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This presentation is for informational purposes only and may not be incorporated into a contract or agreement.
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The following is intended to outline our general product direction. Itis intended for information purposes only, and may not be
incorporated into any contract. It is not a commitment to deliver anymaterial, code, or functionality, and should not be relied upon in
making purchasing decision. The development, release, and timingof any features or functionality described for Oracles products
remains at the sole discretion of Oracle.
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Copyright 2006 Oracle Corporation
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Copyright 2006 Oracle Corporation
Data Warehousing
ETL
OLAP
Data Mining
Oracle 10Oracle 10 g g DBDB
Statistics
Oracle In-Database Advanced AnalyticsStatistics, Data Mining, Text Mining, & More!
Charlie BergerSr. Dir. Product Management, Life & Health Sciences Industry & Data Mining TechnologiesOracle [email protected]
mailto:[email protected]:[email protected]8/8/2019 Pres Db Data Mining 10gr2 0806
5/61Copyright 2006 Oracle Corporation
Agenda
Oracle Data Mining Overview Demos
Oracle Data Mining Integration with Oracle BI EE Spreadsheet Add-in for Predictive Analytics Text Mining
Code Generation Release In-Database Analytics Example
Comparison to SAS Partners Summary
Data Warehousing
ETL
OLAP
Data Mining
Oracle 10Oracle 10 g g DBDB
Statistics
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The Evolving Role of BI
Analysts
Historical data
Reporting results
From: To:
Pervasive use
Real-time, predictive data
Insight-driven business
process optimization
Unified, enterprise viewFragmented view
Unified infrastructure &prebuilt analytic solutions
Analytic tools
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What is Data Mining?
Process of sifting through massive amountsof data to find hidden patterns and discovernew insights
Data Mining can provide valuable results: Identify factors more associated with a target
attribute (Attribute Importance) Predict individual behavior (Classification) Find profiles of targeted people or items
(Decision Trees)
Segment a population (Clustering) Determine important relationships with the
population (Associations) Find fraud or rare events (Anomaly Detection)
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Business Intelligence & Analytics
Knowledge discoveryof hidden patterns
Who will buy a mutualfund in the next 6months and why?
Extraction ofdetailed androll up data
Who purchasedmutual funds inthe last 3 years?
Summaries,trends andforecasts
What is theaverageincome ofmutual fundbuyers, byregion, by year?
Queryand Reporting OLAP Data Mining
Insight & Prediction Information Analysis
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Copyright 2006 Oracle Corporation
Example Data Mining ApplicationsFinancial Services Combat attrition (churn)
Fraud detection Loan default (Basel II)
Identify selling opportunities
Database Marketing Buy product x More targeted & successful
campaigns Identify cross-sell & up-sell
opportunitiesTelecommunications
Identify customers likely to leaveTarget highest lifetime valuecustomers
Identify cross-sell opportunities
Insurance, Government Flag accounting anomalies
(Sarbanes-Oxley) Reduce cost of investigating
suspicious activity or false claims
Retail Loyalty programs Cross-sell Market-basket analysis Fraud detection
Life Sciences Find factors associated withhealthy or unhealthy patients Discover gene and protein targets Identify leads for new drugs
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Copyright 2006 Oracle Corporation
Oracle Data Mining 10gR2Oracle in-Database Mining Engine
Oracle Data Miner (GUI) Simplified, guided data mining
Spreadsheet Add-In for Predictive Analytics
1-click data mining from a spreadsheet PL/SQL API & Java (JDM) API Develop advanced analytical applications
Wide range of algorithms Anomaly detection Attribute importance Association rules
Clustering Classification & regression Nonnegative matrix factorization Structured & unstructured data (text mining) BLAST (life sciences similarity search algorithm)
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Copyright 2006 Oracle Corporation
Customer References"...Because Data Mining algorithms and the data arehoused together in the Oracle database, we don't have tomove huge data sets to external programs to run thealgorithms and learn something about our dataThe fact
that it cost about 75 percent less than the leadingcompetitor didn't hurt either "-- Tracy E. Thieret, Ph.D. Principal Scientist Xerox Innovation Group Imaging andSolutions Technology Center
Walter Reed Medical CenterUsing Oracle Data Mining, medical researchers arediscovering trends and patterns that will improve the healthcare for millions of people around the globe.--Dr. Carolyn Hamm, Director of Decision Support, Walter Reed Medical Center.Saving Lives with Oracle
IRS Detecting taxpayer noncompliance
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Copyright 2006 Oracle Corporation
Customer References
"Oracle Data Mining will allow us to pinpoint the mostimportant attributes of law school applicants thatcorrelate to successful legal careers. We will mine ourapplicant pool to seek our benefactors and trustees oftomorrow, therefore these strategic tools are critical toour long-term success. The security and scalability ofOracle's in-database mining, as well as its seamless
integration with our business intelligence platform weredeciding factors in selecting Oracle over analyticalalternatives."
