Oracle Data Mining Update and Xerox Application Charlie Berger Sr. Director of Product Management, Life Sciences and Data Mining Oracle Corporation Raj Minhas Research Scientist Xerox Corporation Session id: 40262

Copyright © 2003 Oracle Corporation Agenda  Oracle Data Mining Update  ODM/DM4J Demonstration  Xerox Application

Copyright © 2003 Oracle Corporation Oracle Business Intelligence Vision DatabaseEngine Data Integration Engine OLAP Engine Mining Engine  Multiple databases  Multiple servers  Multiple engines  Proprietary interfaces  Complex environment  Slow conversion of data to information Is to change this …

Copyright © 2003 Oracle Corporation Oracle Business Intelligence Vision  Single database  Single server  Standard interfaces  Simplified environment  Fast conversion of data to information Data Warehousing ETL OLAP Data Mining Oracle 10g DB Into this …

Copyright © 2003 Oracle Corporation What is Oracle Data Mining?  Oracle Data Mining (ODM) sifts through massive amounts of data to find hidden patterns and information — valuable information that can help you better understand your customers and anticipate their behavior  ODM insights can be revealing, significant, and valuable e.g. – Predict which customers are likely to churn – Discover what factors are involved with a certain disease – Identify fraudulent behavior

Copyright © 2003 Oracle Corporation  Data mining embedded in Oracle10g Database – Simplifies process, eliminates data movement, speeds analysis, deployment and delivers security and scalability  Build models and applications simultaneously – Build and evaluate models and automatically generate Java code  Enhance applications with predictions and insights – For example, build churn prediction applications and enable call centers with greater customer insight Oracle Data Mining Overview & Differentiating Features Data Mining

Copyright © 2003 Oracle Corporation Oracle Data Mining Business Intelligence Applications  CEOs can ask… – How can I target the “right customers” to maximize profits?  Managers can answer… – Which customers are likely to be interested in which offers and why?  Call Reps can… – Suggest the right “offer” for the customer Information Producers Information Consumers  Data Miners can… – Discover patterns and insights hidden in the data Oracle Data Mining

Copyright © 2003 Oracle Corporation Information Consumers Key factors that influence customers likely to purchase a product Customers sorted in likelihood to purchase a product

Copyright © 2003 Oracle Corporation Oracle11i CRM Application CRM / Data Mining Integration  Marketing analysts can design targeted campaigns without becoming data mining experts – Build models, score lists – Discover patters & make predictions Data mining increases effectiveness of targeted campaigns

Copyright © 2003 Oracle Corporation 10g Oracle Data Mining Wide range of data mining algorithms  Feature Selection – Attribute Importance  Supervised learning (classification & prediction) – Naïve Bayes – Adaptive Bayes Networks – Support Vector Machines  Unsupervised learning (clustering and associations) – Association Rules – Orthogonal Clustering – Enhanced k-means Cluster  Feature Extraction – Non Negative Matrix Factorization Data Mining

Copyright © 2003 Oracle Corporation 10g Additional Features  Text Mining – Ability to combine structured data and unstructured data  ODM API – Java – PL/SQL  Scoring engine  Similarity Searches – BLAST (Life sciences: genes and proteins) Data Mining

Copyright © 2003 Oracle Corporation Oracle Data Mining/DM4J Demonstration

Copyright © 2003 Oracle Corporation DM4J2 New Features  Access Data – Import flat file to db wizard  Visualize Data – Data snapshot – Standard summary statistics – Attribute level histograms  Transform Data – Create View / Table – Random and Stratified sampling – Aggregation – Computed column – Normalization – Discretization – Table Splits – Filtering – Recode  Modeling – Building models – Testing models – Applying (scoring) models – Visualize results  Deploy Models/Results – Generate transformation code (PL/SQL) – View and generate transformation lineage – Generate model code (Java) – Integrate with Oracle tools – JDeveloper – Oracle Warehouse Builder – Discoverer

Copyright © 2003 Oracle Corporation Oracle Data Mining Enabling Data Mining Applications DM4J GUI add-ins provides wizards for building and evaluating models

Copyright © 2003 Oracle Corporation Oracle Data Mining Enabling Data Mining Applications Data analysts can build and review data mining models

Copyright © 2003 Oracle Corporation Oracle Data Mining Enabling Data Mining Applications  Comprehensive GUI for preparing data, building models, evaluating results and deploying models DM4J provides features to transform and prepare the data

Copyright © 2003 Oracle Corporation Oracle Data Mining Enabling Data Mining Applications  Automated, scheduled, and event-driven business intelligence applications can can be easily integrated into enterprise applications DM4J automatically generates the Java code

Copyright © 2003 Oracle Corporation Multiple Examples of tumor tissue (public data from Whitehead/MIT) Oracle 10g 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

Copyright © 2003 Oracle Corporation Oracle 10g 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.

Copyright © 2003 Oracle Corporation TDWI “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.” 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.”” TDWI Brief: Oracle Data Mining gets GUI; IBM and Cognos' BI partnership April 9, By Stephen Swoyer

Copyright © 2003 Oracle Corporation Benefits of Oracle’s Approach Oracle Data Mining FeatureBenefit Data Mining algorithms embedded in database  Eliminates data movement and security exposure  Fastest: Data  Information Wide range of data mining algorithms  Supports most data mining problems Runs on multiple platforms  Applications may be developed and deployed Built on Oracle Technology  Grid, RAC, integrated BI,…  SQL & PL/SQL available  Leverage existing skills

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