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Big Data Analytics with Oracle Advanced Analytics 12c and Big Data SQL
Charlie Berger, MS Engineering, MBA Sr. Director Product Management, Data Mining and Advanced Analytics [email protected] www.twitter.com/CharlieDataMine
Faster than a Mouse: Turn Data Mining Strategy into Action
Miguel Barrera, Director of Risk Analytics, Fiserv Inc. Julia Minkowski, Risk Analytics Manager, Fiserv Inc.
Make Big Data + Analytics Simple
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Data, data everywhere
Oracle Confidential – Internal/Restricted/Highly Restricted
Data Analysis platforms requirements:
• Be extremely powerful and handle large data volumes
• Be easy to learn
• Be highly automated & enable deployment
Growth of Data Exponentially Greater than Growth of Data Analysts!
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
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Analytics + Data Warehouse + Hadoop
• 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
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Vision
• Big Data + Analytic Platform for the Era of Big Data and Cloud
–Make Big Data + Analytics Simple
• Any data size, on any computer infrastructure
• Any variety of data, in any combination
–Make Big Data + Analytics Deployment Simple
• As a service, as a platform, as an application
5
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Scalable in-database 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
Key Features
Oracle Advanced Analytics Database Option Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
OBIEE
Oracle Database Enterprise Edition
Oracle Advanced Analytics Database Architecture Multi-lingual Component of Oracle Database—SQL, SQL Dev/ODMr GUI, R
Oracle Advanced Analytics - Database Option SQL Data Mining & Analytic Functions + R Integration
for Scalable, Distributed, Parallel in-Database ML Execution
SQL Developer
Applications
R Client
Data & Business Analysts R programmers Business Analysts/Mgrs Domain End Users Users
Platform
Oracle Database 12c
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Data remains in the Database
Scalable machine learning algorithms (e.g. clustering, regression, decision Trees, SVM, PCA, NB, text, AR, etc.) implemented as SQL functions
Parallelized SQL data mining functions, data preparation and execution of R open-source packages
High-performance parallel scoring of SQL dm functions and R models
Key Features
Oracle Advanced Analytics Database Option Fastest way to deliver enterprise-wide predictive analytics
avings
Model “Scoring” Embedded Data Prep
Data Preparation
Model Building
Oracle Advanced Analytics
Secs, Mins or Hours
Traditional Analytics
Hours, Days or Weeks
Data Extraction
Data Prep & Transformation
Data Mining Model Building
Data Mining Model “Scoring”
Data Prep. & Transformation
Data Import
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
avings
Model “Scoring” Embedded Data Prep
Data Preparation
Model Building
Oracle Advanced Analytics
Secs, Mins or Hours
Traditional Analytics
Hours, Days or Weeks
Data Extraction
Data Prep & Transformation
Data Mining Model Building
Data Mining Model “Scoring”
Data Prep. & Transformation
Data Import
Lowest Total Cost of Ownership
Leverage investment in Oracle tech stack
Eliminate duplicate data & ETL
Eliminate separate analytical servers
Fastest way to deliver enterprise-wide predictive analytics
Analytical Database Applications
Key Features
Oracle Advanced Analytics Database Option Fastest way to deliver enterprise-wide predictive analytics
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Function Algorithms Applicability
Classification
Logistic Regression (GLM) Decision Trees Naïve Bayes Support Vector Machines (SVM)
Classical statistical technique Popular / Rules / transparency Embedded app Wide / narrow data / text
Regression Linear Regression (GLM) Support Vector Machine (SVM)
Classical statistical technique
Wide / narrow data / text
Anomaly Detection
One Class SVM Unknown fraud cases or anomalies
Attribute Importance
Minimum Description Length (MDL) Principal Components Analysis (PCA)
Attribute reduction, Reduce data noise
Association Rules
Apriori Market basket analysis / Next Best Offer
