Preface

This manual explains how to install Oracle Data Mining and prepare the database for data mining activities.

The preface contains these topics:

• Audience

• Documentation Accessibility

• Related Documentation

• Where to Find Database Tuning Documentation

• Conventions

Audience

This guide is intended primarily for users who want to install, configure, and use Oracle Data Mining on Microsoft Windows for data mining exercises and demonstrations.

Documentation Accessibility

For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup?ctx=acc&id=docacc.

Access to Oracle Support

Oracle customers have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired.

Related Documentation

The documentation set for Oracle Data Mining is part of the Oracle Database 11g Release 2 (11.2) Online Documentation Library. The Oracle Data Mining documentation set consists of the following documents:

• Oracle Data Mining Concepts

• Oracle Data Mining Application Developer's Guide

• Oracle Data Mining Java API Reference (javadoc)

For detailed information about the Oracle Data Mining PL/SQL interface, see Oracle Database PL/SQL Packages and Types Reference .

For detailed information about the SQL data mining functions, see Oracle Database SQL Language Reference.

For an introduction to Oracle application development, see Oracle Database 2 Day Developer's Guide.

For an introduction to Oracle application development in Java, see Oracle Database Java Developer's Guide

Where to Find Database Tuning Documentation

The Oracle Data Mining documentation set does not address the topic of database tuning for data mining. The Oracle Database Performance Tuning Guide is the best source of information for tuning Oracle Database (including when the goal is to invoke mining build/score operations).

The memory tuning parameters PGA_AGGREGATE_TARGET and SGA_TARGET can easily have the most impact on data mining. The correct setting of PGA is very important for model building routines, as well as complex queries and batch scoring. From a Data Mining perspective, the SGA is generally less of a concern, except that real-time scoring is enabled by having the model loaded into the shared cursor in the SGA, so this still should be sized accordingly.

In addition, if parallel execution is desired, then there are INIT.ORA parameters that control this functionality.

Conventions

The following text conventions are used in this document: