Oracle Data Mining Update and Xerox Application Charlie Berger Sr. Director of Product Management, Life Sciences and Data ...
Agenda <ul><li>Oracle Data Mining Update </li></ul><ul><li>ODM/DM4J Demonstration </li></ul><ul><li>Xerox Application </li...
Oracle Business Intelligence Vision <ul><li>Multiple databases </li></ul><ul><li>Multiple servers </li></ul><ul><li>Multip...
Oracle Business Intelligence Vision <ul><li>Single database </li></ul><ul><li>Single server </li></ul><ul><li>Standard int...
What is Oracle Data Mining? <ul><li>Oracle Data Mining (ODM) sifts through massive amounts of data to  find hidden pattern...
<ul><li>Data mining embedded in  Oracle10 g   Database </li></ul><ul><ul><li>Simplifies process, eliminates data  movement...
Oracle Data Mining Business Intelligence Applications Information Producers Information Consumers <ul><li>Data Miners can…...
Information Consumers Key factors that influence customers likely to purchase a product Customers sorted in likelihood to ...
Oracle11 i  CRM Application CRM / Data Mining Integration <ul><li>Marketing analysts can design targeted campaigns without...
10 g   Oracle Data Mining Wide range of data mining algorithms <ul><li>Feature Selection </li></ul><ul><ul><li>Attribute I...
10 g  Additional Features <ul><li>Text Mining </li></ul><ul><ul><li>Ability to combine structured data  and unstructured d...
Oracle Data Mining/DM4J Demonstration
DM4J 2   New Features <ul><li>Access Data </li></ul><ul><ul><li>Import flat file to db wizard </li></ul></ul><ul><li>Visua...
Oracle Data Mining Enabling Data Mining Applications DM4J GUI add-ins provides wizards for building and evaluating models
Oracle Data Mining Enabling Data Mining Applications Data analysts can build and review data mining models Data analysts c...
Oracle Data Mining Enabling Data Mining Applications <ul><li>Comprehensive GUI for preparing data, building models, evalua...
Oracle Data Mining Enabling Data Mining Applications <ul><li>Automated, scheduled, and event-driven business intelligence ...
Multiple Examples of tumor tissue (public data from Whitehead/MIT) Oracle 10 g SVM Classification of Multiple Tumor Types ...
Oracle 10 g SVM Classification of Multiple Tumor Types 78.25% accuracy  Green=Correct   Red=Errors Oracle Data Mining’s SV...
TDWI <ul><li>“ Andrew Braunberg, a senior analyst with research  firm TDWI suggests that DM4J should  simplify the job of ...
Benefits of Oracle’s Approach <ul><li>Grid, RAC, integrated BI,… </li></ul><ul><li>SQL & PL/SQL available </li></ul><ul><l...
A Q & Q  U  E  S  T  I  O  N  S A  N  S  W  E  R  S
 
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  • With Oracle9 i Data Mining, all the data mining and data preparation functions operate entirely inside Oracle9 i Database. There is no longer a need to extract large amounts of data to special, dedicated data mining analytic servers to build predictive models. With Oracle9 i , all the data mining functions are completely embedded in the database. Oracle9 i Data Mining can be used to integrate data mining functionality into applications to enhance them with new insights and predictions obtained through mining vast corporate e-business databases. For example, with Oracle9 i Data Mining , you can build churn prediction applications that enable call centers with greater customer insight about who might be likely to leave for a competitor. Similarly, you could build applications that scour your database, find patterns about what type of customers have purchased products, and then use that insight to make predictions about who else might be likely to be interested in a product. Hence, by building data mining functions into your applications, you can extract greater business intelligence, better satisfy customer needs, and produce higher profits. Oracle9 i Data Mining provides programmatic control to the data mining functions through an application programming interface that is based on the emerging Java Data Mining (JDM) open standard. The JDM specification goes beyond the PMML standard to further specify all the operations relating to how the data mining functions will be called to build models and score other data tables to develop advanced business intelligence applications.
  • This example showsan Oracle CRM11i application, Oracle Marketing Online, that has been enhanced using Oracle data mining. This application allows the marketing manager to build predictive models to help increase the response rates of marketing campaigns. The marketing manager simply builds lists of people who have responded to offers (email, telemarketing, direct mail or loyalty) in the past and then lets the application automatically build predictive models and “score” lists of customers with predictions about their likelihood to respond.
  • The main differentiating feature of Oracle9i Data Mining is that all the data mining functions have been incorporated in the Oracle database. No other competitive data mining tool provides this today. ODM is focused on providing data mining infrastructure that supports the building of data mining applications. ODM’s functionality is all accessible via a standards-based, Java-API. Finally, Oracle9 i Data Mining supports 5 algorithms currently — Naïve Bayes, Decision Trees using an Adaptive Baysian Network (ABN) approach, two types of Clustering and Association rules. These data mining algorithms cover the majority of data mining problems. The Naïve Balyes and ABN models can build models that predict A) the likelihood of a specific outcome, such as churn or response to an offer, B) they can predict the most probable outcome from several choices, e.g. highly profitable customer, profitable customer, and not profitable, and C) they can provide the probabilities of each of these outcomes. The ABN decision tree “rules” provide detailed explanations and insights for the reasons or logic for a prediction. The association rules can identify associated products for market basket analysis and defining product bundles and can be used to make predictions. Oracle’s clustering techniques help find natural groupings in the data.
