About Oracle Data Mining
Oracle Data Mining is a data mining software from Oracle that provides analytics capabilities. It combines accessibility policy, Oracle Cloud Infrastructure, and Oracle AI Database so users can analyze large datasets efficiently. This software is designed to facilitate data analysis and model building for improved decision-making processes. It also includes Oracle Fusion Cloud Applications and quick links for easier navigation and integration with other Oracle services. Key capabilities: accessibility policy Oracle Cloud Infrastructure Oracle Fusion Cloud Applications Oracle AI Database quick links Best for: data analysts and data scientists that need to perform advanced analytics and machine learning tasks on large data sets.
Oracle Data Miner (ODM) is a sophisticated and deeply integrated extension of Oracle SQL Developer that brings powerful machine learning capabilities directly into the Oracle Database environment. Designed to streamline the development and deployment of predictive and descriptive models, it eliminates the traditional inefficiencies of moving data across environments by enabling in-database processing. This approach not only improves performance and scalability but also significantly enhances data security—critical for organizations dealing with sensitive and large-scale data. The interface itself is a major asset. With its intuitive drag-and-drop workflow editor, users—from seasoned data scientists to non-technical analysts often referred to as “citizen data scientists”—can construct complex machine learning workflows with minimal coding effort. Each step in the data pipeline is represented visually, making analytical processes easy to follow, replicate, and share. The user experience is further enriched with built-in model visualization tools, such as cluster and decision trees, allowing for interpretability that goes beyond black-box results.
Pros & Cons
- +Extensive Data Integration: Easily connects disparate data sources and supports complex workflows and transformations2.
- +Robust Governance & Compliance: Offers strong tools for data quality, security, and regulatory compliance4.
- +Advanced Analytics: Includes embedded machine learning, real-time insights, and powerful visualization tools for data exploration6.
- +Scalable & Flexible Deployment: Supports cloud, on-premises, and hybrid environments with high availability architecture8.
- +Marketing Automation Compatibility: Integrates well with existing marketing platforms for audience targeting and segmentation.
- +Global Accessibility: Cloud-based access enables data availability from anywhere.
- +Support Resources: Extensive documentation and training materials help users get up to speed quickly10.
- −Complex Setup: Initial configuration and integration with business processes can be time-consuming.
- −Steep Learning Curve: Requires technical expertise to fully leverage advanced features.
- −Pricing Structure: May be cost-prohibitive for smaller organizations due to complex licensing.
- −Customization Overload: High flexibility can lead to usability challenges for non-technical users.
- −Mobile Limitations: Lack of seamless mobile access and reliance on roaming for authentication may hinder remote use.
- −Data Accuracy Concerns: Some users noted that consumer data accuracy could be improved for marketing use cases.
Features
Key features
- In-Database Machine Learning
- Enables data scientists and analysts to build and deploy ML models directly within the Oracle Database, eliminating data movement.
- Graphical Drag-and-Drop Workflow Editor
- Provides an intuitive visual interface to create, evaluate, and share machine learning methodologies without coding.
- Automated Model Building & Evaluation
- Automatically builds multiple ML models for comparison, including automated testing, evaluation metrics (confusion matrix, lift chart, ROC curve), and visualizers.
- SQL and PL/SQL Script Generation
- Automatically generates scripts from workflows to accelerate the deployment of models throughout the enterprise.
- Integration with Open Source R
- Allows the execution of user-defined R functions within the database server for advanced analytics.
- Big Data SQL Connectivity
- Works with Big Data SQL to access and process data across various big data sources (Oracle Database, Spark, Hadoop).
Additional features
- Extension to Oracle SQL Developer
- Functions as an extension within the Oracle SQL Developer environment.
- Interactive Workflow Tool
- Provides a graphical interface for creating, evaluating, modifying, sharing, and deploying machine learning methodologies.
- Explore and Graph Nodes
- Features nodes for visualizing data, including histograms, summary statistics, scatterplots, and boxplots.
- Transform Node
- Supports popular and custom data transformations, such as binning, recoding variables, missing values treatment, and creating new "engineered features."
- Column Filter Node
- Uses attribute importance/feature selection algorithms (for supervised learning) and Kulback-Leibler divergence (for unsupervised learning) to identify influential attributes.
- Model Build Node
- Automates common steps like creating train/test datasets, model testing/evaluation, and computing various metrics (confusion matrix, lift chart, ROC curve, model statistics).
