Oracle’s Advanced Analytics & Machine Learning 12.2c New Features & Road Map; Bigger, Better, Faster, More!

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Oracle’s Advanced
Analytics 12.2c
New Features & Road Map
Bigger, Better, Faster, More!
Charlie Berger, MS Engineering, MBA
Sr. Director Product Management,
Advanced Analytics and Machine Learning
charlie.berger@oracle.com
www.twitter.com/CharlieDataMine

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

Predictive Analytics 101
Machine Learning/Data Analysis
platforms requirements:
• Be extremely powerful and
handle large data volumes
• Be easy to learn
• Be highly automated & enable
deployment
• Data, data everywhere – explosive growth
• 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

Machine Learning/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

Vision
• Big Data + Machine Learning/Analytics Platform for the Era of Big
Data and Cloud
–Make Big Data + ML/Analytics Model Discovery Simple
• Any data size, on any computer infrastructure—on-premise and/or cloud
• Any variety of data (structured, unstructured, transactional, geospatial), in any
combination
–Make Big Data + ML/Analytics Model Deployment Simple
• As a service, as a platform, as an application
• On-premise and/or cloud Oracle Database 12c
Oracle Cloud +
Advanced Analytics
Advanced Analytics

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” (Anomaly Detection)
•Determine co-occurring items in a “baskets” (Associations)
What is Machine Learning, Data Mining & Predictive
Analytics?
A1 A2 A3 A4 A5 A6 A7

Oracle Advanced Analytics DB Option
In-Database Machine Learning Algorithms*—SQL & &GUI Access
• Decision Tree
• Logistic Regression (GLM)
• Naïve Bayes
• Support Vector Machine (SVM)
• Random Forest
Regression
• Multiple Regression (GLM)
• Support Vector Machine (SVM)
• Stepwise Linear Regression
• Linear Model
• Generalized Linear Model
• Multi-Layer Neural Networks
Attribute Importance
• Minimum Description Length
• Unsupervised pair-wise KL div.
• Hierarchical k-Means
• Orthogonal Partitioning Clustering
• Expectation-Maximization
Feature Extraction & Creation
• Nonnegative Matrix Factorization
• 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 Basket Analysis
• Clustering
• Regression
• Anomaly Detection
• Feature Extraction
Predictive Queries
• Ability to run any R package
(9,000+)via Embedded R mode
Open Source R Algorithms
+ Ability to Mine Unstructured, Structured & Transactional data
+ Partitioned Models
A1 A2 A3 A4 A5
A6 A7
Advanced Analytics
Text Mining
• All OAA/ODM SQL ML support
• Explicit Semantic Analysis

 Parallel, scalable data mining algorithms
and R integration
 In-Database + Hadoop—Don’t move the
data
 Data analysts, data scientists &
developers
 Drag and drop workflow, R and SQL APIs
 Extends data management into powerful
advanced/predictive analytics platform
 Enables enterprise predictive analytics
deployment + applications
Key Features
Oracle’s Advanced Analytics
Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics
Advanced Analytics

Google “Oracle Advanced Analytics”
9
Advanced Analytics
Oracle Data Miner

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 10
Additional relevant data
and “engineered features”
Sensor data, Text, unstructured data,
transactional data, spatial data, etc.
Historical data Assembled
historical data
Historical or Current Data to
be “scored” for predictions
Predictions & Insights
Oracle Data Mining/ Machine Learning/Predictive Analytics
Data Preparation & Adv. Analytical Process Runs In-Database
Oracle Database 12c

 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
 Deliver enterprise-wide applications
 GUI for ML/Predictive Analytics & code gen
 R interface leverages database as HPC engine
Major Benefits
avings
Model “Scoring”
EmbeddedData 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
Fastest Way to Deliver Scalable Enterprise-wide ML/Predictive Analytics
Advanced Analytics

Oracle Advanced Analytics 12.2
Model Build Time Performance
T7-4 (Sparc & Solaris) X5-4 (Intel and Linux)
OAA 12.2 Algorithms Rows (Ms) Model Build Time (Secs / Degree of Parallelism)
Attributes Importance 640 28s / 512 44s / 72
K Means Clustering 640 161s / 256 268s / 144
Expectation Maximization 159 455s / 512 588s / 144
Naive Bayes Classification 320 17s / 256 23s / 72
GLM Classification 640 154s / 512 363s / 144
GLM Regression 640 55s / 512 93s / 144
Support Vector Machine (IPM solver) 640 404s / 512 1411s / 144
Support Vector Machine (SGD solver) 640 84s / 256 188s / 72
Prelim/Unofficial
The way to read their results is that they compare 2 chips: X5 (Intel and Linux) and T7 (Sparc and Solaris). They are measuring scalability (time in seconds) with increase
degree of parallelism (dop). The data also has high cardinality categorical columns which translates in 9K mining attributes (when algorithms require explosion). There
are no comparisons to 12.1 and it is fair to say that the 12.1 algorithms could not run on data of this size.
Wow! That’s Fast!

