What's New in Oracle Data Mining?

This section describes new features in Oracle Data Mining. It includes the following sections:

Oracle Database 11g Release 2 (11.2.0.3) Oracle Data Mining

Oracle Database 11g Release 2 (11.2.0.2) Oracle Data Mining

In Oracle Data Mining 11g Release 2 (11.2.0.2), you can import externally-created GLM models when they are presented as valid PMML documents. PMML is an XML-based standard for representing data mining models.

The IMPORT_MODEL procedure in the DBMS_DATA_MINING package is overloaded with syntax that supports PMML import. When invoked with this syntax, the IMPORT_MODEL procedure will accept a PMML document and translate the information into an Oracle Data Mining model. This includes creating and populating model tables as well as SYS model metadata.

External models imported in this way will be automatically enabled for Exadata scoring offload.

See Also:

Oracle Database PL/SQL Packages and Types Reference for details about DBMS_DATA_MINING.IMPORT_MODEL

"Data Mining in Oracle Exadata"

Oracle Database 11g Release 1 (11.1) Oracle Data Mining

  • Mining Model schema objects

    In Oracle 11g, Data Mining models are implemented as data dictionary objects in the SYS schema. A set of new data dictionary views present mining models and their properties. New system and object privileges control access to mining model objects.

    In previous releases, Data Mining models were implemented as a collection of tables and metadata within the DMSYS schema. In Oracle 11g, the DMSYS schema no longer exists.

    See Also:

  • Automatic Data Preparation (ADP)

    In most cases, data must be transformed using techniques such as binning, normalization, or missing value treatment before it can be mined. Data for build, test, and apply must undergo the exact same transformations.

    In previous releases, data transformation was the responsibility of the user. In Oracle Database 11g, the data preparation process can be automated. Algorithm-appropriate transformation instructions are embedded in the model and automatically applied to the build data and scoring data. The automatic transformations can be complemented by or replaced with user-specified transformations.

    Because they contain the instructions for their own data preparation, mining models are known as supermodels.

    See Also:

  • Scoping of Nested Data and Enhanced Handling of Sparse Data

    Oracle Data Mining supports nested data types for both categorical and numerical data. Multi-record case data must be transformed to nested columns for mining.

    In Oracle Data Mining 10gR2, nested columns were processed as top-level attributes; the user was burdened with the task of ensuring that two nested columns did not contain an attribute with the same name. In Oracle Data Mining 11g, nested attributes are scoped with the column name, which relieves the user of this burden.

    Handling of sparse data and missing values has been standardized across algorithms in Oracle Data Mining 11g. Data is sparse when a high percentage of the cells are empty but all the values are assumed to be known. This is the case in market basket data. When some cells are empty, and their values are not known, they are assumed to be missing at random. Oracle Data Mining assumes that missing data in a nested column is a sparse representation, and missing data in a non-nested column is assumed to be missing at random.

    In Oracle Data Mining 11g, Decision Tree and O-Cluster algorithms do not support nested data.

    See Also:

    Oracle Data Mining Application Developer's Guide

  • Generalized Linear Models

    A new algorithm, Generalized Linear Models, is introduced in Oracle 11g. It supports two mining functions: classification (logistic regression) and regression (linear regression).

    See Also:

    Chapter 12, "Generalized Linear Models"

  • New SQL Data Mining Function

    A new SQL Data Mining function, PREDICTION_BOUNDS, has been introduced for use with Generalized Linear Models. PREDICTION_BOUNDS returns the confidence bounds on predicted values (regression models) or predicted probabilities (classification).

    See Also:

    Oracle Data Mining Application Developer's Guide

  • Enhanced Support for Cost-Sensitive Decision Making

    Cost matrix support is significantly enhanced in Oracle 11g. A cost matrix can be added or removed from any classification model using the new procedures, DBMS_DATA_MINING.ADD_COST_MATRIX and DBMS_DATA_MINING.REMOVE_COST_MATRIX.

    The SQL Data Mining functions support new syntax for specifying an in-line cost matrix. With this new feature, cost-sensitive model results can be returned within a SQL statement even if the model does not have an associated cost matrix for scoring.

    Only Decision Tree models can be built with a cost matrix.

    See Also:

  • Features Not Available in 11g Release 1 (11.1)

    • DMSYS schema

    • Oracle Data Mining Scoring Engine

    • In Oracle 10.2, you could use Database Configuration Assistant (DBCA) to configure the Data Mining option. In Oracle 11g, you do not need to use DBCA to configure the Data Mining option.

    • Basic Local Alignment Search Tool (BLAST)

  • Features Deprecated in 11g Release 1 (11.1)

    • Adaptive Bayes Network classification algorithm (replaced with Decision Tree)

    • DM_USER_MODELS view and functions that provide information about models, model signature, and model settings (for example, GET_MODEL_SETTINGS, GET_DEFAULT_SETTINGS, and GET_MODEL_SIGNATURE) are replaced by data dictionary views. See Oracle Data Mining Application Developer's Guide.