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Table of contents
1.
Introduction
2.
Oracle Data Mining Preface
3.
Features of Oracle Data Miner
4.
Oracle Data Mining Functions
5.
Key Business Advantages
6.
Frequently Asked Questions
6.1.
What is data mining?
6.2.
What is an Oracle Data Miner?
6.3.
How is input taken in ODM?
6.4.
How does ODM do data preparation?
7.
Conclusion
Last Updated: Mar 27, 2024

Oracle Data Mining

Author Pankhuri Goel
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Introduction

Oracle Advanced Analytics Database is part of the Oracle Enterprise Edition. Oracle, the world leader in database software, has combined its database technologies with analytical tools to deliver you Oracle Advanced Analytics Database. It includes classification, regression, prediction, anomaly detection, and other data mining algorithms. This is proprietary software maintained by the Oracle technical team to assist your company in establishing a comprehensive data mining infrastructure at the enterprise level.

 

The algorithms are directly integrated with the Oracle database kernel and function natively on data stored in its own database, removing the requirement for data extraction into standalone analytics servers. The Oracle Data Miner is a set of graphical user interface tools that guide the user through the building, testing and implementation of data models.

Oracle Data Mining Preface

Oracle Data Mining (ODM) is a tool that integrates data mining into the Oracle database. ODM algorithms work directly on relational tables or views, removing the requirement for data to be extracted and sent into standalone tools or specialised analytic servers. The integrated architecture of ODM makes data administration and analysis easier, more reliable, and more efficient. As part of typical database processing pipelines and applications, data mining processes can operate asynchronously and independently of any specific user interface. Data analysts can mine the database for information, create models and methodologies, and then turn the results and methodologies into fully functional application components that can be deployed in production environments.

 

When it comes to deploying models and scoring data in a production setting, the advantages of database integration cannot be overstated. As part of an application, ODM allows a user to leverage all components of Oracle's technology stack. A more direct, more dependable, more powerful advanced business intelligence solution results from fewer "moving parts."

 

ODM allows a single user to access several models at the same time. In the Java interface, ODM programmes can run asynchronously or synchronously. ODM programmes that use the PL/SQL interface execute synchronously. Usage of the Oracle Scheduler is required to run PL/SQL asynchronously.

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Features of Oracle Data Miner

The features of Oracle Data miner are as follows:

  • Machine learning approaches can be created, evaluated, modified, shared, and deployed using an interactive workflow platform.
  • The ODMr tool palette's nodes
    • Visualise data with histograms, summary statistics, scatterplots, and boxplots by exploring and graphing nodes.
    • Binning and recoding variables, missing values treatment, and establishing new "designed features" based on user domain expertise to override are all supported by the Transform node. Oracle Machine Learning prepares data automatically.
    • Column Filter node identifies the most influential attributes in supervised learning using an attribute importance/feature selection algorithm and Kulback-Leibler divergence in unsupervised learning. Using Kulback-Leibler divergence determines the strength of each attribute's correlation with other attributes.
    • Model Build node automates common tasks such as selecting a random sample for train and test datasets, automatic model testing and evaluation, computing a confusion matrix, lift chart, receiver operating characteristic (ROC) curve, and model statistics. It also includes model visualisers such as decision trees, cluster trees, and model attribute coefficients.
  • Ingest and process structured data (numeric and varchar datatypes) in tables and views and unstructured data (CLOBs), transactional data, aggregations, and geographic and graph data.
  • The Model Build node produces numerous machine learning models for comparison when there are multiple algorithms for a given machine learning technique.
  • Integration with open source R for database server-side execution of user-defined R functions, supporting data-parallel and task-parallel execution.
  • Access data from a wide range of large data sources using Big Data SQL, including Oracle Database, Spark, Hadoop, and other data sources.

