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8/3/2019 Oracle Data Mining 11g Release 2 OOW2010 1
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Copyright 2010 Oracle Corporation
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Copyright 2010 Oracle Corporation
Oracle Data Mining 11g Release 2Charlie BergerSr. Director Product Management, Data Mining and Advanced AnalyticsOracle Corporationcharlie.berger@oracle.comwww.twitter.com/CharlieDataMine
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Copyright 2010 Oracle Corporation
The following is intended to outline our generalproduct direction. It is intended for informationpurposes only, and may not be incorporated into anycontract. It is not a commitment to deliver any
material, code, or functionality, and should not berelied upon in making purchasing decisions.The development, release, and timing of anyfeatures or functionality described for Oracles
products remains at the sole discretion of Oracle.
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Agenda
Market Drivers Oracle Data Mining
Exadata and Oracle Data Mining
Oracle Data Miner 11g Release 2 New GUI
Oracle Statistical Functions
Ability to Import 3rd Party e.g. SAS models
Applications Powered by Oracle Data Mining
Getting Started with ODM
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Market Drivers
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Analytics: Strategic and Mission Critical
Competing on Analytics, by Tom Davenport Some companies have built their very businesses
on their ability to collect, analyze, and act on data.
Although numerous organizations are embracing analytics, only ahandful have achieved this level of proficiency. But analyticscompetitors are the leaders in their varied fieldsconsumer products
finance, retail, and travel and entertainment among them.
Organizations are moving beyond query and reporting- IDC 2006
Super Crunchers, by Ian Ayers In the past, one could get by on intuition and experience.
Times have changed. Today, the name of the game is data.Steven D. Levitt, author of Freakonomics
Data-mining and statistical analysis have suddenly becomecool.... Dissecting marketing, politics, and even sports, stuff thiscomplex and important shouldn't be this much funto read.Wired
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Competitive Advantage
Optimization
Predictive Modeling
Forecasting/Extrapolation
Statistical Analysis
Alerts
Query/drill down
Ad hoc reports
Standard Reports
Degree of Intelligence
CompetitiveAd
vantage
Whats the best that can happen?
What will happen next?
What if these trends continue?
Why is this happening?
What actions are needed?
Where exactly is the problem?
How many, how often, where?
What happened?
Source: Competing on Analytics, by T. Davenport & J. Harris
$$Analytic$
Access &Reporting
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Oracle Data Mining
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11 years stem celling analytics into Oracle Designed advanced analytics into database kernel to leverage relational
database strengths
Nave Bayes and Association Rules1st algorithms added
Leverages counting, conditional probabilities, and much more
Now, analytical database platform 12 cutting edge machine learning algorithms and 50+ statistical functions
A data mining model is a schema object in the database, built via a PL/SQL APIand scored via built-in SQL functions.
When building models, leverage existing scalable technology
(e.g., parallel execution, bitmap indexes, aggregation techniques) and add new coredatabase technology (e.g., recursion within the parallel infrastructure, IEEE float, etc.)
