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Oracle Data Miner

Oracle Data Miner is an extension to Oracle SQL Developer. Oracle Data Miner is a graphical user interface to Oracle Data Mining, a feature of Oracle Database.

Data analysts can use the intuitive Oracle Data Miner graphical user interface (GUI) to discover hidden patterns, relationships, and insights in their data. With Oracle Data Miner, everything occurs in an Oracle Database—in a single, secure, scalable platform for advanced business intelligence. Oracle Data Miner eliminates data movement and duplication, maintains security, and minimizes latency time from raw data to valuable information. Enterprises can use Oracle Data Miner for knowledge discovery to better compete on analytics.

Oracle Data Miner helps you perform the data preparation and model building required by the data mining process.

New Features and Changes in Oracle Data Miner 4.2

Oracle Data Miner 4.2 has been enhanced with new features, along with some general enhancements.

New features include:

  • Association Model Aggregation Metrics
    Oracle Data Miner 4.2 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 4.2 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 4.2 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 4.2 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 4.2 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 4.2 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 4.2 supports the building and testing of partitioned models.
  • 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 as ARRAY, BOOLEAN, NUMBER and STRING.
  • 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 4.2 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 12.1.0.2 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 4.2 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.
  • Oracle Database Features
    The new Oracle Database feature includes the support for expanded object name.

Association Model Aggregation Metrics

Oracle Data Miner 4.2 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.


See Also:

Association Model Aggregation Metrics

Enhancements to Algorithm Settings

Oracle Data Miner 4.2 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 4.2 is connected to Oracle Database 12.2.

Changes to the algorithms include:

Changes to Decision Tree Algorithm Settings

The setting Maximum Supervised Bins CLAS_MAX_SUP_BINS is added in the Decision Tree algorithm.

Changes to Expectation Maximization Algorithm Settings

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.

Settings Added:

  • Random Seed

  • Model Search

  • Remove Small Components

Settings Deprecated:

Approximate Computation ODMS_APPROXIMATE_COMPUTATION

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.

Settings Added:

  • Convergence Tolerance GLMS_CONV_TOLERANCE

  • Number of Iterations GLMS_NUM_ITERATIONS

  • Batch Rows GLMS_BATCH_ROWS

  • Solver GLMS_SOLVER

  • Sparse Solver GLMS_SPARSE_SOLVER

Settings Deprecated:

  • Approximate Computation ODMS_APPROXIMATE_COMPUTATION

  • Categorical Predictor Treatment GLMS_SELECT_BLOCK

  • Sampling for Feature Identification GLMS_FTR_IDENTIFICATION

  • Feature Acceptance GLMS_FTR_ACCEPTANCE

Changes to k-Means Algorithm Settings

The following changes are incorporated to the k-Means algorithm settings.

Settings Added:

  • Levels of Details KMNS_DETAILS

  • Random Seeds KMNS_RANDOM_SEEDS

Settings Deprecated:

  • Growth Factor

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.

Settings Added:

  • Solver SVMS_SOLVER

  • Number of Iterations SVMS_NUM_ITERATIONS

  • Regularizer SVMS_REGULARIZER

  • Batch RowsSVMS_BATCH_ROWS

  • Number of Pivots SVMS_NUM_PIVOTS

    Note:

    Applies to Gaussian kernel function only.

Settings Deprecated:

  • Active Learning

  • Cache Size SVMS_KERNEL_CACHE_SIZE

    Note:

    Applies to Gaussian kernel function only.

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.

Settings Added:

  • Solver SVDS_SOLVER

  • Tolerance SVDS_TOLERANCE

  • Random SeedSVDS_RANDOM_SEED

  • Over sampling SVDS_OVER_SAMPLING

  • Power Iteration SVDS_POWER_ITERATION

Settings Deprecated:

  • Approximate Computation ODMS_APPROXIMATE_COMPUTATION

Support for Explicit Semantic Analysis Algorithm

Oracle Data Miner 4.2 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.

Explicit Feature Extraction Node

The Explicit Feature Extraction node is built using the Explicit Semantic Analysis algorithm.

You can use the Explicit Feature Extraction node for the following:

  • Document classification

  • Information retrieval

  • Calculations related to semantics

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.

