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oracleadvancedanalyticsv2otn-2859525.pptx
1.
Copyright © 2016
Oracle and/or its affiliates. All rights reserved. | Charlie Berger, MS Engineering, MBA Sr. Director Product Management, Data Mining and Advanced Analytics charlie.berger@oracle.com www.twitter.com/CharlieDataMine Oracle’s Advanced Analytics Make BigData +AnalyticsSimple
2.
Copyright © 2016
Oracle and/or its affiliates. All rights reserved. Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. 2
3.
Data Analysis platforms requirements: • Be
extremely powerful and handle large data volumes • Be easy to learn • Be highly automated & enable deployment Data, data everywhere Growth of Data Exponentially Greater than Growth of Data Analysts! Copyright © 2016 Oracle and/or its affiliates. All rights reserved. http://www.delphianalytics.net/more-data-than-analysts-the-real-big-data- problem/ http://uk.emc.com/collateral/analyst-reports/ar-the-economist-data-data- everywhere.pdf
4.
Analytics + Data
Warehouse + Hadoop Copyright © 2016 Oracle and/or its affiliates. All rights reserved. • Platform Sprawl – More Duplicated Data – More Data Movement Latency – More Security challenges – More Duplicated Storage – More Duplicated Backups – More Duplicated Systems – More Space and Power
5.
Visio n • Big Data
+Analytic Platform for the Era of Big Data and Cloud – Make BigData +AnalyticsModel DiscoverySimple • Any data size, on any computer infrastructure • Any variety of data (structured, unstructured, transactional, geospatial), in any combination – Make BigData +AnalyticsModel DeploymentSimple • As a service, as a platform, as an application Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
6.
Scalable in-Database
+ Hadoop data mining algorithms and R integration Powerful predictive analytics and deployment platform Drag and drop workflow, R and SQL APIs Data analysts, data scientists & developers Enables enterprise predictive analytics applications KeyFeatures Oracle’s Advanced Analytics Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
7.
Data remains in
Database & Hadoop Model building and scoring occur in- database Use R packages with data-parallel invocations Leverage investment in Oracle IT Eliminate data duplication Eliminate separate analytical servers Fastest way to deliver enterprise- wide predictive analytics GUI for Predictive Analytics and code generation R interface leveraging database as HPC engine KeyFeatures Oracle’s Advanced Analytics avings Model“Scoring” EmbeddedDataPrep Data Preparation ModelBuilding OracleAdvanced Analytics Secs,Mins or Hours TraditionalAnalytics Hours,Daysor Weeks DataExtraction Data Prep& T ransformation DataMining Model Building Data Mining Model “Scoring” Data Prep.& T ransformation Data Import Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
8.
OBIEE OracleDatabaseEnterprise Edition Oracle’s AdvancedAnalytics OracleAdvancedAnalytics-
Database Option SQLData Mining & Analytic Functions +RIntegration for Scalable, Distributed, Parallel in-Database ML Executio SQL Developer/ Oracle Data Miner Applications R Client Multiple interfaces across platforms —SQL, R, GUI, Dashboards, Apps Users R programmers Data & Business Analysts Business Analysts/Mgrs Domain End Users Platfor m OracleDatabase n 12c Hadoop ORAAH Parallel, distributed algorithms OracleCloud Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
9.
Oracle Advanced Analytics
Database Evolution Analytical SQL in the Database 2002 2004 2005 2008 2011 2014 • 7 Data Mining “Partners” 1998 • Oracle acquires Thinking Machine Corp’s dev. team + “Darwin” data mining software 1999 Miner “Classic” wizards driven GUI • SQL statistical functions introduced • New algorithms (EM, PCA, SVD) • Predictive Queries • SQLDEV/Oracle Data Miner 4.0 SQL script generation and SQL Query node (R integration) scalable R algorithms • Oracle Adv. Analytics for Hadoop Connector launched with scalable BDA algorithms • Oracle Data Mining 10gR2 SQL - 7 new SQL dm algorithms • Oracle Data Mining and new Oracle Data 9.2i launched – 2 algorithms (NB and AR) via Java API • ODM 11g & 11gR2 adds AutoDataPrep (ADP), text mining, perf. improvement•sOAA/ORE 1.3 + 1.4 • SQLDEV/Oracle Data Mineradds NN, Stepwise, 3.2 “work flow” GUI launched • Integration with “R” and introduction/addition of Oracle R Enterprise • Product renamed “Oracle Advanced Analytics (ODM + ORE) Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
10.
