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Process of Configuring Siebel Analytics for Real-Time Deployment

The predictive model must be connected to Siebel Analytics as an XML data source for real-time deployment. Whenever you add a new predictive model as an XML data source, you must restart the Analytics server to reload the model into memory. In a real-time deployment scenario, performance is the most important consideration. You will have to precalculate and load Analytics measures in extension columns of OLTP tables in your operational application that serve as inputs to a predictive model. This helps meet the highest availability and performance requirements. Consider Siebel Analytics cache seeding procedures to optimize the performance of your configuration. See the Siebel Analytics Server Administration Guide for details.

To configure Siebel Analytics metadata to invoke a predictive model in real time

1. Create a new data source in the physical layer and select XML Server in the data source definition of the source.
2. Create a new connection pool and select Default (XML) as the call interface. Name the connection pool Web Services Gateway.
3. Create a new table for each model that needs to be invoked. For a given table, create two sets of columns:
   • One set of columns will include all the input variables that the model expects.
   • The other set of columns will include two outputs, Score and Probability, which every predictive model provides.

     For the first set of columns, make sure the names and data types of the columns match exactly with the names of the input variables as defined in your data mining model. (View the exact model definition in Data Mining Workbench or Siebel Miner.) The Probability column data type must be FLOAT. The Score column data type depends on the model:

   • A discrete model requires a Score column of data type VARCHAR.
   • A continuous model requires a Score column of data type FLOAT.
4. Right-click on the table and select Properties. In the XML tab, select the DLL radio button and put the following string in the Search Utility field: <Siebel Analytics Binary Directory>/SADataMining.dll "<Full Path name >/<File name of XML Model in PMML format>". For example, the search utility in the wireless service provider scenario might read as follows: c:\SiebelAnalytics\bin\SADataMining.dll "c:\churnmodel.xml".
5. Create a new business model in the Business Model layer for each data mining model. Then select the new physical table in Physical layer and drag the object onto the new business model. This will create two new logical tables. Rename the logical tables to <Model>Dim and <Model>Fact, respectively.
6. Create a one-to-many Complex Join from the <Model>Dim to the <Model>Fact on any one of the available columns. This join is only necessary to enable the exposure of the model in the presentation layer.
7. Create a new presentation layer catalog and drag the input variable columns from the <Model>Dim logical table and the score and probability columns from the <Model>Fact logical table into it.