The requirements of a Feature Compare node are:

  • 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.

Enhancement to Data Mining Model Detail View

The model viewers in Oracle Data Miner 4.2 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.

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 4.2 logs alerts related to model builds in the model viewers and event logs.

After a model build, Oracle Data Miner server queries Oracle Data Mining for any alerts related to the model build. The alerts are logged in:

  • 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.

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 4.2 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 4.2 supports the building and testing of partitioned models.

The following models are enhanced to support partitioned models:

  • Build Nodes

  • Apply Nodes

  • Test Nodes

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().

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.

The user can apply filters to the data in hierarchical order using the option 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.

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.

The Sampling option is available in the following Build node editors:

  • 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

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.

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.

The Refresh Input Data Definition option is equivalent to 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.

Support for Additional Data Types

Oracle Data Miner 4.2 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

The URITYPE data type provides many sub type instances, which are also supported by Oracle Data Miner 4.2. They are:

  • HTTPURITYPE

  • DBURITYPE

  • XDBURITYPE

Support for In-Memory Column

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 4.2 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.

Support for Workflow Scheduling

Oracle Data Miner 4.2 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.

Enhancement to Polling Performance

Polling performance and resource utilization functionality has been enhanced with new user interfaces.

The enhancement is supported by the following features:
  • 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:

    When POLLING_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.

  • The Workflow Jobs window is enhanced with the following new features:

    • 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.

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:

      When POLLING_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 4.2.

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.

About the Data Mining Process

Data mining is the process of extracting useful information from masses of data by extracting patterns and trends from the data.

Data mining requires a problem definition, collection and cleansing of data, and model building. Most of the time spent in a typical data mining project is devoted to understanding and processing of data.

Related Topics

Overview of Oracle Data Miner

Oracle Data Miner is an extension to Oracle SQL Developer. It is a graphical user interface to Oracle Data Mining, a feature of Oracle Database.

  • Oracle Data Miner enables users to build descriptive and predictive models to:

    • Predict customer behavior

    • Target best customers

    • Discover customer clusters, segments, and profiles

    • Identify customer retention risks

    • Identify promising selling opportunities

    • Detect anomalous behavior

  • Oracle Data Miner provides an Application Programming Interface (API) that enables programmers to build and use models.

  • Oracle Data Miner workflows capture and document the analytical methodology of the user. It can be saved and shared with others to automate advanced analytical methodologies.

  • The Oracle Data Miner GUI is an extension to Oracle SQL Developer 3.0 or later that enables data analysts to:

    • Work directly with data inside the database

    • Explore the data graphically

    • Build and evaluate multiple data mining models

    • Apply Oracle Data Miner models to new data

    • Deploy Oracle Data Miner predictions and insights throughout the enterprise

Figure: Sample Data Miner Workflow shows a sample workflow of Oracle Data Miner.

Figure: Sample Data Miner Workflow

Description of Figure 1-1 follows
Description of "Figure: Sample Data Miner Workflow"

Oracle Data Miner creates predictive models that application developers can integrate into applications to automate the discovery and distribution of new business intelligence—predictions, patterns, and discoveries—throughout the enterprise.

Related Topics

Architecture of Oracle Data Miner

Oracle Data Miner consists of a server and one or more clients.

Before you install Oracle Data Miner, you must understand the architecture of Oracle Data Miner:

  • Oracle Data Miner, the client, is an integrated feature of Oracle SQL Developer 3.0 or later.

  • Oracle Database 12c Release 2 (12.2) Enterprise Edition (includes Oracle Database Personal Edition) or an earlier version of Oracle Database Enterprise Edition is the server. In addition to the database, Oracle Data Miner requires the installation of a Data Miner repository account. The repository is a separate account in the database named ODMRSYS. This repository is shared by all user accounts in the database that have been granted the appropriate privileges to use the Data Miner repository.

Figure: Oracle Data Miner Components shows the components of Oracle Data Miner.

Figure: Oracle Data Miner Components

Description of Figure 1-2 follows
Description of "Figure: Oracle Data Miner Components"

Oracle Database Enterprise Edition includes these services that are critical to the support of Oracle Data Miner:

  • Oracle Data Miner: A component of the Oracle Advanced Analytics option to Oracle Database Enterprise Edition. Oracle Data Miner provides model building, testing, and scoring capabilities for Oracle Data Miner.