YouCanThink of Oracle’s
Advanced Analytics Like This… – “Human-driven” queries – Domain expertise – Any “rules” must be definedand managed SQLQueries – SELECT – DISTINCT – AGGREGATE – WHERE – AND OR – GROUP BY – ORDER BY – RANK Traditional SQL OracleAdvancedAnalytics- SQL & – Automated knowledge discovery, model building and deployment – Domain expertise to assemblethe“right” data to mine/analyze Analytical SQL“Verbs” – PREDICT – DETECT – CLUSTER – CLASSIFY – REGRESS – PROFILE – IDENTIFY FACTORS – ASSOCIATE + Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
11.
Multiple Data Sources/Types
with Predictive Modeling Ease of Deployment through SQL Script Generation Conside r: • Demographics • Past purchases • Recent purchases • Customer comments & tweets Unstructured data also mined by algorithms Transaction al POS data Generates SQL scripts for deployment Inline predictive model to augment input data SQL Joins and arbitrary SQL transforms & queries – power of SQL Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
12.
UK National Health Service Combating
Healthcare Fraud Objectives Use new insight to help identify cost savings and meet goals Identify and prevent healthcare fraud and benefit eligibility errors to save costs Leverage existing data to transform business and productivity Solution Identified up to GBP100 million (US$156 million) potentially saved through benefit fraud and error reduction Used anomaly detection to uncover fraudulent activity where some dentists split a single course of treatment into multiple parts and presented claims for multiple treatments Analyzed billions of records at one time to measure longer- term patient journeys and to analyze drug prescribing patterns to improve patient care “Oracle Advanced Analytics’ data mining capabilities andOracle Exalytics’ performance really impressed us. The overall solution is very fast, and our investment very quickly provided value. We can now do so much more with our data, resulting in significant savings for the NHS as a whole” – Nina Monckton, Head of Information Services, NHS Business ServicesAuthority OracleExadataDatabase Machine OracleAdvanced Analytics OracleExalyticsIn-Memory Machine OracleEndecaInformation Discovery OracleBusinessIntelligence EE Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
13.
Turkcell Combating Communications Fraud Objectives Prepaid
card fraud—millions of dollars/year Extremely fast sifting through huge data volumes; with fraud, time is money Solution Monitor 10 billion daily call-data records Leveraged SQL for the preparation—1 PB Due to the slow process of moving data, Turkcell IT builds and deploys models in-DB Oracle Advanced Analytics on Exadata for extreme speed. Analysts can detect fraud patterns almost immediately “Turkcell manages 100 terabytes of compressed data—or one petabyte of uncompressed raw data—on Oracle Exadata. With Oracle Data Mining, a component of the Oracle Advanced Analytics Option, we can analyze large volumes of customer data and call-data records easier and faster than with any other tool and rapidly detect and combat fraudulent phone use.” – Hasan Tonguç Yılmaz, Manager, Turkcell İletişim HizmetleriA.Ş. OracleAdvanced Analytics In-Database FraudModels Exadata Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
14.
Copyright © 2016
Oracle and/or its affiliates. All rights reserved. Oracle Advanced Analytics—On Premise or Cloud 100% Compatibility Enables Easy Coexistence and Migration 14 OracleCloud CoExistenceand Migration SameArchitecture SameAnalytics SameStandards T ransparently move workloadsand analytical methodologies betwee n On-premiseandpublic cloud On-Premise
15.
Predictive Analytics &
Data Mining Typical Use Cases • Targeting the right customer with the right offer • How is a customer likely to respond to an offer? • Finding the most profitable growth opportunities • Finding and preventing customer churn • Maximizing cross-business impact • Security and suspicious activity detection • Understanding sentiments in customer conversations • Reducing medical errors & improving quality of health • Understanding influencers in social networks Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
16.
Automatically sifting through
large amounts of data to create models that find previously hidden patterns, discover valuable new insights and make predictions •Identify most important factor (Attribute Importance) •Predict customer behavior (Classification) •Predict or estimate a value (Regression) •Find profiles of targeted people or items (Decision Trees) •Segment a population (Clustering) •Find fraudulent or “rare events” (AnomalyDetection) •Determine co-occurring items in a“baskets” (Associations) What is Data Mining & Predictive Analytics? A1 A2 A3 A4 A5 A6 A7 Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
17.