-- Tom Delaney, CIO New York University School of Law
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Copyright 2006 Oracle Corporation
Data Warehousing
ETL
OLAP
Data Mining
Oracle 10Oracle 10 g g DBDB
Statistics
Oracle Data Mining 10gD E M O N S T R A T I O N
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Copyright 2006 Oracle Corporation
Oracle Data Mining Oracle Data Mining providessummary statistical informationprior to data mining
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Copyright 2006 Oracle Corporation
Oracle Data Mining
Oracle DataMiningsActivity
Guidessimplify &automatedata miningfor businessusers
Oracle Data Mining providesmodel performance andevaluation viewers
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Copyright 2006 Oracle Corporation
Oracle Data Mining
Additional model
evaluation viewers
Additional model
evaluation viewers
Apply model
viewers
E l #1
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Copyright 2006 Oracle Corporation
Example #1:Simple, Predictive SQL
Select customers who are more than 60% likely to
purchase a 6 month CD and display their maritalstatus
SELECT * from(SELECT A.CUST_ID, A.MARITAL_STATUS,PREDICTION_PROBABILITY(CD_BUYERS76485_DT, 1USING A.*) prob
FROM CBERGER.CD_BUYERS A)WHERE prob > 0.6;
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Real-time Predictionwithrecords as (select
178255 ANNUAL_INCOME,0 CAPITAL_GAIN,83 SAVINGS_BALANCE,246 AVE_CHECKING_BALANCE,30 AGE,'Bach.' EDUCATION,'SelfENI' WORKCLASS,'Married' MARITAL_STATUS,'Sales' OCCUPATION,'Husband' RELATIONSHIP,'White' RACE,'Male' SEX,70 HOURS_PER_WEEK,'?' NATIVE_COUNTRY,98 PAYROLL_DEDUCTION from dual)
select s.prediction prediction, s.probability probabilityfrom (
select PREDICTION_SET( CD_BUYERS76485_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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Copyright 2006 Oracle Corporation
Real-time Prediction Multiple Modelswith records as (select
178255 ANNUAL_INCOME,
0 CAPITAL_GAIN,83 SAVINGS_BALANCE,246 AVE_CHECKING_BALANCE,
30 AGE,'Bach.' EDUCATION,'SelfENI' WORKCLASS,'Married' MARITAL_STATUS,'Sales' OCCUPATION,'Husband' RELATIONSHIP,'White' RACE,'Male' SEX,70 HOURS_PER_WEEK,'?' NATIVE_COUNTRY,98 PAYROLL_DEDUCTION from dual)
select t.*from (
select 'CAR_MODEL' MODEL , s1.prediction prediction, s1.probability probability,s1.probability*25000 as expected_revenue from (
select PREDICTION_SET(NBMODEL_JDM, 1 USING *) psetfrom records ) t1, TABLE(t1.pset) s1
UNIONselect 'MOTOCYCLE_MODEL' MODEL , s2.prediction prediction, s2.probability probability,
s1.probability*2000 as expected_revenue from (select PREDICTION_SET(ABNMODEL_JDM, 1 USING *) psetfrom records ) t2, TABLE(t2.pset) s2
UNION
select 'TRICYCLE_MODEL' MODEL , s3.prediction prediction, s3.probability probability,s1.probability*50 as expected_revenue from (select PREDICTION_SET(TREEMODEL_JDM, 1 USING *) psetfrom records ) t3, TABLE(t3.pset) s3
UNIONselect 'BICYCLE_MODEL' MODEL , s4.prediction prediction, s4.probability probability,
s1.probability*200 as expected_revenue from (select PREDICTION_SET(SVMCMODEL_JDM, 1 USING *) psetfrom records ) t4, TABLE(t4.pset) s4
) torder by t.expected_revenue desc;
On-the-fly, single recordapply with multiple
models; sort by
expected revenues
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Copyright 2006 Oracle Corporation
Predictive Analytics: ExplainPL/SQL Package
BEGINDBMS_PREDICTIVE_ANALYTICS.EXPLAIN(
data_table_name => 'CD_BUYERS',
explain_column_name => 'CD_BUYER',result_table_name => 'explain_result37');
END;/SELECT * FROM explain_result37;
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Predictive Analytics: PredictPL/SQL Package
SET serveroutput ON
DECLAREv_accuracy NUMBER(10,9);
BEGINDBMS_PREDICTIVE_ANALYTICS.PREDICT ( ACCURACY => v_accuracy,DATA_TABLE_NAME => 'CD_BUYERS',CASE_ID_COLUMN_NAME => 'CUST_ID',TARGET_COLUMN_NAME => 'CD_BUYER',RESULT_TABLE_NAME => 'predict_result24');
DBMS_OUTPUT.PUT_LINE('Accuracy = ' || v_accuracy);END;
/SELECT * FROM predict_result24 WHERE rownum
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Copyright 2006 Oracle Corporation
Example #2Launch & Evaluate a Marketing Campaign
