New Features and Changes in Oracle Data Miner
Oracle Data Miner comprises these new features, and general enhancements.
New features include:
Oracle Data Mining Features
The new Oracle Data Mining features include:
- Association Model Aggregation Metrics
Oracle Data Miner supports the enhanced Association Rules algorithm and allows the user to filter items before building the Association model. - Enhancements to Algorithm Settings
Oracle Data Miner has been enhanced to support enhancements in Oracle Data Mining that includes build settings for building partition models, sampling of training data, numeric data preparation that includes shift and scale transformations, and so on. - Support for Explicit Semantic Analysis Algorithm
Oracle Data Miner 18.3 and later supports a new feature extraction algorithm called Explicit Semantic Analysis algorithm. - Enhancement to Data Mining Model Detail View
The model viewers in Oracle Data Miner have been enhanced to reflect the changes in Oracle Data Mining. - Enhancements to Filter Column Node
Oracle Data mining supports unsupervised Attribute Importance ranking. The Attribute Importance ranking of a column is generated without the need for selecting a target column. The Filter Column node has been enhanced to support unsupervised Attribute Importance ranking. - Mining Model Build Alerts
Oracle Data Miner logs alerts related to model builds in the model viewers and event logs. - R Build Model Node
Oracle Data Mining provides the feature to add R model implementations within the Oracle Data Mining framework. To support R model integration, Oracle Data Miner has been enhanced with a new R Build node with mining functions such as Classification, Regression, Clustering, and Feature Extraction. - Support for Partitioned Models
Oracle Data Miner supports the building and testing of partitioned models.
Parent topic: New Features and Changes in Oracle Data Miner
Association Model Aggregation Metrics
Oracle Data Miner supports the enhanced Association Rules algorithm and allows the user to filter items before building the Association model.
The user can set the filters in the Association Build node editor, Association model viewer, and Model Details node editor.
Related Topics
Parent topic: Oracle Data Mining Features
Enhancements to Algorithm Settings
Oracle Data Miner has been enhanced to support enhancements in Oracle Data Mining that includes build settings for building partition models, sampling of training data, numeric data preparation that includes shift and scale transformations, and so on.
Note:
These settings are available if Oracle Data Miner 18.3 is connected to Oracle Database 12.2 and later.Changes to the algorithms include:
- Changes to Decision Tree Algorithm Settings
The setting Maximum Supervised BinsCLAS_MAX_SUP_BINS
is added in the Decision Tree algorithm. - Changes to Expectation Maximization Algorithm Settings
The settingLevel of Details
replaces the current settingGather Cluster Statistics.
- Changes to Generalized Linear Models Algorithm Settings
The following changes are included in the Generalized Linear Model algorithm settings. The changes apply to both Classification models and Regression models. - Changes to k-Means Algorithm Settings
The following changes are incorporated to the k-Means algorithm settings. - Changes to Support Vector Machine Algorithm Settings
The following changes are included in the Support Vector Machine algorithm settings. The changes are applicable to both Linear and Gaussian kernel functions. - Changes to Singular Value Decomposition and Principal Components Analysis Algorithm Settings
The following changes are included in the Singular Value Decomposition and Principal Components Analysis algorithm.
Parent topic: Oracle Data Mining Features
Changes to Decision Tree Algorithm Settings
The setting Maximum Supervised Bins CLAS_MAX_SUP_BINS
is added in the Decision Tree algorithm.
Parent topic: Enhancements to Algorithm Settings
Changes to Expectation Maximization Algorithm Settings
The setting Level of Details
replaces the current setting Gather Cluster Statistics.
The underlying algorithm setting used is EMCS_CLUSTER_STATISTICS
where All=ENABLE,
and Hierarchy=DISABLE.
Some additional settings are added and some settings are deprecated.
-
Random Seed
-
Model Search
-
Remove Small Components
Settings Deprecated:
Approximate Computation ODMS_APPROXIMATE_COMPUTATION
Parent topic: Enhancements to Algorithm Settings
Changes to Generalized Linear Models Algorithm Settings
The following changes are included in the Generalized Linear Model algorithm settings. The changes apply to both Classification models and Regression models.
