mining of data involving one-to-many relationships (e.g. a star schema). The full functionality of SQL can be used when preparing data for data mining, including dates and spatial data. Oracle Data Mining distinguishes numerical, categorical, and unstructured (text) attributes. The product also provides utilities for data preparation steps prior to model building such as outlier treatment, discretization, normalization and binning (sorting in general speak) Graphical user interface: Oracle Data Miner Users can access Oracle Data Mining through Oracle Data Miner, a GUI client application that provides access to the data mining functions and structured templates (called Mining Activities) that automatically prescribe the order of operations, perform required data transformations, and set model parameters. The user interface also allows the automated generation of Java and/or SQL code associated with the data-mining activities. The Java Code Generator is an extension to Oracle JDeveloper. An independent interface also exists: the Spreadsheet Add-In for Predictive Analytics which enables access to the Oracle Data Mining Predictive Analytics PL/SQL package from Microsoft Excel. From version 11.2 of the Oracle database, Oracle Data Miner integrates with Oracle SQL Developer. PL/SQL and Java interfaces Oracle Data Mining provides a native PL/SQL package (DBMS_DATA_MINING) to create, destroy, describe, apply, test, export and import models. The code below illustrates a typical call to build a classification model: BEGIN DBMS_DATA_MINING.CREATE_MODEL ( model_name => 'credit_risk_model', function => DBMS_DATA_MINING.classification, data_table_name => 'credit_card_data', case_id_column_name => 'customer_id', target_column_name => 'credit_risk', settings_table_name => 'credit_risk_model_settings'); END; where 'credit_risk_model' is the model name, built for the express purpose of classifying future customers' 'credit_risk', based on training data provided in the table 'credit_card_data', each case distinguished by a unique 'customer_id', with the rest of the model parameters specified through the table 'credit_risk_model_settings'. Oracle Data Mining also supports a Java API consistent with the Java Data Mining (JDM) standard for data mining (JSR-73) for enabling integration with web and Java EE applications and to facilitate portability across platforms. SQL scoring functions As of release 10gR2, Oracle Data Mining contains built-in SQL functions for scoring data mining models. These single-row functions support classification, regression, anomaly detection, clustering, and feature extraction. The code below illustrates a typical usage of a classification model: SELECT customer_name FROM credit_card_data WHERE PREDICTION (credit_risk_model USING *) = 'LOW' AND customer_value = 'HIGH'; PMML In Release 11gR2 (11.2.0.2), ODM supports the import of externally created PMML for some of the data mining models. PMML is an XML-based standard