select responder, cust_region, count(*) as cnt,sum(post_purch pre_purch) as tot_increase,avg(post_purch pre_purch) as avg_increase,stats_t_test_paired(pre_purch, post_purch) as
significancefrom (
select cust_name, prediction(campaign_model using *) as responder,
sum(case when purchase_date < 15-Apr-2005 then purchase_amt else 0 end) as pre_purch,
sum(case when purchase_date >= 15-Apr-2005 then purchase_amt else 0 end) as post_purch
from customers, sales, products@PRODDBwhere sales.cust_id = customers.cust_id
and purchase_date between 15-Jan-2005 and 14-Jul-2005
and sales.prod_id = products.prod_idand contains(prod_description, DVD) > 0
group by cust_id, prediction(campaign_model using *) )group by rollup responder, cust_region order by 4 desc;
1.Given a previouslybuilt responsemodel, predictwho will respond toa campaign,and why
2.find out howmuch eachcustomer spent 3months before andafter the campaign
3.how much for
just DVDs ?4. Is the success
statisticallysignificant?
O l D t Mi i
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Copyright 2006 Oracle Corporation
Oracle Data MiningAlgorithms & Example Applications
Attribute Importance Identify most influential attributes
for a target attribute Factors associated with high costs,
responding to an offer, etc.Classification and Prediction Predict customers most likely to:
Respond to a campaign or offer Incur the highest costs
Target your best customers Develop customer profiles
Regression Predict a numeric value
Predict a purchase amount or cost Predict the value of a home
A1 A2 A3 A4 A5 A6 A7
Income
Gender
Status Gender HH Size
>$50K 4
Age
Buy = 0 Buy = 1 Buy = 1 Buy = 0
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Copyright 2006 Oracle Corporation
Oracle Data MiningAlgorithms & Example Applications
Clustering Find naturally occurring groups
Market segmentation
Find disease subgroups Distinguish normal from non-normal behavior
Association Rules Find co-occurring items in a market basket
Suggest product combinations Design better item placement on shelves
Feature Extraction Reduce a large dataset into representative
new attributes Useful for clustering and text mining
F1 F2 F3 F4
Oracle Data Mining
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Oracle Data MiningAlgorithms & Example Applications
Text Mining Combine data and text for better models
Add unstructured text e.g. physicians notes to
structured data e.g. age, weight, height, etc., topredict outcomes
Classify and cluster documents Combined with Oracle Text to develop
advanced text mining applications e.g. Medline
BLAST Sequence matching and alignment
Find genes and proteins thatare similar
ATGCAATGCCAGGATTTCCA
CTGCAA GGCCAGGA AG TTCCAATGC GT TGCCA C ATTTCCA
GGC.. TGCAATGCCAGGAT GA CCAATGCAATG TT AGGA CC TCCA
Oracle Data Mining 10g R2
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Copyright 2006 Oracle Corporation
Oracle Data Mining 10g R2Decision Trees
Problem: Find customerslikely to buy a new car andtheir profiles Decision Trees
Classification
Prediction Customerprofiling
Income
Gender
Status Gender HH Size
>$50K 4
Age
Buy = 0 Buy = 1 Buy = 1 Buy = 0
50K AND Gender=F AND Status >Single ), THEN P(Buy Car=1)Confidence= .77
Support = 250
Oracle Data Mining 10g R2
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Copyright 2006 Oracle Corporation
Oracle Data Mining 10g R2Anomaly Detection
Problem: Detectrare cases One-Class SVM Models
Fraud, noncompliance
Outlier detection Network intrusion detection Disease outbreaks Rare events, true novelty
X2X1
X2X1
Oracle Data Mining
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Copyright 2006 Oracle Corporation
Oracle Data MiningAlgorithm Summary 10gR2
Classification
Association Rules
Clustering
Attribute Importance
Problem Algorithm Applicability
Adaptive Bayes Network
Nave BayesPopular / Rules / transparency
Embedded app
Minimum DescriptionLength (MDL)
Attribute reductionIdentify useful dataReduce data noise
Hierarchical K-Means
Hierarchical O-Cluster
Product groupingText miningGene and protein analysis
AprioriMarket basket analysisLink analysis
Support Vector Machine Wide / narrow data
Support Vector Machine Wide / narrow dataRegression
Feature Extraction NMFText analysis
Feature reduction
Decision Tree
Rules / transparency
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Copyright 2006 Oracle Corporation
