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 220.127.116.11.2 for Visual Studio 2010 and .NET Framework 4
With ODAC 18.104.22.168.2, 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 http://www.oracle.com/technetwork/articles/datawarehouse/saternos-r-161569.html
Data Mining Using the RDOM Package
By Casimir Saternos
Open R and enter the following command.
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).
And say making a Model in Oracle and R-
> numrows <- length(orange_data[,1])
> orange_data.rows <- length(orange_data[,1])
> orange_data.id <- matrix(seq(1, orange_data.rows), nrow=orange_data.rows, ncol=1, dimnames= list(NULL, c(“CASE_ID”)))
> orange_data <- cbind(orange_data.id, 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.
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_dbms_table(DB, "orange_test")
> RODM_drop_dbms_table(DB, "orange_data")
See the full article at http://www.oracle.com/technetwork/articles/datawarehouse/saternos-r-161569.html