Data Mining Provides Customer Months Better Information,
Valuable Insights and Predictions LeaseChurners vs. Loyal Customers Insight & Prediction Segment #1 IF CUST_MO > 14 AND INCOME < $90K, THENPrediction = Lease Churner Confidence = 100% Support = 8/39 Segment #3 IF CUST_MO > 7 AND INCOME < $175K, THEN Prediction = Lease Churner, Confidence = 83% Support = 6/39 Source: Inspired from Data Mining Techniques: ForMarketing, Sales,and CustomerRelationship Management by Michael J. A. Berry, Gordon S. Linoff Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Oracle Advanced Analytics:
Supervised Learning “Classification” Decision TreeAlgorithm • Profiling with discovered If… Then… “rules” • Prediction probabilitie s >4 5 <4 5 Manua l Automati c <=3 5 >3 5 Ag e <25 >25 M Buy Camry= 0 Buy Camry= 1 Camry= 0 Camry= 1 Camry= 0 Camry= 1 Ag e Car Type Mp g Gende r Fast Accelerations F >4 <=4 Simplemodel: Could include unstructured data (e.g. voice of customer comments), transactions data (e.g. geospatial & filed performance data), etc. IF (Age >45ANDCARTYPE=Automatic ANDMpg= >25) THEN Probability(Buy Camry) =.77 and Support =25,500 cases Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Oracle’s AdvancedAnalytics In-Database Data
MiningAlgorithms*—SQL& & GUIAccess • Decision Tree • Logistic Regression (GLM) • Naïve Bayes • Support Vector Machine (SVM) • Random Forest Regression • Multiple Regression (GLM) • Support Vector Machine (SVM) • Linear Model • Generalized Linear Model • Multi-Layer Neural Networks • Stepwise Linear Regression AttributeImportance • Unsupervised pair-wise KL div. g •Hierarchical k-Means •Orthogonal Partitioning Clusterin •Expectation-Maximization FeatureExtraction& Creation •Nonnegative Matrix Factorizatio n •Principal Component Analysis •Singular Value Decomposition • Apriori – Association Rules Anomaly Detection •1 Class Support Vector Machine Time Series • Single & Double Exp. Smoothing Classification Clustering Market BasketAnalysis •Clustering •Regression •Anomaly Detection •Feature Extraction Predictive Queries •Ability to run any R package via Embedded R mode OpenSourceRAlgorithms * supports partitioned models, text mining A1 A2 A3 A4 A5 • Minimum Description Length A6 A7 Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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• R language
for interaction with the database • R-SQL Transparency Framework overloads R functions for scalable in- database execution • Function overload for data selection, manipulation and transforms • Interactive display of graphical results and flow control as in standard R • Submit user-defined R functions for execution at database server under control of Oracle Database • 15+ Powerful data mining algorithms (regression, clustering, AR, DT, etc._ • Run Oracle Data Mining SQL data mining functioning (ORE.odmSVM, ORE.odmDT, etc.) • Speak“R” but executes asproprietary in- database SQL functions—machine learning algorithms and statistical functions • Leverage database strengths: SQL parallelism, scale to large datasets, security Other R packag es Oracle R Enterprise (ORE) packages R->SQLTransparency“Push-Down” • R Engine(s) spawned by Oracle DB for database-managed parallelism • ore.groupApply high performance scoring • Efficient data transfer to spawned R engines • Emulate map-reduce style algorithms and applications • Enables production deployment and automated execution of R scripts OracleDatabase12c R-> SQL Result s In-DatabaseAdvAnalytical SQLFunctions R Engine Other R packag es Oracle R Enterprise packages EmbeddedRPackageCallouts R Result s Oracle AdvancedAnalytics How Oracle R Enterprise Compute Engines Work 1 2 3 Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. | Oracle Advanced Analytics Brief Demos
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Oracle’sAdvancedAnalytics ExampleCustomer References Copyright ©
2016 Oracle and/or its affiliates. All rights reserved. |
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Fiser v Risk Analytics in
Electronic Payments Objectives Prevent $200M in losses every year using data to monitor, understand and anticipate fraud Solution We installed OAA analytics for model development during 2014 When choosing the tools for fraud management, speed is a critical factor OAA provided a fast and flexible solution for model building, visualization and integration with production processes “When choosing the tools for fraud management, speed is a critical factor. Oracle Advance Analytics provided a fast and flexible solution for model building, visualization and integration with production processes.” – Miguel Barrera, Director of Risk Analytics, FiservInc. – Julia Minkowski, Risk Analytics Manager, Fiserv Inc. 3months to run & deploy Logistic Regression (usingSAS) 1month to estimate and deploy Trees and GLM Copyright © 2016 Oracle and/or its affiliates. All rights reserved. | 1 weekto estimate, 1 weektoinstall rules in online application 1 dayto estimateand deploy Trees + GLM models (using Oracle Advanced Analytics) Oracle Advanced Analytics
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© 2014 Fiserv,
Inc. or its affiliates. Data Miner Survey 2016 by Rexer Analytics While 6 out 10 data miners report the data is available for analysis within days of capture, the time to deploy the models takes substantially longer. For 60% of the respondents the deployment time will range between 3 weeks and 1year. Everyone forgets about deployment – but is most important component! Ease of Deployment
37.
