• What is Oracle9i Data Mining?
  
• How does Oracle9i Data Mining differ from Oracle Data Mining Suite (Darwin)?
  
• How does Oracle9i Data Mining fit into the Oracle strategy?
  
• Why would a business benefit from using Oracle9i Data Mining?
  
• What are some typical applications that could be enhanced by Oracle9i Data Mining?
  
• What is the target market?
  
• What algorithms does Oracle9i Data Mining support?
  
• What kind of API does Oracle9i Data Mining use?
  
• What are Oracle9i Data Mining's competitive advantages?
  
• What platforms does Oracle9i Data Mining run on?
  
• What are the system requirements to run Oracle9i Data Mining?
  
• Where can I find more information about Oracle9i Data Mining?

What is Oracle9i Data Mining?
Oracle9i Data Mining is an option to Oracle9i Database Enterprise Edition (EE) that embeds data mining functionality for making classifications, predictions, and associations. All model-building and scoring functions are accessible through a Java-based API.

How does Oracle9i Data Mining differ from Oracle Data Mining Suite (Darwin)?
The main difference is that Oracle Data Mining Suite has a Windows GUI and is targeted towards data analysts doing "ad hoc" data mining. Oracle9i Data Mining targets Java application developers who want to automate the extraction of business intelligence and its integration into other business applications. Our tests show that we're achieving similar results.

How does Oracle9i Data Mining fit into the Oracle strategy?
Oracle9i Data Mining fits into Oracle's strategy to simplify and add value. Because all the data remains in-database, the process of data mining is simplified. Oracle9i Database scales to any problem encountered. Because the insights obtained through data mining are available in the database to any other user or application, Oracle9i Data Mining helps leverage your investment dollars by making the new business information available to everyone.

Why would a business benefit from using Oracle9i Data Mining?
Data mining can sift through massive amounts of data and find hidden information — valuable information that can help you better understand your customers and anticipate their behavior. Oracle9i Data Mining software helps you build applications to uncover this hidden information about your customers. Armed with this information, you can build a close relationship with and understand your customers, which helps you to:

• Better retain customers and avoid churn
  
• Profile customers and understand behavior
  
• Maintain and improve profit margins
  
• Reduce customer acquisition costs
  
• Target profitable customers with the right offer

Oracle9i Data Mining can also find patterns hidden in scientific, government, manufacturing, medical, and other types of data. Applications of data mining in these areas include:

• Predicting the quality of a manufactured part
  
• Finding associations between patients, drugs, and outcomes
  
• Identifying possible network intrusions

Insights discovered by Oracle9i Data Mining can be revealing, significant, and valuable.

What are some typical applications that could be enhanced by Oracle9i Data Mining?
Oracle9i Data Mining can automate the extraction and integration of new insight and predictions into a variety of business applications, including call centers, Web sites, campaign management systems, ATMs, enterprise resource management (ERM), and other operational and business planning applications.

What is the target market?
Oracle9i Data Mining is best suited for companies that have lots of data, are committed to the Oracle platform, and want to automate and operationalize their extraction of business intelligence. The initial end user is a Java application developer, although the end user of the application enhanced by data mining could be a customer service rep, marketing manager, customer, business manager, or just about any other imaginable user.

What algorithms does Oracle9i Data Mining support?
Oracle9i Data Mining provides programmatic access to two data mining algorithms embedded in Oracle9i Database through a Java-based API. Data mining algorithms are machine-learning techniques for analyzing data for specific categories of problems. Different algorithms are good at different types of analysis. Oracle9i Data Mining provides two algorithms: Naive Bayes for Classifications and Predictions and Association Rules for finding patterns of co-occurring events. Together, they cover a broad range of business problems.

Naive Bayes: Oracle9i Data Mining's Naive Bayes algorithm can predict binary or multi-class outcomes. In binary problems, each record either will or will not exhibit the modeled behavior. For example, a model could be built to predict whether a customer will churn or remain loyal. Naive Bayes can also make predictions for multi-class problems where there are several possible outcomes. For example, a model could be built to predict which class of service will be preferred by each prospect.

