This section summarizes the new features of Oracle Data Mining that pertain to installation, administration, and upgrade.
See Also:
"What's New" in Oracle Data Mining Concepts for a complete list of new and deprecated features in Oracle Data Mining 11g Release 1 (11.1).
Chapter 3 for information on upgrading and downgrading a database
This section contains the following topics:
Oracle Data Mining 11g Release 1 (11.1) has a tight integration with Oracle Database. Data Mining metadata and PL/SQL packages have been migrated from DMSYS to SYS. The DMSYS schema no longer exists in Oracle Database 11g Release 1 (11.1) fresh installations.
New catalog views for Data Mining are introduced in 11g Release 1 (11.1):
ALL/DBA/USER_MINING_MODELS provides information about mining models
ALL/DBA/USER_MINING_MODEL_ATTRIBUTES provides information about data columns used to build a mining model
ALL/DBA/USER_MINING_MODEL_SETTINGS provides information about configuration settings for mining models
The ALL/DBA/USER_OBJECTS catalog view now identifies mining models.
Security features of Oracle Data Mining are significantly enhanced in 11g Release 1 (11.1). Improved security for data mining has several aspects:
Previously, Oracle Data Mining used DMSYS as its own repository schema. This necessitated the granting of advanced database privileges to DMSYS, a non-system account. In 11g Release 1 (11.1), the Oracle Data Mining metadata repository is in SYS, where it is accessible only to the system DBA.
New system and object privileges for mining model objects are introduced in 11g Release 1 (11.1).
The SQL auditing system can be used to track operations on mining model objects.
Note:
The privilegeCREATE MINING MODEL is required for creating models in 11g. This privilege should be added to any accounts being upgraded to 11g.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 had to ensure 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. Only nested data can be considered sparse. Missing values in simple numeric or character columns are considered missing at random.
The following features are not supported 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)
The following features are deprecated in 11g Release 1 (11.1):
Adaptive Bayes Network classification algorithm
DM_USER_MODELS view is replaced by data dictionary views
Several PL/SQL procedures have been deprecated.
GET_DEFAULT_SETTINGS
Replaced with data dictionary views: USER/ALL/DBA_MINING_MODEL_SETTINGS
GET_MODEL_SETTINGS
Replaced with data dictionary views: USER/ALL/DBA_MINING_MODEL_SETTINGS
GET_MODEL_SIGNATURE
Replaced with data dictionary views: USER/ALL/DBA_MINING_MODEL_ATTRIBUTES
Note:
Oracle recommends that you do not use deprecated features in new applications. Support for deprecated features is for backward compatibility only.