-
Convergence Tolerance
GLMS_CONV_TOLERANCE
-
Number of Iterations
GLMS_NUM_ITERATIONS
-
Batch Rows
GLMS_BATCH_ROWS
-
Solver
GLMS_SOLVER
-
Sparse Solver
GLMS_SPARSE_SOLVER
-
Approximate Computation
ODMS_APPROXIMATE_COMPUTATION
-
Categorical Predictor Treatment
GLMS_SELECT_BLOCK
-
Sampling for Feature Identification
GLMS_FTR_IDENTIFICATION
-
Feature Acceptance
GLMS_FTR_ACCEPTANCE
Parent topic: Enhancements to Algorithm Settings
Changes to k-Means Algorithm Settings
The following changes are incorporated to the k-Means algorithm settings.
-
Levels of Details
KMNS_DETAILS
-
Random Seeds
KMNS_RANDOM_SEEDS
-
Growth Factor
Parent topic: Enhancements to Algorithm Settings
Changes to Support Vector Machine Algorithm Settings
The following changes are included in the Support Vector Machine algorithm settings. The changes are applicable to both Linear and Gaussian kernel functions.
-
Solver
SVMS_SOLVER
-
Number of Iterations
SVMS_NUM_ITERATIONS
-
Regularizer
SVMS_REGULARIZER
-
Batch Rows
SVMS_BATCH_ROWS
-
Number of Pivots
SVMS_NUM_PIVOTS
Note:
Applies to Gaussian kernel function only.
-
Active Learning
-
Cache Size
SVMS_KERNEL_CACHE_SIZE
Note:
Applies to Gaussian kernel function only.
Parent topic: Enhancements to Algorithm Settings
Changes to Singular Value Decomposition and Principal Components Analysis Algorithm Settings
The following changes are included in the Singular Value Decomposition and Principal Components Analysis algorithm.
-
Solver
SVDS_SOLVER
-
Tolerance
SVDS_TOLERANCE
-
Random Seed
SVDS_RANDOM_SEED
-
Over sampling
SVDS_OVER_SAMPLING
-
Power Iteration
SVDS_POWER_ITERATION
-
Approximate Computation
ODMS_APPROXIMATE_COMPUTATION
Parent topic: Enhancements to Algorithm Settings
Support for Explicit Semantic Analysis Algorithm
Oracle Data Miner 18.3 and later supports a new feature extraction algorithm called Explicit Semantic Analysis algorithm.
The algorithm is supported by two new nodes, that are Explicit Feature Extraction node and Feature Compare node.
- Explicit Feature Extraction Node
The Explicit Feature Extraction node is built using the Explicit Semantic Analysis algorithm. - Feature Compare Node
The Feature Compare node enables you to perform calculations related to semantics in text data, contained in one Data Source node against another Data Source node.
Parent topic: Oracle Data Mining Features
Explicit Feature Extraction Node
The Explicit Feature Extraction node is built using the Explicit Semantic Analysis algorithm.
-
Document classification
-
Information retrieval
-
Calculations related to semantics
Parent topic: Support for Explicit Semantic Analysis Algorithm
Feature Compare Node
The Feature Compare node enables you to perform calculations related to semantics in text data, contained in one Data Source node against another Data Source node.
-
Two input data sources. The data source can be data flow of records, such as connected by a Data Source node or a single record data entered by user inside the node. In case of data entered by users, input data provider is not needed.
-
One input Feature Extraction or Explicit Feature Extraction Model, where a model can be selected for calculations related to semantics.
Parent topic: Support for Explicit Semantic Analysis Algorithm
Enhancement to Data Mining Model Detail View
The model viewers in Oracle Data Miner have been enhanced to reflect the changes in Oracle Data Mining.
Enhancements to the model viewers include the following:
-
The computed settings within the model are displayed in the Settings tab of the model viewer.
-
The new user embedded transformation dictionary view is integrated with the Inputs tab under Settings.
-
The build details data are displayed in the Summary tab under Summary
-
The Cluster model viewer detects models with partial details, and displays a message indicating so. This also applies to k-Means model viewer and Expectation Maximization model viewers.
Parent topic: Oracle Data Mining Features
Enhancements to Filter Column Node
Oracle Data mining supports unsupervised Attribute Importance ranking. The Attribute Importance ranking of a column is generated without the need for selecting a target column. The Filter Column node has been enhanced to support unsupervised Attribute Importance ranking.
Parent topic: Oracle Data Mining Features
Mining Model Build Alerts
Oracle Data Miner logs alerts related to model builds in the model viewers and event logs.
-
Model viewers: The build alerts are displayed in the Alerts tab.