  • Oracle XML DB: Provides services to manage the Oracle Data Miner repository metadata, such as the details about the workflow specifications.

  • Oracle Scheduler: Provides the engine for scheduling the Oracle Data Miner workflows.

  • Oracle Text: Provides services necessary to support text mining.

Snippets in Oracle Data Miner

Snippets are code fragments, such as SQL functions, optimizer hints, and miscellaneous PL/SQL programming techniques.

SQL Developer provides snippets to help you write PL/SQL programs. Some snippets are just syntax, and others are examples. You can insert and edit snippets when you are using the SQL Worksheet or when creating or editing a PL/SQL function or procedure using SQL Worksheet or SQL Query node.

  • Open SQL worksheet: To open SQL Worksheet, go to Connections for SQL Developer, right-click the connection to use, and select Open SQL Worksheet.

  • Insert snippet: To insert a snippet into your code in a SQL Worksheet or in a PL/SQL function or procedure, drag the snippet from the snippets window and drop it into the desired place in your code. Then edit the syntax so that the SQL function is valid in the current context. To see a brief description of a SQL function in a tool tip, hold the pointer over the function name. Oracle Data Miner provides snippets for the EXPLAIN, PREDICT, and PROFILE procedures in DBMS_PREDICTIVE_ANALYTICS, and for the Data Mining functions for scoring data using Prediction, Clustering, or Feature Extraction.


See Also:

See the SQL Developer Online Help for more information about SQL Worksheet.

Using Predictive Analytics Snippets

In Oracle SQL Developer, you can view the list of Predictive Analytics snippets and use them.

To view the list of Predictive Analytics functions, and to use a snippet:

  1. Open Oracle SQL Developer.
  2. Select the connection that you are using for Oracle Data Miner.
  3. From the SQL Developer menu, go to View and then select Snippets.
  4. From the drop-down list, select Predictive Analytics.

    The Predictive Analytics group of snippets includes the following snippets:

    • Explain: Use DBMS_PREDICTIVE_ANALYTICS.EXPLAIN() to rank attributes in order of influence when explaining a target column.

    • Predict: Use DBMS_PREDICTIVE_ANALYTICS.PREDICT() to predict the value of a target column based on values in the input data.

    • Prediction Anomaly Function: Use the Anomaly Detection Predictive query to predict anomalous customers.

    • Prediction Classification Function: Makes predictions using dynamic classifications.

    • Prediction Cluster Function: Predicts the cluster a customer belongs to.

    • Prediction Feature Set Function: Predicts feature sets to provide a general characterization of the underlying customer data.

    • Prediction Regression Function: Predicts the age of customers who are likely to use an affinity card.

    • Profile: Use DBMS_PREDICTIVE_ANALYTICS.PROFILE() to generate rules that identify the records that have the same target value.

  5. To use a snippet, drag the snippet to the SQL Worksheet or to a place in a PL/SQL program.

    Note:

    The Explain, Predict, and Profile snippets have one or more commented-out DROP statements, such as:

    --DROP TABLE mining_explain_result;

    If you run one of these snippets more than once, then remove the comment characters for the DROP statement.


If you drag the Explain snippet to SQL Worksheet, then you see:

--Available in Oracle Enterprise DB 10.2 and later

--Ranks attributes in order of influence to explain a target column.

--For more info go to: http://www.oracle.com/pls/db112/vbook_subject?subject=dma

--Remove comment on the Drop statement if you want to rerun this script

--DROP TABLE mining_explain_result;

--Perform EXPLAIN operation

BEGIN

DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(

data_table_name => '"CUSTOMERS"',

explain_column_name => '"CUST_GENDER"',

result_table_name => 'mining_explain_result',

data_schema_name => '"SH"');

END;

/

--output first 10 rows from resulting table mining_explain_result

COLUMN ATTRIBUTE_NAME FORMAT A30

COLUMN ATTRIBUTE_SUBNAME FORMAT A30

COLUMN EXPLANATORY_VALUE FORMAT 0D999999

COLUMN RANK FORMAT 999999

select * from mining_explain_result where rownum < 10;