True power of embedding within the database is evident when scoring modelsusing built-in SQL functions (incl. Exadata)
select cust_id
from customers
where region = US
andprediction_probability(churnmod, Y using *) > 0.8;
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You Can Think of It Like This
Traditional SQL
Human-driven queries Domain expertise
Any rules must bedefined and managed
SQL Queries SELECT
DISTINCT
AGGREGATE
WHERE
AND OR
GROUP BY
ORDER BY
RANK
Oracle Data Mining
Automated knowledgediscovery, model building anddeployment
Domain expertise to assemblethe right data to mine
ODM Verbs PREDICT
DETECT
CLUSTER
CLASSIFY
REGRESS
PROFILE
IDENTIFY FACTORS
ASSOCIATE
+
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Oracle Data Mining Algorithms
Classification
AssociationRules
Clustering
AttributeImportance
Problem Algorithm ApplicabilityClassical statistical technique
Popular / Rules / transparency
Embedded app
Wide / narrow data / text
Minimum DescriptionLength (MDL)
Attribute reductionIdentify useful dataReduce data noise
Hierarchical K-Means
Hierarchical O-Cluster
Product groupingText mining
Gene and protein analysis
AprioriMarket basket analysisLink analysis
Multiple Regression (GLM)Support Vector Machine
Classical statistical technique
Wide / narrow data / text
Regression
FeatureExtraction
NMFText analysisFeature reduction
Logistic Regression (GLM)Decision TreesNave BayesSupport Vector Machine
One Class SVM Lack examplesAnomalyDetection
A1 A2 A3 A4 A5 A6 A7
F1 F2 F3 F4
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Oracle Data Miner 11g Release 2 GUIFree product on OTN for Oracle Data Mining Option
Graphical UserInterface for dataanalyst
SQL DeveloperExtension (OTN download)
Explore datadiscover new insights
Build and evaluatedata mining models
Apply predictive
models Share analytical
workflows
Deploy SQL Applycode/scripts
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The Forrester Wave: Predictive Analytics And
Data Mining Solutions, Q1 2010Oracle Data Mining Cited as a Leader; 2ndplace in Current Offering
Ranks 2nd place inCurrent Offering
Oracle focuses on in-database mining in theOracle Database, onintegration of Oracle DataMining into the kernel ofthat database, and onleveraging that technology
in Oracles branded
applications.
The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is agraphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forresterdoes not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment atthe time and are subject to change.
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Traditional Analytics (SAS) Environment
Source Data(Oracle, DB2,
SQL Server,
TeraData,
Ext. Tables, etc.)
SAS WorkArea
(SAS Datasets)
SASProcessing
(Statistical
functions/
Data mining)
ProcessOutput
(SAS Work Area)
Target(e.g. Oracle)
SAS environment requires:
Data movement
Data duplication
Loss of security
SAS SAS SASX X X
Hours, Days or Weeks
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Traditional Analytics (SAS) Environment
Source Data(Oracle, DB2,
SQL Server,
TeraData,
Ext. Tables, etc.)
SAS WorkArea
(SAS Datasets)
SASProcessing
(Statistical
functions/
Data mining)
ProcessOutput
(SAS Work Area)
Target(e.g. Oracle)
SAS environment requires:
Data movement
Data duplication
Loss of security
SAS SAS SASX X X Oracle environment:
Eliminates data movement
Eliminates data duplication
Preserves security
Secs, Mins or Hours
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Traditional Analytics
Hours, Days or Weeks
In-Database Data Mining
Data Extraction
Data Prep &Transformation
Data MiningModel Building
Data MiningModel Scoring
Data Preparationand
Transformation
Data Import
Source
Data
SAS
WorkArea
SAS
Processing
Process
Output
Target
Results Faster time for
Data to Insights
Lower TCOEliminates Data Movement Data Duplication
Maintains Security
Data remains in the Database
SQLMost powerful language for datapreparation and transformation
Embedded data preparation
Cutting edge machine learningalgorithms inside the SQL kernel of
Database
Model ScoringData remains in the Database
Savings
Secs, Mins or Hours
Model Scoring
Embedded Data Prep
Data Preparation
Model Building
Oracle Data Mining
SAS SAS SAS
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Exadata & ODM
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Exadata + Data Mining 11g Release 2DM Scoring Pushed to Storage!
Company Confidential June 2009
Scoring function executed in Exadata
Faster
In 11g Release 2, SQL predicates and Oracle Data Mining
models are pushed to storage level for executionFor example, find the US customers likely to churn:
select cust_idfrom customerswhere region = US
andprediction_probability(churnmod,Y using *) > 0.8;
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Exadata + Data Mining 11g Release 2Benefits
Eliminates data movement 2X-5X+ faster scoring on Exadata Depends on number of joins involved with data for scoring
Preserves security
Significant architecture and performance advantagesover SAS Institute
Years ahead of SASs road map to move SAS analytics
towards RDBMSs (http://support.sas.com/resources/papers/InDatabase07.pdf)
Netezza performance but using industry standard
RDBMS + SQL-based in-database advanced analytics
Best platform for building enterprise predictiveanalytics applications e.g. Fusion ApplicationsAnalytical iPod for the Enterprise
Faster
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Oracle Data Miner11g Release 2
Easier
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Oracle Data Miner 11g Release 2 GUI
Predictcustomer behavior
Identify keyfactors
Predict next-likelyproduct
Customer profiling
Detect fraud &anomalies
Mine text and
unstructured data
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Explore Data
Thumbnail
distributions ofevery attribute
Grouped byanother attribute
Summarystatistics for allattributes
Min, max, stdev,variance
median, mean,skewness,kurtosis, etc.