Integration with Oracle BI EE
Likelihood to buy
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i i h O l i
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Integration with Oracle Discoverer
Copyright 2006 Oracle Corporation
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Spreadsheet Add-In for Predictive Analytics
Enables Excelusers to mineOracle or Exceldata using oneclick Predict andExplain predictiveanalytics features
Users select a tableor view, or point todata in Excel, and
select a targetattribute
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Copyright 2006 Oracle Corporation
Oracle Data Mining & Oracle Text
Oracle Data Miningmines text to buildclassification and
clustering models Oracle Text(included in Oracle DatabaseStandard Edition)
preprocessesunstructured text
Handles large
volumes ofdocuments or text
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Data Warehousing
ETL
OLAP
Data Mining
Oracle 10Oracle 10 g g DBDB
Statistics
Oracle Data Miner 10gR2Code Generation Release
Oracle Data Miner (gui)10 R2 S OTN R l
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10gR2 Summer OTN Release
PL/SQL codegeneration for
Mining Activities
Oracle Data Miner (gui)10 R2 S OTN R l
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10gR2 Summer OTN Release
Oracle Data Miner (gui)10 R2 S OTN R l
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10gR2 Summer OTN Release
Oracle Data Miner (gui)10 R2 S OTN R l
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10gR2 Summer OTN Release
Oracle Data Miner (gui)10gR2 S mmer OTN Release
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10gR2 Summer OTN Release
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Data Warehousing
ETL
OLAP
Data Mining
Oracle 10Oracle 10 g g DBDB
Statistics
In-Database Analytics
Example
Example #1T M k i C i
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Test a Marketing Campaign
Given a previously built response model
(classification), predict who will respond tothe campaign, and why
Example #1P di t R d
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Predict Responders
select cust_name, prediction(campaign_model using *)
as responder, prediction_details(campaign_model using *)
as reasonfrom customers;
Example #1Combine with Relational Data
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Combine with Relational Data
In addition to predicting responders, find
out how much each customer has spentfor a period of 3 months before and afterthe start of the campaign
Example #1Combine with Relational Data
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Combine with Relational Data
select cust_name, prediction(campaign_model using *) as
responder,
sum(case when purchase_date < 15-Apr-2005 then purchase_amt else 0 end) as pre_purch,sum(case when purchase_date >= 15-Apr-2005
then purchase_amt else 0 end) as post_purch
from customers , saleswhere sales.cust_id = customers.cust_id
and purchase_date between 15-Jan-2005 and 14-Jul-2005
group by cust_id, prediction(campaign_model using *);
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Example #1Multi-Domain Multi-DB data
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Copyright 2006 Oracle Corporation
Multi-Domain, Multi-DB data
select cust_name, prediction(campaign_model using *) as responder,
sum(case when purchase_date < 15-Apr-2005 then purchase_amt else 0 end) as pre_purch,
sum(case when purchase_date >= 15-Apr-2005 then purchase_amt else 0 end) as post_purch
from customers, sales , products@PRODDBwhere sales.cust_id = customers.cust_id
and purchase_date between 15-Jan-2005 and 14-Jul-2005and sales.prod_id = products.prod_idand contains(prod_description, DVD) > 0
group by cust_id, prediction(campaign_model using *);
Example #1Test Effectiveness / Significance
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Copyright 2006 Oracle Corporation
Test Effectiveness / Significance
In addition to predicting responders, find out howmuch each customer has spent on DVDs for a
period of 3 months before and after the start ofthe campaign, and Compare the success rate of predicted
responders and non-responders within differentregions and across the company
Is the success statistically significant?