Binary model example:
Q: Is this customer likely to become a high-profit customer?
A: Yes, with 85% probability

Multi-class model example:
Q: Which one of five customer segments is this customer most likely to fit into — Grow, Stable, Defect, Decline or Insignificant?
A: Stable, with 55% probability

Association Rules: Association Rules detect "associated" or co-occurring events hidden in databases. Association analysis, or unsupervised learning, is often used to find popular bundles (e.g. market basket analysis) of products that are related for customers, such as "milk" and "cereal" being associated with "bananas." Oracle9i Data Mining's Association Rules can be used to identify co-occurring items or events in a variety of business problems, such as:

• Which manufactured parts and equipment settings are associated with failure events?
  
• Which patient and drug attributes are associated with which outcomes?
  
• Which items or products is this person most likely to buy or like?

The Association Rules algorithm generates a set of antecedent and consequent pairs in the form of A implies B with a probability of n%. This modeling technique allows users to discover associations between items or events. Oracle9i Data Mining includes utilities for examining and reporting the association rules. The associations or "rules" thus discovered are useful in designing special promotions, products bundles, and store displays.

What kind of API does Oracle9i Data Mining use?
Automation of data mining tasks is facilitated by Oracle9i Data Mining's Java-based API. Application programmers can control all aspects of data mining. They can expose complex settings for advanced users or completely automate the process for business users. Programmatic control extends from data preparation and model building to on-demand scoring of single records and batch scoring of large data sets. Batch scores may be stored in relational tables for access by other business applications (e.g. call centers or marketing campaign systems) or called "on-demand" in interactive applications where new information is collected that must be factored in the predictive model.
Competitive Advantages of Java-based API

What are Oracle9i Data Mining's competitive advantages?
Oracle9i Data Mining provides several distinctive competitive advantages:

Data Mining Embedded in Oracle9i Database
Ability to Enhance Applications with Predictions and Insights
Java-based API

1. Data Mining Embedded in Oracle9i Database
   Oracle9i Data Mining simplifies the process of extracting business intelligence from large amounts of data because it eliminates all tasks related with off-loading data to external special-purpose analytic servers for data mining and the subsequent scoring of even larger amounts of data involved to make predictions. With Oracle9i Data Mining, all the data mining functionality is embedded in Oracle9i Database, so the data, data preparation, data mining, and scoring all occur within the database. This can greatly simplify the entire process of data mining — from source data to decision.

   Oracle9i Data Mining is available on all platforms supported by Oracle9i. This provides the widest range of platform support of any competitive data mining vendor.

   Oracle9i Data Mining can scale to the size of the problem by adding hardware or switching to more powerful platforms. Oracle9i Data Mining is not limited in the amount of data it can mine by the hardware/omputing limitations of the PC, like some competitive PC data mining offerings. Oracle9i Data Mining takes advantage of Oracle's parallelism for faster computing.

2. Ability to Enhance Applications with Predictions and Insights
   Oracle9i Data Mining enables companies to systemize the extraction and integration of new business intelligence within their operations. Application developers can use Oracle9i Data Mining's Java-based API to add data mining insights and predictions to enhance business applications, such as Customer Relationship Management (CRM), Enterprise Resource Management (ERP), Web portals, and wireless applications. If a customer wishes to migrate their data mining application to another platform, their investment is preserved.

   Rather than having special departments of advanced data analysts who work on ad hoc data mining projects, the true value of data mining is realized when the new insights and predictions are integrated and "operationalized" into existing business applications.

   Telecommunications companies, for example, can use Oracle9i Data Mining to build churn applications that identify customers that are likely to churn before they leave for a competitor. Oracle9i Data Mining's predictions can be used to anticipate customer behavior and proactively manage them in mutually beneficial 1:1 relationships.

3. Java-based API
   Application developers access Oracle9i Data Mining's functionality through a Java-based API. Programmatic control of all data mining functions enables automation of data preparation, model-building, and model-scoring operations. Java Data Mining (JDM) is an emerging data mining standard, following SUN's Java Community Process as a Java Specification Request (JSR). JDM has participation from Oracle, Sun, IBM, and many other companies that recognize the need for a Java- based standard for specifying and using data mining. JDM leverages several evolving data mining standards, including Object Management Group's Common Warehouse Metadata (CWM), the Data Mining Group's Predictive Mining Markup Language (PMML), and International Standards Organization's SQL/MM for Data Mining.

   Oracle9i Data Mining's API provides an early look at concepts and approaches being proposed for JDM. Ultimately, Oracle9i Data Mining will comply with the standard after it is published.