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Build and Evaluate Models
Comparative
modelperformanceresults
Adjust and tune
predictivemodels
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Understand Model Details
Interactive model
viewers
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Analytical Work Flow Methodologies
Build, share andautomate predictiveanalyticsmethodologies
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SQL Developer Active Query Builder
New, easy touse, interactivequery builder inSQL Developerfor assemblingand preparingdatafor mining
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Example: Simple, Predictive SQL
Select customers who are more than 85% likely to be HIGH VALUE
customers & display their AGE & MORTGAGE_AMOUNT
SELECT * from(
SELECT A.CUST_ID, A.AGE,
MORTGAGE_AMOUNT,PREDICTION_PROBABILITY
(CUST_INSUR_LT46939_DT, 'VERY HIGH'
USING A.*) prob
FROM CBERGER.CUST_INSUR_LTV A)
WHERE prob > 0.85;
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Fraud Prediction Demodrop table CLAIMS_SET;
exec dbms_data_mining.drop_model('CLAIMSMODEL');
create table CLAIMS_SET (setting_name varchar2(30), setting_value varchar2(4000));insert into CLAIMS_SET values
('ALGO_NAME','ALGO_SUPPORT_VECTOR_MACHINES');
insert into CLAIMS_SET values ('PREP_AUTO','ON');
commit;
begin
dbms_data_mining.create_model('CLAIMSMODEL', 'CLASSIFICATION',
'CLAIMS2', 'POLICYNUMBER', null, 'CLAIMS_SET');
end;/
-- Top 5 most suspicious fraud policy holder claims
select * from
(select POLICYNUMBER, round(prob_fraud*100,2) percent_fraud,
rank() over (order by prob_fraud desc) rnk from
(select POLICYNUMBER, prediction_probability(CLAIMSMODEL, '0' using *) prob_fraud
from CLAIMS2
where PASTNUMBEROFCLAIMS in ('2 to 4', 'more than 4')))where rnk
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Real-time Predictionwithrecords as (select
78000 SALARY,250000 MORTGAGE_AMOUNT,6 TIME_AS_CUSTOMER,12 MONTHLY_CHECKS_WRITTEN,55 AGE,423 BANK_FUNDS,'Married' MARITAL_STATUS,
'Nurse' PROFESSION,'M' SEX,4000 CREDIT_CARD_LIMITS,2 N_OF_DEPENDENTS,2 HOUSE_OWNERSHIP from dual)
select s.prediction prediction, s.probability probabilityfrom (select PREDICTION_SET(CUST_INSUR_LT46939_DT, 1 USING *) psetfrom records) t, TABLE(t.pset) s;
On-the-fly, single recordapply with new data (e.g.
from call center)
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Oracle StatisticalFunctions (Free)
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11g Statistics & SQL Analytics (Free)
Ranking functions rank, dense_rank, cume_dist,
percent_rank, ntile
Window Aggregate functions(moving and cumulative)
Avg, sum, min, max, count, variance,stddev, first_value, last_value
LAG/LEAD functions
Direct inter-row reference using offsets Reporting Aggregate functions
Sum, avg, min, max, variance, stddev,count, ratio_to_report
Statistical Aggregates Correlation, linear regression family,
covariance
Linear regression Fitting of an ordinary-least-squares
regression line to a set of number pairs.