  • Ramaswamy et al 2001
  • Ramaswamy et al 2001
  • Download presentation/whitepaper

    1. 2. Oracle Data Mining Update and Xerox Application Charlie Berger Sr. Director of Product Management, Life Sciences and Data Mining [email_address] Oracle Corporation Raj Minhas Research Scientist Xerox Corporation [email_address] Session id: 40262
    2. 3. Agenda <ul><li>Oracle Data Mining Update </li></ul><ul><li>ODM/DM4J Demonstration </li></ul><ul><li>Xerox Application </li></ul>
    3. 4. Oracle Business Intelligence Vision <ul><li>Multiple databases </li></ul><ul><li>Multiple servers </li></ul><ul><li>Multiple engines </li></ul><ul><li>Proprietary interfaces </li></ul><ul><li>Complex environment </li></ul><ul><li>Slow conversion of data to information </li></ul>Database Engine Data Integration Engine OLAP Engine Mining Engine Is to change this …
    4. 5. Oracle Business Intelligence Vision <ul><li>Single database </li></ul><ul><li>Single server </li></ul><ul><li>Standard interfaces </li></ul><ul><li>Simplified environment </li></ul><ul><li>Fast conversion of data to information </li></ul>Data Warehousing ETL OLAP Data Mining Oracle 10 g DB Into this …
    5. 6. What is Oracle Data Mining? <ul><li>Oracle Data Mining (ODM) sifts through massive amounts of data to find hidden patterns and information </li></ul><ul><ul><li>— valuable information that can help you better understand your customers and anticipate their behavior </li></ul></ul><ul><li>ODM insights can be revealing, significant, and valuable e.g. </li></ul><ul><ul><li>Predict which customers are likely to churn </li></ul></ul><ul><ul><li>Discover what factors are involved with a certain disease </li></ul></ul><ul><ul><li>Identify fraudulent behavior </li></ul></ul>
    6. 7. <ul><li>Data mining embedded in Oracle10 g Database </li></ul><ul><ul><li>Simplifies process, eliminates data movement, speeds analysis, deployment and delivers security and scalability </li></ul></ul><ul><li>Build models and applications simultaneously </li></ul><ul><ul><li>Build and evaluate models and automatically generate Java code </li></ul></ul><ul><li>Enhance applications with predictions and insights </li></ul><ul><ul><li>For example, build churn prediction applications and enable call centers with greater customer insight </li></ul></ul>Oracle Data Mining Overview & Differentiating Features Data Mining
    7. 8. Oracle Data Mining Business Intelligence Applications Information Producers Information Consumers <ul><li>Data Miners can… </li></ul><ul><ul><li>Discover patterns and insights hidden in the data </li></ul></ul>Oracle Data Mining <ul><li>CEOs can ask… </li></ul><ul><ul><li>How can I target the “right customers” to maximize profits? </li></ul></ul><ul><li>Managers can answer… </li></ul><ul><ul><li>Which customers are likely to be interested in which offers and why? </li></ul></ul><ul><li>Call Reps can… </li></ul><ul><ul><li>Suggest the right “offer” for the customer </li></ul></ul>
    8. 9. Information Consumers Key factors that influence customers likely to purchase a product Customers sorted in likelihood to purchase a product
    9. 10. Oracle11 i CRM Application CRM / Data Mining Integration <ul><li>Marketing analysts can design targeted campaigns without becoming data mining experts </li></ul><ul><ul><li>Build models, score lists </li></ul></ul><ul><ul><li>Discover patters & make predictions </li></ul></ul>Data mining increases effectiveness of targeted campaigns
    10. 11. 10 g Oracle Data Mining Wide range of data mining algorithms <ul><li>Feature Selection </li></ul><ul><ul><li>Attribute Importance </li></ul></ul><ul><li>Supervised learning (classification & prediction) </li></ul><ul><ul><li>Naïve Bayes </li></ul></ul><ul><ul><li>Adaptive Bayes Networks </li></ul></ul><ul><ul><li>Support Vector Machines </li></ul></ul><ul><li>Unsupervised learning (clustering and associations) </li></ul><ul><ul><li>Association Rules </li></ul></ul><ul><ul><li>Orthogonal Clustering </li></ul></ul><ul><ul><li>Enhanced k-means Cluster </li></ul></ul><ul><li>Feature Extraction </li></ul><ul><ul><li>Non Negative Matrix Factorization </li></ul></ul>Data Mining
    11. 12. 10 g Additional Features <ul><li>Text Mining </li></ul><ul><ul><li>Ability to combine structured data and unstructured data </li></ul></ul><ul><li>ODM API </li></ul><ul><ul><li>Java </li></ul></ul><ul><ul><li>PL/SQL </li></ul></ul><ul><li>Scoring engine </li></ul><ul><li>Similarity Searches </li></ul><ul><ul><li>BLAST (Life sciences: genes and proteins) </li></ul></ul>Data Mining