Copyright © 2016
Oracle and/or its affiliates. All rights reserved. Market Basket & Advanced Analytics at Dunkin Brands Objectives Store development dashboards to identify opportunities 8 M daily transactions, ~25M transaction detail lines 20 TB data warehouse size, sales data about 10 TB Market basket analysis and customer loyalty & segmentation Solution Exadata Engineered Systesm Oracle Advanced Analytics Option Market Basket Analysis, Clustering, Classification, Segmentation, Loyalty Analysis “Exponential growth in combinations with each hierarchy. 2 years of pre-computed Market Baskets and associated sales measures for reporting. Nightly compute within ETL window data with 1 day latency.” – Dunkin Brands, Mahesh Jagannath, Senior Manager, Business Intelligence (Excerpts from Dunkin Brands presentation at Oracle Open World2014) Excerpts from Dunkin Brands presentation at Oracle Open World 2014 https://oracleus.activeevents.com/2014/connect/fileDownload/session/82C7DB4F27CC2C97F29D8592754E4216/CON6545_Jagannath- OOW2014MarketBasketAndAdvancedAnalyticsAtDunkinBrands10012014Final.pptx Clusterin g Algorith m Custome r Data Profiles
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. | DX Marketing Cloud Based Predictive Analytics/Database Marketing Objectives Solution “Time to market has significantly improved from 4-6 weeks to less than a week with the result the company can bring new clients on board faster. This has helped boost revenues by 25% in the six months since using Oracle’s DBCS..” – DX Marketing OracleCloud Oracle Advanced Analytics Cloud-based solution Increase revenue Reduce time-to- market
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Accelerates Complex Segmentation
Queries from Weeks to Minutes—Gains Competitive Advantage Objectives World’s leading customer-science company Accelerate analytic capabilities to near real time using Oracle Advanced Analytics and third-party tools, enabling analysis of unstructured big data from emerging sources, like smart phones Solution Accelerated segmentation and customer-loyalty analysis from one week to just four hours—enabling the company to deliver more timely information & finer-grained analysis Generated more accurate business insights and marketing recommendations with the ability to analyze 100% of data— including years of historical data— instead of just a small sample “Improved analysts’ productivity and focus as they can now run queries and complete analysis without having to wait hours or days for a query to process” “Improved accuracy of marketing recommendations by analyzing larger sample sizes and predicting the market’s reception to new product ideas and strategies” – dunnhumby Oracle Customer Snapshot ) Customer Snapshot: http://www.oracle.com/us/corporate/customers/customersearch/dunnhumby-1-exadata-ss- 2137635.html Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. | Oracle’sAdvancedAnalytics Differentiators
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Oracle Advanced Analytics Differentiators •
Data remains in the Database and Hadoop – Brings algorithms to the data, not move data to the algorithms – Oracle’sadvanced analytics parallel implementations run on Hadoop and Databasedata – Provides parallel implementations of commonly used algorithms with auto-data preparation – Enables data analysts and scientists to work directly on transactional data, star schemas, text • Leverage investment in Oracle IT – Leverageslatest Oracletechnology: Engineered Systems(Exadata,BDA),BigData SQL,“smart scans” – Provides wide range of options: Cloud and on-premise – same Oracle code & products – Database provides industry leadership in data security, encryption, audit, backup, and recovery – Diverse data source gateways, social media data, geo-coding, ETL – on-premise / on-cloud Copyright © 2016 Oracle and/or its affiliates. All rights reserved. 4
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Oracle Advanced Analytics Differentiators •