Clustering Hierarchical k-Means Hierarchical O-Cluster Expectation-Maximization Clustering (EM)
Product grouping / Text mining Gene and protein analysis
Feature Extraction
Nonnegative Matrix Factorization (NMF) Singular Value Decomposition (SVD)
Text analysis / Feature reduction
Oracle Advanced Analytics In-Database Data Mining Algorithms—SQL & & GUI Access
A1 A2 A3 A4 A5 A6 A7
F1 F2 F3 F4
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Oracle Advanced Analytics
• 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 as proprietary in-database SQL functions—machine learning algorithms and statistical functions
• Leverage database strengths: SQL parallelism, scale to large datasets, security
• Access big data in Database and Hadoop via SQL, R, and Big Data SQL
Other R packages
Oracle R Enterprise (ORE) packages
R-> SQL Transparency “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
Oracle Database 12c R-> SQL
Results
In-Database Adv Analytical SQL Functions
R Engine Other R packages
Oracle R Enterprise packages
Embedded R Package Callouts
R
Results
How Oracle R Enterprise Compute Engines Work
1 2 3
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You Can Think of Oracle Advanced Analytics Like This… Traditional SQL
– “Human-driven” queries
– Domain expertise
– Any “rules” must be defined and managed
SQL Queries – SELECT
– DISTINCT
– AGGREGATE
– WHERE
– AND OR
– GROUP BY
– ORDER BY
– RANK
Oracle Advanced Analytics - SQL & – Automated knowledge discovery, model
building and deployment
– Domain expertise to assemble the “right” data to mine/analyze
Analytical SQL “Verbs” – PREDICT
– DETECT
– CLUSTER
– CLASSIFY
– REGRESS
– PROFILE
– IDENTIFY FACTORS
– ASSOCIATE
+
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Predicting Behavior Identify “Likely Behavior” and their Profiles
Consider: • Demographics • Past purchases • Recent purchases • Customer comments & tweets Unstructured data
also mined by algorithms
Transactional 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 © 2015 Oracle and/or its affiliates. All rights reserved. |
SQL Developer/Oracle Data Miner 4.0 New Features SQL Script Generation
– Deploy entire methodology as a SQL script
– Immediate deployment of data analyst’s methodologies
R
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Fraud Prediction Demo
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; begin dbms_data_mining.create_model('CLAIMSMODEL', 'CLASSIFICATION', 'CLAIMS', '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 CLAIMS where PASTNUMBEROFCLAIMS in ('2to4', 'morethan4'))) where rnk <= 5 order by percent_fraud desc;
Automated In-DB Analytical Methodology
POLICYNUMBER PERCENT_FRAUD RNK ------------ ------------- ---------- 6532 64.78 1 2749 64.17 2 3440 63.22 3 654 63.1 4 12650 62.36 5
Automated Monthly “Application”! Just
add:
Create
View CLAIMS2_30
As
Select * from CLAIMS2
Where mydate > SYSDATE – 30
Time measure: set timing on;
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Oracle Advanced Analytics
• On-the-fly, single record apply with new data (e.g. from call center)
More Details
Call Center Get Advice
Web Mobile
Branch Office
Social Media
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:
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Introducing Oracle Big Data SQL
32
Massively Parallel SQL Query across Oracle, Hadoop and NoSQL
Oracle Database 12c
Offload Query to Exadata Storage Servers
Small data subset quickly returned
Hadoop & NoSQL
Offload Query to Data Nodes
SQL
data subset
SQL
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Manage and Analyze All Data—SQL & Oracle Big Data SQL
33
Store JSON data unconverted in Hadoop
JSON
Oracle Database 12c Oracle Big Data Appliance
SQL
Data analyzed via SQL or R Store business-critical data in Oracle
SQL
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
More Data Variety—Better Predictive Models
• Increasing sources of relevant data can boost model accuracy
Naïve Guess or Random
100%
0% Population Size
Res
po
nd
ers
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 • Purchase POS transactional
data • “Unstructured data”, text &
comments • Spatial location data • Long term vs. recent historical
behavior • Web visits • Sensor data • etc.
