Updating your ODM (11g R2) model in production

Posted on December 20, 2011

In my previous blog posts on creating an ODM model, I gave the details of how you can do this using the ODM PL/SQL API.

But at some point you will have a fairly stable environment. What this means is that you will know what type of algorithm and its corresponding settings work best for for your data.

At this point you should be able to re-create your ODM model in the production database. The frequency of doing this update is dependent on number of new cases that you have. So you need to update your ODM model could be daily, weekly, monthly, etc.

To update your model you will need to:

– Creating a settings table for your model
– Create a new ODM model
– Rename your new ODM model to the production name

The following examples are based on the example data, model names, etc that I’ve used in my previous post.

Creating a Settings Table

The first step is to create a setting table for your algorithm. This will contain all the parameter settings needed to create the new model. You will have worked out these setting from your previous attempts at creating your models and you will know what parameters and their values work best.

— Create the settings table
CREATE TABLE decision_tree_model_settings (
    setting_name VARCHAR2(30),
    setting_value VARCHAR2(30));

— Populate the settings table
— Specify DT. By default, Naive Bayes is used for classification.
— Specify ADP. By default, ADP is not used.
BEGIN
    INSERT INTO decision_tree_model_settings (setting_name, setting_value)
    VALUES (dbms_data_mining.algo_name,       
           dbms_data_mining.algo_decision_tree);
   
    INSERT INTO decision_tree_model_settings (setting_name, setting_value)
    VALUES (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
  
    COMMIT;
END;

Create a new ODM Model

We will need to use the DBMS_DATA_MINING.CREATE_MODEL procedure. In our example we will want to create a Decision Tree based on our sample data, which contains the previously generated cases and the new cases since the last model rebuild.

BEGIN
    DBMS_DATA_MINING.CREATE_MODEL(
        model_name          => ‘Decision_Tree_Method2′,
        mining_function     => dbms_data_mining.classification,
        data_table_name     => ‘mining_data_build_v’,
        case_id_column_name => ‘cust_id’,
        target_column_name  => ‘affinity_card’,
        settings_table_name => ‘decision_tree_model_settings’);
END;

Rename your ODM model to production name

The model we have create created above is not the name that is used in our production software. So we will need to rename it to our production name.

But we need to be careful about when we do this. If you drop a model or rename a model when it is being used then you can end up with indeterminate results.

What I suggest you do, is to pick a time of the day when your production software is not doing any data mining. You should drop the existing mode (or rename it) and the to rename the new model to the production model name.

DBMS_DATA_MINING.DROP_MODEL(‘CLAS_DECISION_TREE‘);

and then

DBMS_DATA_MINING.RENAME_MODEL(‘Decision_Tree_Method2’, ‘CLAS_DECISION_TREE’);