Fastest way to deliver enterprise-wide predictive analytics applications – Delivers big data + analytics platform for model build, deployment & applications – GUI to build analytical workflows with code generation and scheduling for deployment – Supports near real-time scoring for embedding in applications – Explains model logic and transparency via Prediction_Details • Tight integration with open source R – Leverage CRAN R packages using data- and task-parallel database infrastructure – Store and manage R scripts and R objects in-database and invoke R scripts from SQL – Use production quality infrastructure without custom plumbing or extra complexity Copyright © 2016 Oracle and/or its affiliates. All rights reserved. 4
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SQL Developer/Oracle Data
Miner 4.0 New Features SQLScriptGeneration – Deploy entire methodology as a SQL script – Immediate deployment of dataanalyst’s methodologies R Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Fraud Prediction Demo Automated
In-DB Analytical Methodology drop 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; POLICYNUMBER PERCENT_FRAUDRNK ------------ ------------- ---------- 6532 64.78 1 2749 64.17 2 3440 63.22 3 654 63.1 4 12650 62.36 5 begin dbms_data_mining.create_model('CLAIMSMODEL', 'CLASSIFICATION', 'CLAIMS', 'POLICYNUMBER', null, 'CLAIMS_SET'); end; / Automated Monthly “Application”! Just add: Create View CLAIMS2_30 As Select * from CLAIMS2 Where mydate > SYSDATE – 30 Time measure: set timing on; -- 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 CLAIMS where PASTNUMBEROFCLAIMS in ('2to4', 'morethan4'))) where rnk <= 5 order by percent_fraud desc; Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Oracle Advanced Analytics More Details •
On-the-fly, single record apply with new data (e.g. from call center) Call Center We b Mobile Get AdviceBranch Office Social Media Emai l R Select prediction_probability(CLAS_DT_1_2, 'Yes' USING 7800 as bank_funds, 125 as checking_amount, 20 as credit_balance, 55 as age, 'Married' as marital_status, 250 as MONEY_MONTLY_OVERDRAWN, 1 as house_ownership) from dual; Likelihood to respond: Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Data Mining When
Lack Examples Customer Months Better Information, Valuable Insights and Predictions CellPhoneFraud vs. Loyal Customers Source: Inspired from Data Mining Techniques: ForMarketing, Sales,and CustomerRelationship Management by Michael J. A. Berry, Gordon S. Linoff ? Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Challenge: Finding Anomalies • Considering multiple attributes •
Taken alone, may seem “normal” • Taken collectively, a record may appear to be anomalous • Look for what is “different” X 1 X2 X 3 X 4 X1 X2 X3 X4 Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Tax Noncomplaince Audit Selection Copyright
© 2016 Oracle and/or its affiliates. All rights reserved. • Simple Oracle Data Mining predictive model – Uses Decision Tree for classification of Noncompliant tax submissions (yes/no) based on historical 2011 data
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Oracle Advanced Analytics OAA/OracleREnterprise(R integration) Copyright
© 2016 Oracle and/or its affiliates. All rights reserved. |
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• Strengths – Powerful
& Extensible – Graphical & Extensive statistics – Free—open source • Challenges – Memory constrained – Single threaded – Outer loop—slows down process – Not industrial strength Copyright © 2016 Oracle and/or its affiliates. All rights reserved. Renvironment R—Widely Popular R is a statistics language similar to Base SAS or SPSS statistics
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R: Transparency through
function overloading Invoke in-database aggregation function > aggdata <- aggregate(ONTIME_S$DEST, by = list(ONTIME_S$DEST), FUN = length) ONTIME _S In-db Stats OracleDatabase Oracle SQL select DEST, count(*) from ONTIME_S group by DEST Oracle Advanced Analytics ORE Client Packages Transparency Layer + + > class(aggdata) [1] "ore.frame" attr(,"package") [1] "OREbase" > head(aggdata) Group. 1 1 ABE 2 ABI 3 ABQ 4 ABY 5 ACK 6 ACT x 237 34 135 7 10 3 33 DatabaseServer Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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R: Transparency through