Example #1Test Effectiveness / Significance
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Copyright 2006 Oracle Corporation
Test Effectiveness / Significance select responder, cust_region, count(*) as cnt,
sum(post_purch pre_purch) as tot_increase,avg(post_purch pre_purch) as avg_increase,stats_t_test_paired(pre_purch, post_purch) as
significance
from (select cust_name,
prediction(campaign_model using *) as responder,sum(case when purchase_date < 15-Apr-2005 then
purchase_amt else 0 end) as pre_purch,sum(case when purchase_date >= 15-Apr-2005 then
purchase_amt else 0 end) as post_purchfrom customers, sales, products@PRODDBwhere sales.cust_id = customers.cust_id
and purchase_date between 15-Jan-2005 and 14-Jul-2005and sales.prod_id = products.prod_idand contains(prod_description, DVD) > 0
group by cust_id, prediction(campaign_model using *) )group by rollup responder, cust_region order by 4 desc;
Analytics vs.
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Copyright 2006 Oracle Corporation
1. In-Database Analytics EngineBasic Statistics (Free)Data MiningText Mining
2. DevelopmentPlatform
Java (standard)
SQL (standard)J2EE (standard)
3. Costs (ODM: $20K cpu)Simplified environmentSingle serverSecurity
1. External Analytical EngineBasic StatisticsData MiningText Mining (separate: SAS EM for Text)Advanced Statistics
2. DevelopmentPlatform
SAS Code (proprietary)
3. Costs (SAS EM: $150K/5 users)Annual Renewal Fee
(~40% each year)
Data Warehousing
ETL
OLAP
Data Mining
Oracle 10Oracle 10 g g DBDB
Statistics
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Advanced Analytics Partners
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Advanced Analytics Partners
Benefits of Oracles Approach
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Copyright 2006 Oracle Corporation
In-Database Analytics Benefit Platform for AnalyticalApplications
Eliminates data movement andsecurity exposure
Fastest: Data Information
Wide range of data miningalgorithms & statisticalfunctions
Supports most analyticalproblems
Runs on multiple platforms Applications may be developedand deployed
Built on Oracle Technology Grid, RAC, integrated BI, SQL & PL/SQL available Leverage existing skills
Oracle 10Oracle 10 gg DBDBOracle Advanced Analytics
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Copyright 2006 Oracle Corporation
Know More! Leverage your data, discover new hidden
information and valuable insights, and makepredictions
Do More! Build applications that automate the extraction and dissemination
of data minings insights Move from End User Tool to Enterprise BI Application
Spend Less! Option to Oracle 10g Database Enterprise Edition Eliminates need for redundant data, new servers, new software,
and new support skills/resources
Data Warehousing
ETL
OLAP
Data Mining
Oracle 10Oracle 10 g g DBDB
Statistics
y
For More Information
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Oracle Business Intelligence Solutions oracle.com/bi
Oracle Data Mining 10g oracle.com/technology/products/bi/odm/index.html
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Q U E S T I O N SQ U E S T I O N S
A N S W E R SA N S W E R S
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This presentation is for informational purposes only and may not be incorporated into a contract or agreement.