When you run this code, you get the following results (in Script Output):

anonymous block completed

ATTRIBUTE_NAME ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK

----------------- ------------------------------ ----------------- ------

CUST_LAST_NAME 0.151359 1

CUST_MARITAL_STATUS 0.015043 3

CUST_INCOME_LEVEL 0.002592 4

CUST_CREDIT_LIMIT 0.000195 5

CUST_EMAIL 0.000000 6

CUST_TOTAL 0.000000 6

CUST_TOTAL_ID 0.000000 6

CUST_FIRST_NAME 0.000000 6

9 rows selected

About Oracle Data Miner Repository Installation

The Oracle Data Miner repository resides in the database that the Oracle Data Miner client connects to. The repository stores metadata about workflows.

The Oracle by Example tutorial Setting Up Oracle Data Miner 4.1 describes how to install the Oracle Data Miner Repository and grants rights to an account. The tutorial describes the following steps:

  1. Create a Data Miner User Account using SQL Developer.

  2. Create a SQL Developer connection for the Data Miner User.

  3. Install the Data Miner Repository.

    By default, sample data is installed. You can deselect loading these tables and views.

    You can drop the repository through GUI, if necessary.

If you have dropped a Repository or if you do not want to use the OBE, then you must reinstall the repository.

When you install the repository, rights are automatically granted to the user account that you connect with.

To grant rights to another account, define a database connection for the account and open the account in the Data Miner navigator. The GUI tells you that the account does not have the correct grants. Click OK for the grants to be created. You must log in using DBA privilege.


See Also:


Installing the Data Miner Repository Using the GUI

The Data Miner Repository installation process starts automatically when you activate a SQL Developer connection for the first time from the Data Miner tab.

To install the repository:

  1. Double-click a connection. If the repository is not installed, then the warning message 
    Repository not Installed
    is displayed. You also see this message if you try to create a project when the repository is not installed.

    Click Yes to install a Data Miner repository in the database to which you are connected.


    Note:

    If you have several database connections, then you need to install the repository using only one connection. You must grant privileges to the other connections.


  2. Log in using an administrative account for the database that you are connected to.

  3. The Repository Installation Settings dialog box opens.

    • Name of the repository—ODMRSYS. You cannot change this name.

    • Select a default tablespace and a temporary tablespace, and click OK.

      If you are connected to Oracle Database 11g Release 2 (11.2.0.4) or later, then permanent tablespaces are filtered so that only Oracle ASM tablespaces are displayed. The temporary tablespace does not have to be Oracle ASM.

      If you are connected to Oracle Database 11g Release 2 (11.2.0.3) or earlier, then no filtering takes place.

  4. The Install Data Miner Repository dialog box opens. By default, demo data is installed used by the Oracle By Example tutorials. Click Start to begin the installation.


    Note:

    The sample data requires the SH schema.


  5. The installation may take several minutes. After the installation completes, you get a message indicating that the task completed successfully. You can examine the log of the task by clicking Show Log. Click Close when done.

  6. If you have more than one Connection, then you must grant privileges to each connection as follows:

    1. Click the connection.

    2. In the Required Privileges Missing dialog box, click Yes to grant privileges.

    3. Log in using an administrative account for the database that you connect to.

    4. In the Data Miner Grants dialog box, click Start to grant privileges and to install sample data.

    5. After the tasks completes, a message is displayed indicating that the task has completed successfully. You can examine the log of the task by clicking Show Log.

    6. Click Close when done.

About Dropping the Oracle Data Miner Repository

If you plan to stop using Oracle Data Miner, then you must drop the Repository. You may also have to drop the repository when you upgrade from one version of Oracle Database to another.

When you drop the Repository, all workflows and internal tables are dropped. Models created by Oracle Data Miner are dropped. Tables created by the Create Table or View node are not dropped.


Note:

Drop Repository cannot be undone. When you drop a repository, the repository and all internal user objects created by Data Miner are permanently removed. The objects removed include models, tables or views generated by the Create Table node, hidden tables used store results (model viewers), and text specification objects used to support text transformations. In addition all Data Miner workflows are dropped. To save a workflow, export it, and later import it.