-
Event log: All build alerts are displayed along with other details such as job name, node, sub node, time, and message.
Parent topic: Oracle Data Mining Features
R Build Model Node
Oracle Data Mining provides the feature to add R model implementations within the Oracle Data Mining framework. To support R model integration, Oracle Data Miner has been enhanced with a new R Build node with mining functions such as Classification, Regression, Clustering, and Feature Extraction.
Parent topic: Oracle Data Mining Features
Support for Partitioned Models
Oracle Data Miner supports the building and testing of partitioned models.
-
Build Nodes
-
Apply Nodes
-
Test Nodes
Parent topic: Oracle Data Mining Features
Oracle Data Miner Features
The new Oracle Data Miner features include:
- Aggregation Node Support for DATE and TIMESTAMP Data Types
The Aggregation node has been enhanced to support DATE and TIMESTAMP data types. - Enhancement to JSON Query Node
The JSON Query node allows to specify filter conditions on attributes with data types such asARRAY, BOOLEAN, NUMBER
andSTRING.
- Enhancement to Build Nodes
All Build nodes are enhanced to support sampling of training data and preparation of numeric data. - Enhancement to Text Settings
Text settings are enhanced to support the following features: - Refresh Input Data Definition
Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed. - Support for Additional Data Types
Oracle Data Miner allows the following data types for input as columns in a Data Source node, and as new computed columns within the workflow: - Support for In-Memory Column
Oracle Data Miner supports In-Memory Column Store (IM Column Store) in Oracle Database 12.2 and later, which is an optional static SGA pool that stores copies of tables and partitions in a special columnar format. - Support for Workflow Scheduling
Oracle Data Miner supports the feature to schedule workflows to run at a definite date and time. - Enhancement to Polling Performance
Polling performance and resource utilization functionality has been enhanced with new user interfaces. - Workflow Status Polling Performance Improvement
The performance of workflow status polling has been enhanced.
Parent topic: New Features and Changes in Oracle Data Miner
Aggregation Node Support for DATE and TIMESTAMP Data Types
The Aggregation node has been enhanced to support DATE and TIMESTAMP data types.
For DATE and TIMESTAMP data types, the functions available are COUNT(), COUNT (DISTINCT()), MAX(), MEDIAN(), MIN(), STATS_MODE().
Parent topic: Oracle Data Miner Features
Enhancement to JSON Query Node
The JSON Query node allows to specify filter conditions on attributes with data types such as ARRAY, BOOLEAN, NUMBER
and STRING.
All
or Any
in the Filter Settings dialog box. The user also has the option to specify whether to apply filters to data that is used for relational data projection or aggregation definition or both by using any one of the following options:
-
JSON Unnest — Applies filter to JSON data that is used for projection to relational data format.
-
Aggregations — Applies filters to JSON data that is used for aggregation.
-
JSON Unnest and Aggregations — Applies filter to both.
Parent topic: Oracle Data Miner Features
Enhancement to Build Nodes
All Build nodes are enhanced to support sampling of training data and preparation of numeric data.
The enhancement is implemented in the Sampling tab in all Build nodes editors. By default, the Sampling option is set to OFF.
When set to ON
, the user can specify the sample row size or choose the system determined settings.
Note:
Data preparation is not supported in Association Build model.-
Edit Anomaly Detection Node
-
Edit Association Build Node
-
Edit Classification Build Node
-
Edit Clustering Build Node
-
Edit Explicit Feature Extraction Build Node
-
Edit Feature Extraction Build Node
-
Edit Regression Build Node
Parent topic: Oracle Data Miner Features
Enhancement to Text Settings
Text settings are enhanced to support the following features:
-
Text support for synonyms (thesaurus): Text Mining in Oracle Data Miner supports synonyms. By default, no thesaurus is loaded. The user must manually load the default thesaurus provided by Oracle Text or upload his own thesaurus.
-
New settings added in Text tab:
-
Minimum number of rows (documents) required for a token
-
Max number of tokens across all rows (documents)
-
New tokens added for BIGRAM setting:
-
BIGRAM:
Here,NORMAL
tokens are mixed with their bigrams -
STEM BIGRAM:
Here,STEM
tokens are extracted first and then stem bigrams are formed.
-
-
Parent topic: Oracle Data Miner Features
Refresh Input Data Definition
Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed.
SELECT*
capability in the input source. The option allows you to quickly refresh your workflow definitions to include or exclude columns, as applicable.
