computer science

Oracle Data Mining

Oracle Data Mining is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Oracle Data Mining rather than just read about it. In short: Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics.

Key takeaways

Oracle Data Mining belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Oracle Data Mining to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Oracle Data Mining from memory before moving on to harder problems.

Reference excerpt

Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics. It provides means for the creation, management and operational deployment of data mining models inside the database environment.

Overview Oracle Corporation has implemented a variety of data mining algorithms inside its Oracle Database relational database product. These implementations integrate directly with the Oracle database kernel and operate natively on data stored in the relational database tables. This eliminates the need for extraction or transfer of data into standalone mining/analytic servers. The relational database platform is leveraged to securely manage models and to efficiently execute SQL queries on large volumes of data. The system is organized around a few generic operations providing a general unified interface for data-mining functions. These operations include functions to create, apply, test, and manipulate data-mining models. Models are created and stored as database objects, and their management is done within the database - similar to tables, views, indexes and other database objects. In data mining, the process of using a model to derive predictions or descriptions of behavior that is yet to occur is called "scoring". In traditional analytic workbenches, a model built in the analytic engine has to be deployed in a mission-critical system to score new data, or the data is moved from relational tables into the analytical workbench - most workbenches offer proprietary scoring interfaces. ODM simplifies model deployment by offering Oracle SQL functions to score data stored right in the database. This way, the user/application-developer can leverage the full power of Oracle SQL - in terms of the ability to pipeline and manipulate the results over several levels, and in terms of parallelizing and partitioning data access for performance. Models can be created and managed by one of several means. Oracle Data Miner provides a graphical user interface that steps the user through the process of creating, testing, and applying models (e.g. along the lines of the CRISP-DM methodology). Application- and tools-developers can embed predictive and descriptive mining capabilities using PL/SQL or Java APIs. Business analysts can quickly experiment with, or demonstrate the power of, predictive analytics using Oracle Spreadsheet Add-In for Predictive Analytics, a dedicated Microsoft Excel adaptor interface. ODM offers a choice of well-known machine learning approaches such as Decision Trees, Naive Bayes, Support vector machines, Generalized linear model (GLM) for predictive mining, Association rules, K-means and Orthogonal Partitioning Clustering, and Non-negative matrix factorization for descriptive mining. A minimum description length based technique to grade the relative importance of input mining attributes for a given problem is also provided. Most Oracle Data Mining functions also allow text mining by accepting text (unstructured data) attributes as input. Users do not need to configure text-mining options - the Database_options database option handles this behind the scenes.

History Oracle Data Mining was first introduced in 2002 and its releases are named according to the corresponding Oracle database release:

Oracle Data Mining 9iR2 (9.2.0.1.0 - May 2002) Oracle Data Mining 10gR1 (10.1.0.2.0 - February 2004) Oracle Data Mining 10gR2 (10.2.0.1.0 - July 2005) Oracle Data Mining 11gR1 (11.1 - September 2007) Oracle Data Mining 11gR2 (11.2 - September 2009) Oracle Data Mining is a logical successor of the Darwin data mining toolset developed by Thinking Machines Corporation in the mid-1990s and later distributed by Oracle after its acquisition of Thinking Machines in 1999. However, the product itself is a complete redesign and rewrite from ground-up - while Darwin was a classic GUI-based analytical workbench, ODM offers a data mining development/deployment platform integrated into the Oracle database, along with the Oracle Data Miner GUI. The Oracle Data Miner 11gR2 New Workflow GUI was previewed at Oracle Open World 2009. An updated Oracle Data Miner GUI was released in 2012. It is free, and is available as an extension to Oracle SQL Developer 3.1 .

Functionality As of release 11gR1 Oracle Data Mining contains the following data mining functions:

Data transformation and model analysis: Data sampling, binning, discretization, and other data transformations. Model exploration, evaluation and analysis. Feature selection (Attribute Importance). Minimum description length (MDL). Classification. Naive Bayes (NB). Generalized linear model (GLM) for Logistic regression. Support Vector Machine (SVM). Decision Trees (DT). Anomaly detection. One-class Support Vector Machine (SVM). Regression Support Vector Machine (SVM). Generalized linear model (GLM) for Multiple regression Clustering: Enhanced k-means (EKM). Orthogonal Partitioning Clustering (O-Cluster). Association rule learning: Itemsets and association rules (AM). Feature extraction. Non-negative matrix factorization (NMF). Text and spatial mining: Combined text and non-text columns of input data. Spatial/GIS data.

Input sources and data preparation Most Oracle Data Mining functions accept as input one relational table or view. Flat data can be combined with transactional data through the use of nested columns, enabling mining of data involving one-to-many relationships (e.g. a star schema). The full functionality of SQL can be used when preparing data for data mining, including dates and spatial data. Oracle Data Mining distinguishes numerical, categorical, and unstructured (text) attributes. The product also provides utilities for data preparation steps prior to model building such as outlier treatment, discretization, normalization and binning (sorting in general speak)

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Oracle Data Mining

Start with the simplest possible case. Write down what Oracle Data Mining claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Oracle Data Mining before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Oracle Data Mining ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Oracle Data Mining

In research: Oracle Data Mining appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Oracle Data Mining in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Oracle Data Mining is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data mining and machine learning software, Oracle software, so understanding it makes those chapters shorter.
In everyday life: Look for Oracle Data Mining outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Oracle Data Mining in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Oracle Data Mining means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Oracle Data Mining out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Oracle Data Mining in simple terms?

Why does Oracle Data Mining matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Oracle Data Mining?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Oracle Data Mining.