100%
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Oracle R Advanced Analytics for Hadoop
35
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
• Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics
• OAA’s clustering and predictions available in-DB for OBIEE
• Automatic Customer Segmentation, Churn Predictions, and Sentiment Analysis
Pre-Built Predictive Models
Oracle Communications Industry Data Model Example Predictive Analytics Application
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
New Features
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Oracle Data Miner 4.1 New Features
• JSON Query node
JSON Query node extracts BDA data via External Tables and parses out JSON data type and assembles data for data mining
Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
Oracle Data Miner 4.1 New Features
• Oracle Data Miner Workflow API to Manage, Schedule and Run Workflows – PL/SQL APIs to enable
applications to execute workflows immediately or schedule them
– Oracle Scheduler for scheduling functionality
– ODMr repository views can be queried for project and workflow information
– Applications can monitor workflow execution and query generated results
CONNECT DMUSER/DMUSER SET SERVEROUTPUT ON DECLARE v_jobId VARCHAR2(30) := NULL; v_status VARCHAR2(30) := NULL; v_projectName VARCHAR2(30) := 'Project'; v_workflow_name VARCHAR2(30) := 'build_workflow'; v_node VARCHAR2(30) := 'MODEL_COEFFCIENTS'; v_run_mode VARCHAR2(30) := ODMRSYS.ODMR_WORKFLOW.RERUN_NODE_PARENTS; v_failure NUMBER := 0; v_nodes ODMRSYS.ODMR_OBJECT_NAMES := ODMRSYS.ODMR_OBJECT_NAMES(); BEGIN v_nodes.extend(); v_nodes(v_nodes.count) := v_node; v_jobId := ODMRSYS.ODMR_WORKFLOW.WF_RUN(p_project_name => v_projectName, p_workflow_name => v_workflow_name, p_node_names => v_nodes, p_run_mode => v_run_mode, p_start_date => '31-DEC-14 12.00.00 AM AMERICA/NEW_YORK', p_repeat_interval => 'FREQ=MONTHLY;BYMONTHDAY=-1', p_end_date => '31-DEC-15 12.00.00 AM AMERICA/NEW_YORK'); DBMS_OUTPUT.PUT_LINE('Job: '||v_jobId);
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Oracle R Advanced Analytics for Hadoop
Oracle Internal - Proprietary 40
Algorithms and Functions
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
OAA Links and Resources • Oracle Advanced Analytics Overview:
– OAA presentation— Big Data Analytics in Oracle Database 12c With Oracle Advanced Analytics & Big Data SQL
– Big Data Analytics with Oracle Advanced Analytics: Making Big Data and Analytics Simple white paper on OTN
– Oracle Internal OAA Product Management Wiki and Workspace
• YouTube recorded OAA Presentations and Demos:
– Oracle Advanced Analytics and Data Mining at the YouTube Movies (6 + OAA “live” Demos on 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 4.1 Oracle by Examples (free) Tutorials on OTN
– Take a Free Test Drive of Oracle Advanced Analytics (Oracle Data Miner GUI) on the Amazon Cloud
– Link to OAA/Oracle R Enterprise (free) Tutorial Series on OTN
• Additional Resources:
– Oracle Advanced Analytics Option on OTN page
– OAA/Oracle Data Mining on OTN page, ODM Documentation & ODM Blog
– OAA/Oracle R Enterprise page on OTN page, ORE Documentation & ORE Blog
– Oracle SQL based Basic Statistical functions on OTN
– BIWA Summit’16, Jan 26-28, 2016 – Oracle Big Data & Analytics User Conference @ Oracle HQ Conference Center
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
• Hands-on-Labs • Customer stories, told by the customers • Educational sessions by Practitioners and Direct from Developers • Oracle Keynote presentations • Presentations covering: Advanced Analytics, Big Data, Business Intelligence,
Cloud, Data Warehousing and Integration, Spatial and Graph, SQL • Networking with product management and development professionals
Faster than a Mouse: Turn Data Mining Strategy into Action
Miguel Barrera, Director of Risk Analytics, Fiserv Inc.