function overloading Invoke in-database Data Mining model (Support Vector Machine) CUS T In-db Minin g Model OracleDatabase Oracle PL/SQL BEGIN DBMS_DATA_MINING.CREATE_ MODEL( model_name =>’SVM_MOD’, mining_function => dbms_data_mining.classification ... Oracle Advanced Analytics ORE Client Packages Transparency Layer > svm_mod <- ore.odmSVM(BUY~INCOME+YRS_CUST+MARITAL_STATUS,data=CUST, "classification", kernel="linear") > summary(svm_mod) Call: ore.odmSVM(formula = BUY ~ INCOME + YRS_CUST + MARITAL_STATUS, data = CUST, type = "classification", kernel.function = "linear") Settings: prep.auto value on Coefficients: class 1 0 Copyright © 2016 Oracle and/or its affiliates. All rights reserved. variable value INCOME estimate 5.204561e- 05 active.learning al.enable 2 0 MARITAL_STA TUS M -4.531359e-05 complexity.factor 46.044899 3 0 MARITAL_STA TUS S 4.531359e-05 conv.tolerance 1e-04 4 0 YRS_CUST 1.264948e-04 kernel.function linear 5 0 (Intercept) 9.999269e-01 6 1 INCOME 2.032340e-05 7 1 MARITAL_STA TUS M 2.636552e-06 8 1 MARITAL_STA TUS S -2.636555e-06 9 1 YRS_CUST -1.588211e-04 10 1 (Intercept) -9.999324e-01 DatabaseServer
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. | Oracle Advanced Analyticsfor Hadoop Predictivealgorithmsthat execute in a parallel/distributed manner onHadoopwith data in HDFS
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HadoopCluster with Oracle R
Advanced Analytics for Hadoop (ORAAH) Oracle R Advanced Analytics for Hadoop Using Hadoop and HIVE Integration, plus R Engine and Open-Source R Packages R Analytics Oracle R Advanced Analytics for Hadoop RClient SQL Developer Other SQL Apps SQLClient HQ L Oracle Database with Advanced Analytics option Copyright © 2016 Oracle and/or its affiliates. All rights reserved. R R interface to HQL Basic Statistics, Data Prep, Joins and View creation Parallel, distributed algorithms: MLP Neural Nets*, GLM*, LM, PCA, k-Means, NMF, LMF * Spark-Caching enabled Use of Open-source R packages via custom R Mappers / Reducers
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Oracle R Advanced
Analytics for Hadoop AdvancedAnalyticsalgorithmsin a HadoopCluster:Map-ReduceandSparkbased Generalized Linear Model Logistic Regression Regression Linear Regression Multi-Layer Neural Networks Classification Attribute Importance Principal Components Analysis Clustering Hierarchical k-Means Feature Extraction Nonnegative Matrix Fact(NMF) Collaborative Filtering (LMF) StatisticalFunctions Copyright © 2016 Oracle and/or its affiliates. All rights reserved. Correlation Covariance Cross Tabulation Summary statistics
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Copyright © 2016
Oracle and/or its affiliates. All rights r eserved . ORAAH: Machine Learning in Spark against HDFS data InvokeORAAHcustomparallel distributed GLMModel usingSparkCaching > Connects to Spark > spark.connect("yarn-client",memory="24g") > # Attaches the HDFS file for use withinR > ont1bi <- hdfs.attach("/user/oracle/ontime_1bi") > # Formula definition: Cancelled flights (0 or 1) based on otherattributes > form_oraah_glm2 <- CANCELLED ~ DISTANCE + ORIGIN + DEST + F(YEAR) + F(MONTH)+ + F(DAYOFMONTH) + F(DAYOFWEEK) > system.time(m_spark_glm <- orch.glm2(formula=form_oraah_glm2, ont1bi)) ORCH GLM: processed 6 factor variables, 25.806sec ORCH GLM: created model matrix, 100128 partitions, 32.871sec ORC H GLM: iter 1, deviance 1.38433414089348300E+09, elapsed time 9.582 sec ORC H GLM: iter 2, deviance 3.39315388583931150E+08, elapsed time 9.213 sec ORC H GLM: iter 3, deviance 2.06855738812683250E+08, elapsed time 9.218 sec ORC H GLM: iter 4, deviance 1.75868100359263200E+08, elapsed time 9.104 sec ORC H GLM: iter 5, deviance 1.70023181759611580E+08, elapsed time 9.132 sec ORC H GLM: iter 6, deviance 1.69476890425481350E+08, elapsed time 9.124 sec user system elapsed 84.107 5.606 143.591 Oracle Distribution of R version 3.1.1 (--) -- "Sock it to Me" YARN: Apache Spark Job 4 2 Custom Spark Java Algorithm distributed in-Memory Computation Custom Spark Java Algorithm distributed in-Memory Computation /user/oracle/ontime_ s 3 Oracle R Advanced Analytics for Hadoop Client Packages Spark-Based Machine Learning algorithms module 64 1 5
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. | Big Data SQL PushdownSQLpredictsto storagelayers
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. What gives Exadata extreme performance? 68 Oracle Database 12c SQ L Small data subset quickly returned Offload Query to Exadata Storage Servers Hadoop & NoSQL
