Oracle Data Miner comprises these new features, and general enhancements.
New features include:
The new Oracle Data Mining features include:
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
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 17.4 is connected to Oracle Database 12.2.Changes to the algorithms include:
CLAS_MAX_SUP_BINS is added in the Decision Tree algorithm.Level of Detailsreplaces the current setting Gather Cluster Statistics.The setting Maximum Supervised Bins CLAS_MAX_SUP_BINS is added in the Decision Tree algorithm.
The setting Level of Detailsreplaces 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
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
The following changes are incorporated to the k-Means algorithm settings.
Levels of Details KMNS_DETAILS
Random Seeds KMNS_RANDOM_SEEDS
Growth Factor
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 RowsSVMS_BATCH_ROWS
SVMS_NUM_PIVOTS
Note:
Applies to Gaussian kernel function only.Active Learning
SVMS_KERNEL_CACHE_SIZE
Note:
Applies to Gaussian kernel function only.The following changes are included in the Singular Value Decomposition and Principal Components Analysis algorithm.
Solver SVDS_SOLVER
Tolerance SVDS_TOLERANCE
Random SeedSVDS_RANDOM_SEED
Over sampling SVDS_OVER_SAMPLING
Power Iteration SVDS_POWER_ITERATION
Approximate Computation ODMS_APPROXIMATE_COMPUTATION
Oracle Data Miner 17.4 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.
The Explicit Feature Extraction node is built using the Explicit Semantic Analysis algorithm.
Document classification
Information retrieval
Calculations related to semantics
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.
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.
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.
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.
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.
The new Oracle Data Miner features include:
ARRAY, BOOLEAN, NUMBER and STRING.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().
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.
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
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.
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.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
Oracle Data Miner supports In-Memory Column Store (IM Column Store) in Oracle Database 12.1.0.2 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.
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.
Polling performance and resource utilization functionality has been enhanced with new user interfaces.
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 to TRUE, then automatic query for workflow status sets in. When POLLING_IDLE_ENABLED is set to FALSE, 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.
Manual refresh of workflow jobs.
Administrative override of automatic updates through Oracle Data Miner repository settings.
Access to Workflow Jobs preferences through the new Settings option.
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 to TRUE, then automatic query for workflow status sets in. When POLLING_IDLE_ENABLED is set to FALSE, 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 17.4.
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.