Before you drop the Repository, check that no projects or workflows are open. If any connections are open, then the process may fail.

For Drop Repository to complete successfully, no sessions with the role of ODMRUSER can be active. Active ODMRUSER sessions can result in object locks that block dropping of the repository.

During the process of dropping the repository:

  • The database does not allow any new connections to be established during this process.

  • All sessions with the ODMRUSER role are automatically disconnected.

  • All workflows and internal tables are dropped. Models created by Oracle Data Miner are dropped. Tables created by the Create Table or View node are dropped.


Note:

After you drop the repository, you must install the repository again before you can perform any data mining.


Related Topics

Dropping the Data Miner Repository Using GUI

There is one Oracle Data Miner repository per database. If you have several database connections, then it is necessary to drop the repository for one connection only.

To drop the repository:

  1. In SQL Developer UI, go to Tools and click Data Miner. Then click Drop Repository.
  2. The Drop Repository dialog box opens. Select the connection where you want to drop the repository. You can add and edit connections here, if required.
  3. Log in with DBA privilege and click OK.
  4. A list of sessions that will be disconnected is displayed. Click OK to continue.
  5. The Drop Data Miner Repository dialog box opens. Click Start to begin the process of dropping the repository. This process may take several minutes. Specific messages indicate which steps are being performed.

    After the operation completes, examine the log files to see which operations were performed.

About Oracle Data Miner Repository Migration

Migration may require conversion of the repository. If migration is required, then the Migrate Oracle Data Miner Repository dialog box opens.

If you have downloaded a new version of SQL Developer that is not compatible with the installed Data Miner repository, then you will be notified that a Data Miner repository upgrade is necessary when you open a connection used for data mining. The GUI issues a message describing the problem and asks if it should perform necessary migration. If you answer yes, then you are prompted for the administrative password.

Migrating the Oracle Data Miner Repository Using the GUI

Migration of Oracle Data Miner repository arises when upgrading the Oracle Data Miner client, or when upgrading Oracle Database on which SQL Developer is installed.

You must migrate the Oracle Data Miner repository in the following cases:

  • A version of SQL Developer before 4.0 is installed on Oracle Database 11g Release 2 (11.2.0.4) or later, and you want to connect using a SQL Developer 4.0 or later client, that is, you upgrade the client.

  • SQL Developer 4.0 installed on a version of Oracle Database 11g Release 2 (11.2.0.3) or earlier, and the database is upgraded to Oracle Database 11g Release 2 (11.2.0.4) or later.

Either of these conditions is detected when you open the connection in the Data Miner Navigator. If the ODMRSYS default permanent tablespace is not an ASM tablespace, then a dialog is displayed requesting an ASM tablespace. The ASM tablespace does not replace the existing default permanent tablespace already specified for ODMRSYS, but instead, it converts the workflow_data column that stores the workflow XML data in the ODMR$_WORKFLOWS table. The workflow_data is usually the largest data component stored in ODMRSYS. This approach reduces the amount of time required to perform the migration.

The Repository Migration does not request temporary tablespace.

How to Use Oracle Data Miner

Lists the different ways in which you can learn how to use Oracle Data Miner.

You can learn how to use Oracle Data Miner in the following ways:

  • By using Oracle By Example tutorials

  • By experimenting on your own using the sample data

  • By using the Online Help

  • By asking questions in the Oracle Data Mining forum

  • By referring to Oracle Data Miner documentation for reference information about data mining in general and Oracle Data Miner in particular.

  • Oracle By Example for Oracle Data Miner 4.1
    The Oracle By Example tutorials teach you how to install and use Oracle Data Miner.
  • Sample Data
    Sample Data is loaded in your account when you install Oracle Data Miner.
  • Oracle Data Miner Online Help
    The online help specific to Oracle Data Miner is in the help folder Oracle Data Miner Concepts and Usage.
  • Oracle Data Mining Forum
    Oracle Data Mining forum is a discussion forum to share, participate, and follow discussions related to data mining and Oracle Data Miner. You must log in to participate.
  • Oracle Data Miner Documentation
    Oracle Data Miner is the graphical user interface for Oracle Data Mining. Oracle Data Miner is a component of the Oracle Advanced Analytics option to Oracle Database Enterprise Edition.