- Model Visualizers
- Offers visualizers for models, including decision trees, cluster trees, and model attribute coefficients.
- Ingest and Process Structured Data
- Handles numeric and varchar datatypes in tables and views.
- Ingest and Process Unstructured Data
- Capable of processing CLOBs (Character Large Objects) for text.
- Ingest and Process Transactional Data
- Supports analysis of transactional data.
- Ingest and Process Aggregations
- Works with aggregated data.
- Ingest and Process Spatial and Graph Data
- Can analyze spatial and graph data.
- Automatic Multiple Model Building
- Automatically builds multiple machine learning models for a given technique for comparison.
- Integration with Oracle Machine Learning for R
- Enables in-database execution of user-defined R functions, including data-parallel and task-parallel execution.
- Big Data SQL Compatibility
- Accesses data across Oracle Database, Spark, Hadoop, and other big data sources.
- Eliminate Data Movement
- Performs analytics directly in the database, avoiding data extraction and transfer.
- Achieve Big Data Scalability
- Leverages the scalability of the Oracle Database for large datasets.
- Preserve Security
- Utilizes Oracle Database's security mechanisms to protect data and models.
- Accelerate Time from Model Development to Deployment
- Streamlines the process from model creation to operational use.
- Support for Development, Staging, and Production Environments
- Facilitates easy movement of models and scripts between different Oracle Database environments.
- Empower Diverse Skillsets
- Enables data-driven projects for users with varied technical backgrounds through in-database algorithms.
- Drag-and-Drop User Face
- Accelerates knowledge discovery and model building for "citizen data scientists."
- Workflow Documentation
- Documents machine learning methodologies for sharing and automation.
- Generates SQL and PL/SQL Scripts
- Creates scripts from workflows for automated model deployment.
- Workflow API
- Allows programmatic invocation of workflows.
Pricing
Countries & Languages
Interface languages
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About Oracle Data Mining
Oracle Data Mining is a data mining software from Oracle that provides analytics capabilities. It combines accessibility policy, Oracle Cloud Infrastructure, and Oracle AI Database so users can analyze large datasets efficiently. This software is designed to facilitate data analysis and model building for improved decision-making processes. It also includes Oracle Fusion Cloud Applications and quick links for easier navigation and integration with other Oracle services. Key capabilities: accessibility policy Oracle Cloud Infrastructure Oracle Fusion Cloud Applications Oracle AI Database quick links Best for: data analysts and data scientists that need to perform advanced analytics and machine learning tasks on large data sets.
Oracle Data Mining Details
Oracle Data Mining's In-App Market Place
Does Oracle Data Mining have an in-app market place?
Yes
How many Mini-Apps in the marketplace?
1
Mini Apps
N/A
Pricing Options
Accepted Payment Currencies
USD ($), EUR (€), GBP (£), JPY (¥), AUD (A$), CAD (C$), CHF (CHF), CNY (¥), SEK (kr), NZD (NZ$), KRW (₩), INR (₹), RUB (₽), BRL (R$), MXN (MXN$).
Pros & Cons
- Extensive Data Integration: Easily connects disparate data sources and supports complex workflows and transformations2.
- Robust Governance & Compliance: Offers strong tools for data quality, security, and regulatory compliance4.
- Advanced Analytics: Includes embedded machine learning, real-time insights, and powerful visualization tools for data exploration6.
- Scalable & Flexible Deployment: Supports cloud, on-premises, and hybrid environments with high availability architecture8.
- Marketing Automation Compatibility: Integrates well with existing marketing platforms for audience targeting and segmentation.
- Global Accessibility: Cloud-based access enables data availability from anywhere.
- Support Resources: Extensive documentation and training materials help users get up to speed quickly10.
- Complex Setup: Initial configuration and integration with business processes can be time-consuming.
- Steep Learning Curve: Requires technical expertise to fully leverage advanced features.
- Pricing Structure: May be cost-prohibitive for smaller organizations due to complex licensing.
- Customization Overload: High flexibility can lead to usability challenges for non-technical users.
- Mobile Limitations: Lack of seamless mobile access and reliance on roaming for authentication may hinder remote use.
- Data Accuracy Concerns: Some users noted that consumer data accuracy could be improved for marketing use cases.
Oracle Data Mining's Support Options
Contact
+1 737-867-1000Oracle Data Mining's Alternatives
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