    12. 13. Oracle Data Mining/DM4J Demonstration
    13. 14. DM4J 2 New Features <ul><li>Access Data </li></ul><ul><ul><li>Import flat file to db wizard </li></ul></ul><ul><li>Visualize Data </li></ul><ul><ul><li>Data snapshot </li></ul></ul><ul><ul><li>Standard summary statistics </li></ul></ul><ul><ul><li>Attribute level histograms </li></ul></ul><ul><li>Transform Data </li></ul><ul><ul><li>Create View / Table </li></ul></ul><ul><ul><li>Random and Stratified sampling </li></ul></ul><ul><ul><li>Aggregation </li></ul></ul><ul><ul><li>Computed column </li></ul></ul><ul><ul><li>Normalization </li></ul></ul><ul><ul><li>Discretization </li></ul></ul><ul><ul><li>Table Splits </li></ul></ul><ul><ul><li>Filtering </li></ul></ul><ul><ul><li>Recode </li></ul></ul><ul><li>Modeling </li></ul><ul><ul><li>Building models </li></ul></ul><ul><ul><li>Testing models </li></ul></ul><ul><ul><li>Applying (scoring) models </li></ul></ul><ul><ul><li>Visualize results </li></ul></ul><ul><li>Deploy Models/Results </li></ul><ul><ul><li>Generate transformation code (PL/SQL) </li></ul></ul><ul><ul><li>View and generate transformation lineage </li></ul></ul><ul><ul><li>Generate model code (Java) </li></ul></ul><ul><ul><li>Integrate with Oracle tools </li></ul></ul><ul><ul><ul><li>JDeveloper </li></ul></ul></ul><ul><ul><ul><li>Oracle Warehouse Builder </li></ul></ul></ul><ul><ul><ul><li>Discoverer </li></ul></ul></ul>
    14. 15. Oracle Data Mining Enabling Data Mining Applications DM4J GUI add-ins provides wizards for building and evaluating models
    15. 16. Oracle Data Mining Enabling Data Mining Applications Data analysts can build and review data mining models Data analysts can build and review data mining models
    16. 17. Oracle Data Mining Enabling Data Mining Applications <ul><li>Comprehensive GUI for preparing data, building models, evaluating results and deploying models </li></ul>DM4J provides features to transform and prepare the data
    17. 18. Oracle Data Mining Enabling Data Mining Applications <ul><li>Automated, scheduled, and event-driven business intelligence applications can can be easily integrated into enterprise applications </li></ul>DM4J automatically generates the Java code
    18. 19. Multiple Examples of tumor tissue (public data from Whitehead/MIT) Oracle 10 g SVM Classification of Multiple Tumor Types DNA Microarray Data Oracle Data Mining 78.25% accuracy Green=Correct Red=Errors We feed multiple cancer types data into the Oracle DB: 16,063 genes, 144 cancer patients and 10 samples per class. We mine the data using Support Vector Machines and create the confusion matrix
    19. 20. Oracle 10 g SVM Classification of Multiple Tumor Types 78.25% accuracy Green=Correct Red=Errors Oracle Data Mining’s SVM models are able to accurately predict the multi-class tumor problem with 78.25% accuracy.               
    20. 21. TDWI <ul><li>“ Andrew Braunberg, a senior analyst with research firm TDWI suggests that DM4J should simplify the job of data analysts . Before Oracle released DM4J, Braunberg notes, analysts who used ODM had to write out all of the Java code that was required to build their predictive models. “This was a time-consuming process that slowed model development and deployment.” </li></ul><ul><li>With DM4J, Braunberg notes, Java code is automatically written as data analysts build their predictive models. Moreover, developers or data analysts can re-use this code in other Java-based applications. As a result, he anticipates, DM4J will “enhance analysts’ ability to create predictive models using Oracle Data Mining.”” </li></ul><ul><li>TDWI Brief: Oracle Data Mining gets GUI; IBM and Cognos' BI partnership April 9, 2003 http://www.dw-institute.com/research/display.asp?id=6632 </li></ul><ul><li>By Stephen Swoyer </li></ul>
    21. 22. Benefits of Oracle’s Approach <ul><li>Grid, RAC, integrated BI,… </li></ul><ul><li>SQL & PL/SQL available </li></ul><ul><li>Leverage existing skills </li></ul>Built on Oracle Technology <ul><li>Applications may be developed and deployed </li></ul>Runs on multiple platforms <ul><li>Supports most data mining problems </li></ul>Wide range of data mining algorithms <ul><li>Eliminates data movement and security exposure </li></ul><ul><li>Fastest: Data  Information </li></ul>Data Mining algorithms embedded in database Benefit Oracle Data Mining Feature
    22. 23. A Q & Q U E S T I O N S A N S W E R S
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