Oracle By Example for Oracle Data Miner 4.1

The Oracle By Example tutorials teach you how to install and use Oracle Data Miner.

You can learn how to:

  • Set Up Oracle Data Miner 4.1: This tutorial covers the process of setting up Oracle Data Miner for use within Oracle SQL Developer 4.1 connected to Oracle Database 12c.

  • Use Oracle Data Miner 4.1: This tutorial covers the use of Oracle Data Miner 4.1 to perform data mining on Oracle Database 12c. In this lesson, you examine and solve a data mining business problem by using the Oracle Data Miner graphical user interface (GUI). The Oracle Data Miner GUI is included as an extension of Oracle SQL Developer, version 4.1.

  • Use Feature Selection and Generation with GLM: This tutorial covers the use of Oracle Data Miner 4.1 to leverage enhancements to the Oracle implementation of Generalized Liner Models (GLM) for Oracle Database 12c. These enhancements include support for Feature Selection and Generation.

  • Perform Text Mining with an Expectation Maximization Clustering Model: This tutorial covers the use of Oracle Data Miner 4.1 to leverage new text mining enhancements while applying a clustering model. In this lesson, you learn how to use the Expectation Maximization (EM) algorithm in a clustering model.

  • Use Predictive Queries With Oracle Data Miner 4.1: This tutorial covers the use of Predictive Queries against mining data by using Oracle Data Miner 4.1.

  • Use the SQL Query Node in an Oracle Data Miner Workflow: This tutorial covers the use of the new SQL Query Node in an Oracle Data Miner 4.1 workflow.


Note:

The tutorials are in Oracle Learning Library under the Oracle Data Miner 12c OBE Series at Oracle Data Mining 4.1 OBE (Oracle By Example) Series.


Sample Data

Sample Data is loaded in your account when you install Oracle Data Miner.


Note:

The SH sample schema is not shipped with Oracle Database 12.2. To install the sample schema, go to https://github.com/oracle/db-sample-schemas.

Oracle Data Miner Online Help

The online help specific to Oracle Data Miner is in the help folder Oracle Data Miner Concepts and Usage.

To view or search the online help for Oracle Data Miner click Help and then click Table of Content. Then expand the Table of Content and go to Oracle Data Miner Concepts and Usage on the Contents tab of Help Center.

To get help for a specific dialog box, click the Help button or press F1. To get help for objects in a workflow, select the object and press the F1 key.

Online help contains reference topics and the topics that describe how the GUI works. To see reference topics, either expand the help contents in the online help or search in the online help.

Search the Online Help

To search the online help, use the search box at the top of Help Center:
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Type the word or words that you want to search for in the search box and press Enter.

Select one of the following search options: case sensitive (Match case) or case insensitive; and whether to match topics based on all specified words, any specified words, or a Boolean expression.

Search performs a full-text search of all Oracle Data Miner online help topics, including Oracle Data Miner Release Notes.

To cancel a search, click Cancel symbol. A red square..

Oracle Data Mining Forum

Oracle Data Mining forum is a discussion forum to share, participate, and follow discussions related to data mining and Oracle Data Miner. You must log in to participate.

Participate in the Oracle Data Miner Discussion Forum at http://forums.oracle.com/community/developer/english/business_intelligence/data_warehousing/data_mining to discuss using Oracle Data Miner and data mining.

Oracle Data Miner Documentation

Oracle Data Miner is the graphical user interface for Oracle Data Mining. Oracle Data Miner is a component of the Oracle Advanced Analytics option to Oracle Database Enterprise Edition.

Oracle Data Miner documentation is included in the Oracle Database Documentation Library for the version of the database that you have installed. Documentation Libraries are posted at the Documentation site at http://docs.oracle.com in the Database section. To go directly to the Business Intelligence and Data Warehousing documentation, use http://www.oracle.com/pls/topic/lookup?ctx=db112&id=dwbitab if you connect to Oracle Database 11g Release 2 (11.2) or Oracle Database 12.2 if you connect to Oracle database 12c Release 2 (12.2).