Julia Minkowski, Risk Analytics Manager, Fiserv Inc.
© 2014 Fiserv, Inc. or its affiliates.
Risk Analytics @ Fiserv Electronic Payments
• We prevent $200M in losses every year using data to monitor, understand and anticipate fraud
• We manage risk for $30BB in transfers, servicing 2,500+ financial institutions, including the 27 of the top 30 banks in the US
• A department of 6 people, we operated in start-up mode until we were acquired in 2011 by Fiserv
• We build our risk models, supervise their installation & develop the next-generation of strategies for risk mitigation
© 2014 Fiserv, Inc. or its affiliates.
What is special about Fraud Prevention?
• Fraud is performed by organized criminal groups using sophisticated technologies and logistics
• Hard to detect: target has low frequency (2 in 10,000)
• Misclassification is expensive
$ Losses if you fail to detect fraud
60% increase in customer attrition if you miss-classify
• The environment changes fast, so you need to adapt quickly
Fraud prevention
is a great field for
the application of
predictive
analytics
© 2014 Fiserv, Inc. or its affiliates.
Evolution of Model Deployment
3 months to run & deploy Logistic Regression
(using SAS) 1 month to estimate and deploy Trees and GLM
1 week to estimate, 1 week to install rules in online application
1 day to estimate and deploy Trees + GLM models (using Oracle Advanced Analytics)
2008 2010 2013 2015
© 2014 Fiserv, Inc. or its affiliates.
Lessons we learned for our business
• Complex methods were hard to deploy
because they required large investments in
infrastructure and translation time
• Once we created good predictive attributes,
simple methods (Trees, GLM) were almost as
good as complex estimates (ensemble,
gradient descent)
© 2014 Fiserv, Inc. or its affiliates.
What to Do?
• Option 1: Get a bigger door
(Netezza/ Hadoop / Pivotal )
• Option 2: Shrink the Elephant
Install Simpler Algorithms
+
© 2014 Fiserv, Inc. or its affiliates.
Analytics Software + Infrastructure Looking for a cost-effective way to migrate our modeling requirements from other vendors to a scalable infrastructure: IBM/ Spark
• We stopped replicating
data into other software
tables
• We integrated all
preprocessing for the
models into the DB
• We installed OAA
analytics for model
development and
integrated R during 2014
Co
st
© 2014 Fiserv, Inc. or its affiliates.
Why we like Oracle Advanced Analytics:
Accuracy
Agility
Scalability
• The loss reduction from timely deployment (hours) compensates for
the small increase of highly complex models
• No data transfer needed (in-database)
• New opportunities to combine structured data with unstructured data
• The integration with our DB replication makes re-fit inexpensive
• The same algorithm can scale-up for all other clients
© 2014 Fiserv, Inc. or its affiliates.
Data Miner Survey 2015 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!
© 2014 Fiserv, Inc. or its affiliates.
In Fraud-Mitigation Speed is the Key How long can you wait to deploy a solution?
© 2014 Fiserv, Inc. or its affiliates.
The Value of Time
© 2015, Oracle Corporation
avings
Model “Scoring” Embedded Data Prep
Data Preparation
Model Building
Oracle Advanced Analytics
Secs, Mins or Hours
Traditional Analytics
Hours, Days or Weeks
Data Extraction
Data Prep & Transformation
Data Mining Model Building
Data Mining Model “Scoring”
Data Prep. & Transformation
Data Import
© 2014 Fiserv, Inc. or its affiliates.
Accuracy / Agility vs. Cost to Deploy
• Pick the best combination of: • Less days to deployment
• High model accuracy
• Lower Cost
Application Deploy (Days) Accuracy Total Cost
SAS Server 3 0.92 x5
ODM 1 0.90 1
SAS Base 15 0.83 30%
Angoss 12 0.85 10%
© 2014 Fiserv, Inc. or its affiliates.