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. 69 Introducing Oracle Big Data SQL MassivelyParallel SQLQueryacrossOracle,Hadoopand NoSQL Oracle Database 12c Offload Query to Exadata Storage Servers Hadoop & NoSQL Offload Query to Data Nodes data subse t SQL SQL Small data subset quickly returned
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. Manage and Analyze All Data—SQL & Oracle Big Data SQL 70 StructuredandUnstructured Data Reservoir • JSONdata • HDFS/ Hive • NoSQL • Spatial andGraph data • ImageandVideo data • SocialMedia JSON Oracle Database 12c Oracle Big Data Appliance SQL/ R Data analyzedvia SQL/ R/ GUI • RClients • SQLClients • OracleData Miner Storebusiness-criticaldata in Oracle • Customer data • Transactionaldata • Unstructureddocuments, comments • SpatialandGraph data • ImageandVideo data • SocialMedia Oracle’sAdvancedAnalytics
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More Data Variety—Better
Predictive Models • Increasing sources of relevant data can boost model accuracy Naïve Guess or Random 100% 0% Population Size Responde rs Model with 20 variables Model with 75 variables Model with 250 variables Model with “Big Data” and hundreds -- thousands of input variables including: • Demographic data • PurchasePOStransactional data • “Unstructured data”, text & comments • Spatial location data • Longterm vs.recent historical behavior • Webvisits • Sensordata • etc. 100% Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Wargaming Creates Complex
Analytical Models in Minutes, Ensures Superior Gaming Experience Objectives Online game developer, publisher, and leader in free-to- play massively multiplayer online game market Implement a scalable and flexible data warehouse Ensured optimal game play for more than 110 million players Expand insight into game health, market health, and business S o h l u e t a i l o t h n Ran predicative analytics model in just three minutes instead of more than six hours Enabled developers to adapt game, based on where players experience most exciting play Expanded insight with increased intelligence on customer acquisition and loyalty as well as identifying potential monetization opportunities Customer Snapshot: http://www.oracle.com/us/corporate/customers/customersearch/wargaming-1-bda-ss- 2408474.html “Oracle Big Data Appliance gives us unprecedented insight into game, business, and market health. The possibilities are endless with this highly extensible solution that enables us to gather, analyze, and use data, including social-media data, in ways that were simply not possible before.” – Craig Fryar, Head of Wargaming Business Intelligence Oracle Big Data Appliance, Oracle Database Appliance, Oracle Advanced Analytics, Oracle Big Data Connectors Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. | Oracle’sAdvancedAnalytics PredictiveApplications+OBIEE Integration
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Enabling “Predictive” EnterpriseApplications Oracle
Applications Using Oracle Advanced Analytics— Partial List • OracleHCM Fusion – Employee turnover and performance prediction and “What if?” analysis • OracleCRMFusion – Prediction of sales opportunities, what to sell, amount, timing, etc. • OracleIndustryData Models – CommunicationsData Model churn prediction, segmentation, profiling, etc. – Retail Data Model loyalty and market basket analysis – Airline Data Model analysis frequent flyers, loyalty, etc. – Utilities Data Model customer churn, cross-sell, loyalty, etc. • OracleRetail CustomerAnalytics – ”Shopping cart analysis” and next bestoffers • OracleCustomer Support – Predictive Incident Monitoring (PIM) • OracleSpendClassification – Real-time and batch flagging of noncompliance and anomalies in expense submissions • OracleFinServAnalytic Applications – Customer Insight, Enterprise Risk Management, Enterprise Performance, Financial Crime and Compliance • OracleAdaptiveAccessManager – Real-time security and fraud analytics Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Integrated Business Intelligence Enhance Dashboards
with Predictions and Data Mining Insights • In-database predictive models “mine” customer data and predict their behavior • OBIEE’sintegrated spatial mapping shows location • All OAA results and predictions available in Database via OBIEE Admin to enhance dashboards Oracle Data Mining results available to Oracle BI EE administrators Oracle BI EE defines results for end user presentation Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Pre-BuiltPredictive Models • Fastest