Frequently combined with theCOVAR_POP, COVAR_SAMP, andCORR functions
Descriptive Statistics DBMS_STAT_FUNCS: summarizes
numerical columns of a table and returnscount, min, max, range, mean, median,stats_mode, variance, standard deviation,quantile values, +/- n sigma values,top/bottom 5 values
Correlations Pearsons correlation coefficients, Spearman's
and Kendall's (both nonparametric).
Cross Tabs Enhanced with % statistics: chi squared, phi
coefficient, Cramer's V, contingencycoefficient, Cohen's kappa
Hypothesis Testing Student t-test , F-test, Binomial test, Wilcoxon
Signed Ranks test, Chi-square, Mann Whitneytest, Kolmogorov-Smirnov test, One-wayANOVA
Distribution Fitting Kolmogorov-Smirnov Test, Anderson-Darling
Test, Chi-Squared Test, Normal, Uniform,Weibull, Exponential
Note: Statistics and SQL Analytics are included in Oracle Database Standard Edition
Statistics
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Split Lot A/B Offer testing
Offer A to onepopulation and Bto another
Over time periodt calculate medianpurchase amountsof customers receiving offer A & B
Perform t-test to compare
If statistically significantly better resultsachieved from one offer over another, offereveryone higher performing offer
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Independent Samples T-Test(Pooled Variances)
Query compares the mean of AMOUNT_SOLD betweenMEN and WOMEN within CUST_INCOME_LEVEL ranges
SELECT substr(cust_income_level,1,22) income_level,
avg(decode(cust_gender,'M',amount_sold,null)) sold_to_men,
avg(decode(cust_gender,'F',amount_sold,null)) sold_to_women,stats_t_test_indep(cust_gender, amount_sold, 'STATISTIC','F')
t_observed,
stats_t_test_indep(cust_gender, amount_sold) two_sided_p_value
FROM sh.customers c, sh.sales s
WHERE c.cust_id=s.cust_id
GROUP BY rollup(cust_income_level)
ORDER BY 1;
SQL Worksheet
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Ability to Import3rd Party e.g. SAS Models
New
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Ability to Import 3rd Party DM Models
Capability to import 3rd
party dm models, import, andconvert to native ODM models
Benefits
SAS, SPSS, R, etc. data mining models can be used for scoring insidethe Database
Imported dm models become native ODM models and inherit all ODMbenefits including scoring at Exadata storage layer, 1st class objects,security, etc.
New
Hours, Days or WeeksSource
Data
SAS
WorkArea
SAS
Processing
Process
Output
Target
SAS SAS SAS
SAS
T diti l M lti St P
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SAS
Traditional Multi-Step ProcessModel Build & Scoring in SAS; Import Scores
Traditional Analytics
Data Viewscreated as
Selects, Joins +Transforms
Data Prep &Transformationassociated w/
Modeling/Mining
SAS Work Area(SAS Datasets)
Data MiningModel Building &
Evaluation
Data Prep &Transformationassociated w/
Building Models
Data Prep &Transformation
associated w/Scoring Models
Data MiningModel Scoring
SAS Datasets
Import Scores
Data Extraction
T diti l M lti St P
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SAS
Traditional Multi-Step ProcessModel Build in SAS; Manual SAS Model Conversion
Traditional Analytics
Data Viewscreated as
Selects, Joins +Transforms
Data Prep &Transformationassociated w/
Modeling/Mining
SAS Work Area(SAS Datasets)
Data MiningModel Building &
Evaluation
Data Prep &Transformationassociated w/
Building Models
SAS DatasetsData Extraction
ManualConversion ofSAS Models to
PL/SQL
Scoring Data Data Prep &Transformation
associated w/Scoring Models
Data MiningSQL Scoring
Abilit t I t 3rd P t DM M d l
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SAS
Ability to Import 3rd Party DM ModelsImport SAS PMML, Convert to ODM; Score In-DB
Traditional Analytics
Data Viewscreated as
Selects, Joins +Transforms
Data Prep &Transformationassociated w/
Modeling/Mining
SAS Work Area(SAS Datasets)
Data MiningModel Building &
Evaluation
Data Prep &Transformationassociated w/
Building Models
SAS DatasetsData Extraction
Scoring Data Data Prep &Transformation
associated w/Scoring Models
Data MiningModel Scoring
SAS models converted to
native ODM functions
BetterFaster
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In-Database SAS ScoringScore the SAS_ODM Model
SAS models become native ODMmodels
No loss of information
Original source data for scoringremains in Database
Exadata scoring of SAS models
Faster
SAS
SAS
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In-Database SAS ScoringImport the SAS Modelbegin
dbms_data_mining.import_model(
'SAS_Log_Reg_Model4',
XMLType(bfilename('PMML_DIR',
'SAS_Logistic_Regression_PMML_Model.xml'),
nls_charset_id('AL32UTF8'))
);
end;
/
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In-Database SAS ScoringScore the SAS_ODM Model
selectprediction(SAS_Log_Reg_Model4 using *),
prediction_probability(SAS_Log_Reg_Model using *)
from
sas_dataset where id < 10;
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Applications Poweredby Oracle Data Mining
Simpler!