Note:
The Refresh Input Data Definition option is available as a context menu option in Data Source nodes and SQL Query nodes.Parent topic: Oracle Data Miner Features
Support for Additional Data Types
Oracle Data Miner allows the following data types for input as columns in a Data Source node, and as new computed columns within the workflow:
-
RAW
-
ROWID
-
UROWID
-
URITYPE
URITYPE
data type provides many sub type instances, which are also supported by Oracle Data Miner. They are:
-
HTTPURITYPE
-
DBURITYPE
-
XDBURITYPE
Parent topic: Oracle Data Miner Features
Support for In-Memory Column
Oracle Data Miner supports In-Memory Column Store (IM Column Store) in Oracle Database 12.2 and later, which is an optional static SGA pool that stores copies of tables and partitions in a special columnar format.
Oracle Data Miner has been enhanced to support In-Memory Column in nodes in a workflow. For In-Memory Column settings, the options to set Data Compression Method and Priority Level are available in the Edit Node Performance Settings dialog box.
Parent topic: Oracle Data Miner Features
Support for Workflow Scheduling
Oracle Data Miner supports the feature to schedule workflows to run at a definite date and time.
A scheduled workflow is available only for viewing. The option to cancel a scheduled workflow is available. After cancelling a scheduled workflow, the workflow can be edited and rescheduled.
Parent topic: Oracle Data Miner Features
Enhancement to Polling Performance
Polling performance and resource utilization functionality has been enhanced with new user interfaces.
-
The repository property
POLLING_IDLE_ENABLED
is added to determine whether the user interface will use automatic query or manual query for workflow status. This applies to the Workflow Jobs and Scheduled Jobs user interface. However, the Workflow Editor will continue to poll automatically when monitoring a workflow that is running.Note:
WhenPOLLING_IDLE_ENABLED
is set toTRUE,
then automatic query for workflow status sets in. WhenPOLLING_IDLE_ENABLED
is set toFALSE,
then manual query sets in.A new dockable window Scheduled Workflow has been added that displays the list of scheduled jobs and allows the user to manage the scheduled jobs.
-
The Workflow Jobs window is enhanced with the following new features:
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Manual refresh of workflow jobs.
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Administrative override of automatic updates through Oracle Data Miner repository settings.
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Access to Workflow Jobs preferences through the new Settings option.
-
Parent topic: Oracle Data Miner Features
Workflow Status Polling Performance Improvement
The performance of workflow status polling has been enhanced.
The enhancement includes new repository views, repository properties, and user interface changes:
-
The repository view
ODMR_USER_WORKFLOW_ALL_POLL
is added for workflow status polling. -
The following repository properties are added:
-
POLLING_IDLE_RATE:
Determines the rate at which the client will poll the database when there are no workflows detected as running. -
POLLING_ACTIVE_RATE:
Determines the rate at which the client will poll the database when there are workflows detected running. -
POLLING_IDLE_ENABLED:
Determines whether the user interface will use automatic query or manual query for workflow status. This applies to the Workflow Jobs and Scheduled Jobs user interface. However, the Workflow Editor will continue to poll automatically when monitoring a workflow that is running.Note:
WhenPOLLING_IDLE_ENABLED
is set toTRUE,
then automatic query for workflow status sets in. WhenPOLLING_IDLE_ENABLED
is set toFALSE,
then manual query sets in. -
POLLING_COMPLETED_WINDOW:
Determines the time required to include completed workfows in the polling query result. -
PURGE_WORKFLOW_SCHEDULER_JOBS:
Purges old Oracle Scheduler objects generated by the running of Data Miner workflows. -
PURGE_WORKFLOW_EVENT_LOG:
Controls how many workflow runs are preserved for each workflow in the event log. The events of the older workflow are purged to keep within the limit.
-
-
New user interface includes the Scheduled Jobs window which can be accessed from the Data Miner option in both Tools menu and View menu in SQL Developer 18.3 and later.
Parent topic: Oracle Data Miner Features
Oracle Database Features
The new Oracle Database feature includes the support for expanded object name.
The support for schema name, table name, column name, and synonym that are 128 bytes are available in the upcoming Oracle Database release. To support Oracle Database, Oracle Data Miner repository views, tables, XML schema, and PL/SQL packages are enhanced to support 128 bytes names.
Parent topic: New Features and Changes in Oracle Data Miner