Oracle Data Mining Functions

Oracle Data Mining supports the below-mentioned data mining functions:

Supervised data mining:

  • Classification is the process of categorising objects into discrete classes and predicting which class they belong to.
  • Approximating and forecasting continuous values using regression
  • Determining which attributes are most significant in predicting outcomes.
  • Anomaly detection is the process of identifying objects that do not meet the criteria for "normal" data (outliers)

 

Unsupervised data mining:

  • Clustering is the process of identifying natural groupings in data.
  • Analysing "market baskets" with association models
  • Feature extraction is done by combining the original attributes to create new attributes (features).

 

Oracle Data Mining allows you to mine one or more text columns.

 

Oracle Data Mining also includes specific sequence search and alignment algorithms (BLAST) to detect similarities between nucleotide and amino acid sequences.

Key Business Advantages

The advantages of Oracle Data Miner, which makes it beneficial for businesses, are as stated below:

  • Reduce data transfer, achieve big data scalability, maintain security, and shorten the time between model creation and deployment.
  • To provide development staging and production deployment scenarios for Oracle Machine Learning models and related data assembly, transformation, and preparation scripts, moving smoothly between Oracle Database environments.
  • In-database machine learning algorithms empower staff with a broad skill set, enabling data-driven projects.
  • For "citizen data scientists," an easy-to-use "drag and drop" user interface speeds up knowledge discovery and model construction.
  • Workflows are a set of documents that describe the machine learning approaches that have been developed for sharing and automation.
  • Automates and accelerates model deployment across the company by generating SQL and PL/SQL scripts from workflows.
  • Invoking workflows programmatically is possible with the Workflow API.

Also read anomalies in database

Frequently Asked Questions

What is data mining?

Data mining is the process of extracting potentially valuable patterns from extensive data collections. It's a multidisciplinary skill set that uses machine learning, statistics, and artificial intelligence to extract data and predict future outcomes. Data mining insights are used for various objectives, including marketing, fraud detection, scientific research, etc.

Data mining discovers previously unknown but valid relationships among previously hidden, unforeseen, and unknown data. Data mining is also known as knowledge discovery in data (KDD), knowledge extraction, data/pattern analysis, information harvesting, and other synonyms. It essentially transforms raw data into valuable information.

 

What is an Oracle Data Miner?

Oracle Data Mining is accessed through Oracle Data Miner, a graphical user interface client application that gives users access to data mining functions and structured templates (known as Mining Activities). They automatically prescribe the order of operations, perform required data transformations, and set model parameters. The user interface also provides for the automatic development of Java and/or SQL code for data-mining tasks. Oracle JDeveloper has a Java Code Generator plugin. The Spreadsheet Add-In for Predictive Analytics is a standalone interface that gives you access to the Oracle Data Mining Predictive Analytics PL/SQL package from Microsoft Excel.

 

How is input taken in ODM?

Most Oracle Data Mining functions take one relational table or view as input. Through the usage of nested columns, flat data can be mixed with transactional data, allowing for the mining of data with one-to-many relationships (e.g. a star schema). When preparing data for data mining, SQL's entire functionality, including dates and spatial data, can be employed.

 

How does ODM do data preparation?

Numerical, categorical, and unstructured (text) properties are distinguished by Oracle Data Mining. Outlier treatment, discretisation, normalisation, and binning(sorting) are some of the data preparation stages provided by the product prior to model creation.

Conclusion

In this article, we learned about Oracle Data Mining. We learned about its functionality and key business advantages. We also learned about Oracle Data Miner and its features.

We hope this blog has helped you enhance your knowledge. If you want to learn more, check out our articles on Data Mining: Turning raw data into useful information – Coding Ninjas BlogData Mining Algorithms | Learn & Practice from Coding Ninjas StudioThe Data Mining Process - Coding Ninjas Coding Ninjas Studio and Data Mining and Data Analytics - Coding Ninjas Coding Ninjas Studio. Do upvote our blog to help other ninjas grow.

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