Predictive Analytics Applications
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Predictive Analytics ApplicationsPowered byOracle Data Mining (Partial List as of March 2010)
CRM OnDemandSales ProspectorOracle Communications Data Model
Spend Classification
Oracle Open World - Schedule Builder
Oracle Retail Data Model
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New Applications Powered by ODM
Fusion HCM Predictive Analytics
Fusion CRM Sales Predictor
Oracle Identity and Access Management 11g
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Meet John
Top Performer
Domain Expert for Next Product
Mentor to other Workers
Cross Team Liaison
Arranges Monthly Socials
Senior Member of Team
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John Resigns
Q4:Q1:
1. 4 years at same level
2. 18 Months since last payincrease
3. ManagerUnderperforming
4. 10 Months since last dayoff
5. Competition Hiring
6. Market Recovering
7. Top Performer
Q2: Q3:
The average internalcost of turnover for a
single exempt worker isa minimum of one
year's pay and benefits,or a maximum of two
years' pay and benefit.
Saratoga Institute, 2008
Attrition Rate is 21%with a Replacement
Cost of $1.1B.
JohnsReplacement
Hired
You Find OutJohn Goes to
Competition
Johns
Performance
Drops
Johns Retention
Issues Emerge
Source: McKinsey War for Talent 2000
Wouldnt It Be Nice To Know & CorrectHere
F i HCM P di i A l i
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Fusion HCM Predictive Analytics
Fusion HCM Predictive Analytics
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Fusion HCM Predictive Analytics
Fusion CRM Sales Predictor
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Which prospectsmost resemblethose customers?
Fusion CRM Sales PredictorSales Predictor Optimizes CRM Processes
Products
Customers
Pipeline
Which types ofcustomers arebuying which
products?
Am I positioningthe right products
to the rightcustomers at the
right price?
SalesManagement
Right recommendations to the right customers
T diti l A h C l d Di j i t d
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Todays
Process
Data Mining
Modeling
Spreadsheet
Targeting
Reporting
ForesightsAnalysis
Expert Insight
Customer Outreach
Traditional Approaches are Complex and Disjointed
Streamline Sales Planning with Intelligent
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Predicted Markets
Streamline Sales Planning with IntelligentPredictions
Continuous
Learning
Simulation
Statistical Models
Mine historicalsales data touncover patterns
Integrated reportingfor analysis
Prediction Rules
Capture expert insightthrough powerful ruleeditor
Driverecommendations
without historical data
Sales Predictor
Fusion CRM Sales Predictor
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Sales Predictor Maximizes Account Potential
Next Best Products
for Customer
Fusion CRM Sales Predictor
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Recommendation Work Area Overview
This is the landing page of theRecommendation Workarea inwhich the Sales Analyst can
access a list of tasksassociated with Sales Predictor
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Getting Started
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Getting Started
Oracle Data Miner Cue Cardspart of client install Oracle By Example Online Learning on OTN
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http://search.oraclecorp.com/search/searchRecommended