit ODAC for Visual Studio 2010 and .NET Framework 4

With ODAC, developers can connect to Oracle Database versions 9.2 and higher from Visual Studio 2010 and .NET Framework 4. ODAC components support the full framework, as well as the new .NET Framework Client Profile.

Statement of Direction: Oracle Database and Microsoft Entity Framework

Learn about Oracle’s beta and production plans to support Microsoft Entity Framework with Oracle Database.

Also see


Data Mining Using the RDOM Package

By Casimir Saternos

Some excerpts-

Open R and enter the following command.

> library(RODM)

This command loads the RODM library and as well the dependent RODBC package. The next step is to make a database connection.

> DB <- RODM_open_dbms_connection(dsn="orcl", uid="dm", pwd="dm")

Subsequent commands use the DB object (an instance of the RODBC class) to connect to the database. The DNS specified in the command is the name you used earlier for the Data Source Name during the ODBC connection configuration. You can view the actual R code being executed by the command by simply typing the function name (without parentheses).

> RODM_open_dbms_connection

And say making a Model in Oracle and R-

> numrows <- length(orange_data[,1])
> orange_data.rows <- length(orange_data[,1])
> <- matrix(seq(1, orange_data.rows),  nrow=orange_data.rows, ncol=1, dimnames= list(NULL, c(“CASE_ID”)))
> orange_data <- cbind(, orange_data)

This adjustment to the data frame then needs to be propagated to the database. You can confirm the change using the sqlColumns function, as listed earlier.

> RODM_create_dbms_table(DB, "orange_data")
> sqlColumns(DB, 'orange_data')$COLUMN_NAME

> glm <- RODM_create_glm_model(
database = DB,
data_table_name = “orange_data”,
case_id_column_name = “CASE_ID”,
target_column_name = “circumference”,
model_name = “GLM_MODEL”,
mining_function = “regression”)

Information about this model can then be obtained by analyzing value returned from the model and stored in the variable named glm.

> glm$model.model_settings
> glm$glm.globals
> $glm.coefficients

Once you have a model, you can apply the model to a new set of data. To begin, create or retrieve sample data in the same format as the training data.

> query<-('select 999 case_id, 1 tree, 120 age, 
32 circumference from dual')

> orange_test<-sqlQuery(DB, query)
> RODM_create_dbms_table(DB, "orange_test")
Finally, the model can be applied to the new data set and the results analyzed.

results <- RODM_apply_model(database = DB, 
data_table_name = "orange_test",
model_name = "GLM_MODEL",
supplemental_cols = "circumference")

When your session is complete, you can clean up objects that were created (if you like) and you should close the database connection:

> RODM_drop_model(database=DB,'GLM_MODEL')
> RODM_drop_dbms_table(DB, "orange_test")
> RODM_drop_dbms_table(DB, "orange_data")
> RODM_close_dbms_connection(DB)

See the full article at

R Oracle Data Mining

Here is a new package called R ODM and it is an interface to do Data Mining via Oracle Tables through R. You can read more here and here . Also there is a contest for creative use of R and ODM.

R Interface to Oracle Data Mining

The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies.

R-ODM is especially useful for:

  • Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application
  • Scripting of “production” data mining methodologies
  • Customizing graphics of ODM data mining results (examples: classificationregressionanomaly detection)

The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc.

R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment’s Comprehensive R Archive Network ( CRAN). For information about the R environment, R packages and CRAN, see


Present and win an Apple iPod Touch!
The BI, Warehousing and Analytics (BIWA) SIG is giving an Apple iPOD Touch to the best new presenter. Be part of the TechCast series and get a chance to win!

Consider highlighting a creative use of R and ODM.

BIWA invites all Oracle professionals (experts, end users, managers, DBAs, developers, data analysts, ISVs, partners, etc.) to submit abstracts for 45 minute technical webcasts to our Oracle BIWA (IOUG SIG) Community in our Wednesday TechCast series. Note that the contest is limited to new presenters to encourage fresh participation by the BIWA community.

Also an interview with Oracle Data Mining head, Charlie Berger