Time to Fit & Deploy
© 2014 Fiserv, Inc. or its affiliates.
How we have leveraged the Oracle-R interface
Expand Data Exploration and Visualization 1. Chart and store the results of existing procedures in the DB 2. Run R matrices for visualization of variable densities Model Fitting 1. Transport R models and legacy code from desktops to the DB 2. All the model documentation stays in the DB and access is public
Deployment 1. Run process directly from PL/SQL and visualize in the application 2. Call R models from stored procedures for scoring and direct action
© 2014 Fiserv, Inc. or its affiliates.
OAA+ R Improves Exploration and Visualization
1. We process routines in R and save
images in DB
2. We removed the memory size constraint that made R-methods and plots impractical
3. Allowed to integrate leverage our DBAs and add them to the modeling team
Chart and store the results of existing
procedures in the DB
© 2014 Fiserv, Inc. or its affiliates.
Fit from SQL Developer and Store in DB
• Wrap the script around your existing R code, just like you would with an existing R function
• The server stores the script in a table that you can call and just assemble the code dynamically to fit your models
• The SQL access allow you to preprocess information and sub-set your data as part of SQL calls in screen or procedures, so you can seamlessly integrate into your exploration or production scoring processes.
© 2014 Fiserv, Inc. or its affiliates.
Store the results directly in the Database
• ggplot creates a chart, prints it to the output, then the
query runs the script and retrieves the output as an
image
• You can wrap a procedure and just run the analysis and
store the output directly into a table in the DB
© 2014 Fiserv, Inc. or its affiliates.
Store models, data and performance in the DB
Turning Data Mining Strategy into Action
© 2014 Fiserv, Inc. or its affiliates.
Stakeholders: Everyone has different incentives
Business Manager
Data Scientist IT Manager
• Preserve Service Level
Agreement
• Reduce Operational Risk
• Preserve Budget
© 2014 Fiserv, Inc. or its affiliates.
Conflict of Interests?
Cannot agree on success factors?
Wonder why…?
© 2014 Fiserv, Inc. or its affiliates.
Managing the Quants
• Define clearly the objective and constraints
• Implement SMART* goal setting
• Get familiar with basic analytics concepts
• Establish a time-line for delivery then multiply x 2
• Make sure you understand enough to explain to other executives… you will champion this initiative and negotiate the budgets
Source: Davenport, Tom (2013), “Keep up with your quants", Harvard Business Review, Issue July-August 2013
© 2014 Fiserv, Inc. or its affiliates.
Take Care of Business (tips for Data Scientists)
• Communicate clearly business level information
• When and what is the expected result
• Present the key concept in 2 phrases
• Avoid technical language for communication
• If asked for more details, then present the “How”
• Provide a Business Dashboard
• Provide the $$ metrics profit/loss reduction
• Show the impact of algorithms deployed / provided
• Current vs. Historical
• Pick the right model - the model that maximizes the ROI
Source: Davenport, Tom (2013), “Keep up with your quants", Harvard Business Review, Issue July-August 2013
© 2014 Fiserv, Inc. or its affiliates.
Tracking Performance: Dashboard
Our dashboards tracked the key performance metrics:
• Historical Trends for Fraud Rates and Losses (Business KPI)
• Percentage of Transfers affected by Risk Mitigation (Business KPI)
• % of population affected by policy and % of fraud prevented (KPI for Analytics)
• Fraud detection rates for rules installed (KPI for Analytics)
© 2014 Fiserv, Inc. or its affiliates.
Key Takeaways On Fraud Modeling
• 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
• The additional interface with R has allowed the group to leverage the skillset for both the analytics and data management team, accelerating adoption across our group
Turning Strategy into Action
• Involving the key stakeholders early in the process maximizes your chance for success. Once you have aligned the incentives for the team, selecting the appropriate techniques, tools and infrastructure becomes much simpler
• For data scientists it is important to select their models and projects based on the expected business impact and to translate their findings into the relevant metrics
© 2014 Fiserv, Inc. or its affiliates. 69
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