Way to Deliver Scalable Enterprise-wide Predictive Analytics • OAA’sclustering and predictions available in-DB for OBIEE • Automatic Customer Segmentation, Churn Predictions, and Sentiment Analysis Oracle Communications Industry Data Model Example Predictive Analytics Application Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Oracle Communications Data Model Pre-Built
Data Mining Models 1.Churn Prediction 2.Customer Profiling 3.Customer Churn Factor 4.Cross-Sell Opportunity 5.Customer Life Time Value 6.Customer Sentiment 7.Customer Life Time Value Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Oracle Communications Industry
Data Model Predictive Analytics Applications OCDMTelcoChurnEnhancedby SNAAnalysis • Integrated with OCDM, OBIEE, and leverages Oracle Data Mining with specialized SNA code • Identification of social network communities from CDR data • Predictive scores for churn and influence at a node level, as well as potential revenue/value at risk • User interface targeted at business users and flexible ad- hoc reporting Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Fusion HCM Predictive Workforce Predictive
Analytics Applications FusionHumanCapitalManagement PoweredbyOAA • Oracle Advanced Analytics factory- installed predictive analytics • Employees likely to leave and predicted performance • Top reasons, expected behavior • Real-time "What if?" analysis Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Fusion HCM Predictive Workforce Predictive
Analytics Applications FusionHumanCapitalManagement PoweredbyOAA • Oracle Advanced Analytics factory- installed predictive analytics • Employees likely to leave and predicted performance • Top reasons, expected behavior • Real-time "What if?" analysis Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Getting starte d Copyright © 2016
Oracle and/or its affiliates. All rights reserved. |
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Copyright © 2016
Oracle and/or its affiliates. All rights reserved. OAA Links and Resources • OracleAdvancedAnalytics Overview: – OAApresentation—Big Data Analytics in Oracle Database 12c With OracleAdvancedAnalytics & Big Data SQL – BigDataAnalytics with OracleAdvancedAnalytics: Making BigData andAnalytics Simplewhite paper on OTN – Oracle Internal OAA Product Management Wiki and Workspace • YouTube recordedOAAPresentations and Demos: – OracleAdvancedAnalytics andData Mining at the YouTube Movies (6 +OAA“live” Demoson ODM’r 4.0 New Features, Retail, Fraud, Loyalty, Overview, etc.) • Getting Started: – Link to Getting Started w/ ODM blog entry – Link to New OAA/Oracle Data Mining 2-Day Instructor Led Oracle University course. – Link to OAA/Oracle Data Mining 4.0 OraclebyExamples(free) Tutorialson OTN – Take a Free Test Drive of Oracle Advanced Analytics (Oracle Data Miner GUI) on the Amazon Cloud – Link to OAA/Oracle REnterprise(free) Tutorial Serieson OTN • Additional Resources: – Oracle Advanced Analytics Option on OTN page – OAA/Oracle Data Mining onOTNpage, ODM Documentation & ODM Blog – OAA/Oracle REnterprisepageonOTNpage, ORE Documentation & ORE Blog – Oracle SQL based Basic Statistical functions on OTN – BIWASummit’16, Jan26-28, 2016 – Oracle Big Data & Analytics User Conference @ Oracle HQ Conference Center
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Books on Oracle Advanced Analytics L Bookavailable
onAmazon PredictiveAnalyticsUsingOracleData Miner: Developfor ODM in SQL&PL/SQ Copyright © 2016 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted 83 Bookavailable onAmazon UsingRto Unlockthe Valueof BigData
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• Hands-on-Labs • Customerstories,told
bythe customers • EducationalsessionsbyPractitionersandDirect from Developers • OracleKeynote presentations • Presentations covering:AdvancedAnalytics, BigData, BusinessIntelligence, Cloud,Data WarehousingandIntegration, SpatialandGraph, SQL • Networking with productmanagementanddevelopmentprofessionals Copyright © 2016 Oracle and/or its affiliates. All rights reserved.
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Oracle and/or its affiliates. All rights reserved.
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