OBIEE
Oracle Database Enterprise Edition
Oracle’s Advanced Analytics (Machine Learning Platform)
Multiple interfaces across platforms — SQL, R, GUI, Dashboards, Apps
Oracle Advanced Analytics - Database Option
SQL Data Mining, ML & Analytic Functions + R Integration
for Scalable, Distributed, Parallel in-DB ML Execution
SQL Developer/
Oracle Data Miner
ApplicationsR Client
Data & Business AnalystsR programmers Business Analysts/Mgrs Domain End UsersUsers
Platform
Hadoop
ORAAH
Parallel,
distributed
algorithms
Oracle Cloud
Information Producers Information Consumers
HQL
Oracle Database 12c
Advanced Analytics

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
SQL Statistical Functions - SQL &
– Automated knowledge discovery, model
building and deployment
– Domain expertise to assemble the “right”
data to mine/analyze
Statistical SQL “Verbs”
– MEAN, STDEV
– MEDIAN
– SUMMARY
– CORRELATE
– FIT
– COMPARE
– ANOVA
+
FREE!

You Can Think of Oracle’s 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
+
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
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
Oracle Database 12c
Advanced Analytics

Oracle Text
Native Capability of every Oracle Database
• Oracle Text uses standard SQL to index, search, and analyze text and
documents stored in the Oracle database, in files, and on the web.
• Oracle Text supports multiple languages and uses advanced relevance-
ranking technology to improve search quality.
• Oracle Advanced Analytics uses Oracle Text to pre-process (“tokenize”)
unstructured data for the OAA SQL data mining functions

Rapidly Build, Evaluate & Deploy Analytical Methodologies
Leveraging a Variety of Data Sources and Types
Consider:
• Demographics
• Past purchases
• Recent purchases
• Comments & tweetsUnstructured data
also mined by
algorithms
Transactional
POS data
Generates SQL scripts
and workflow API for
deployment
Inline predictive
model to augment
input data
SQL Joins and arbitrary SQL
transforms & queries – power of SQL
Modeling
Approaches
Advanced Analytics

Oracle Advanced Analytics—On Premise or Cloud
100% Compatibility Enables Easy Coexistence and Migration
19
Oracle Cloud
CoExistence and Migration
Same Architecture
Same ML/Analytics
Same Standards
Transparently move workloads and ML/analytical methodologies between
On-premise and public cloud
On-Premise
Advanced Analytics

Manage and Analyze All Data—SQL & Oracle Big Data SQL
Structured and Unstructured Data Reservoir
• JSON data
• HDFS / Hive
• NoSQL
• Spatial and Graph data
• Image and Video data
• Social Media
JSON
Oracle Database 12cOracle Big Data Appliance
SQL / R
Data analyzed via SQL / R / GUI
• R Clients
• SQL Clients
• Oracle Data Miner
Store business-critical data in Oracle
• Customer data
• Transactional data
• Unstructured documents, comments
• Spatial and Graph data
• Image and Video data
• Social Media
Data Analysts
Big Data SQL + Advanced Analytics

More Data Variety—Better Predictive Models
• Increasing sources of
relevant data can boost
model accuracy
Naïve Guess or
Random
100%
0% Population Size
Responders
Model with 20 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%
Engineered Features – Derived attributes/variable that
reflect domain knowledge—key to best models

Brief Demos

Oracle Data Miner GUI
Easy to Use for “Citizen Data Scientist”
• Easy to use to
define analytical
methodologies
that can be
shared
• SQL Developer
Extension
• Workflow API
and generates
SQL code for
immediate
deployment
23
Advanced Analytics

Sharing, Automation and Deployment
Immediately Go to “Productionization” of Analytical Methodologies
• Share ODMr workflows
• Workflow API for 100% automation
• Immediate deployment of data analyst’s
methodologies
• SQL Script Generation
• Deploy methodology as SQL scripts

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
Automated Monthly “Application”! Just
add:
Create
View CLAIMS2_30
As
Select * from CLAIMS2
Where mydate > SYSDATE – 30
Time measure: set timing on;

• On-the-fly, single record apply with new data (e.g. from call center)
Real-Time Scoring, Predictions and Recommendations
Call Center
Get Advice
Web Mobile
Branch
Office
Social Media
Email
R
Select prediction_probability(FRAUD_1_SVM_1, '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:

Example Customer References

Fiserv
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, Fiserv Inc.
– Julia Minkowski, Risk Analytics Manager, Fiserv Inc.
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)

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 and Oracle
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 Services Authority
Oracle Exadata Database
Machine
Oracle Advanced
Analytics
Oracle Exalytics In-Memory
Machine
Oracle Endeca Information
Discovery
Oracle Business Intelligence EE

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
Oracle Cloud
 Cloud-based solution
 Increase revenue
 Reduce time-to-market
DX Marketing Expands Customer Acquisition with Oracle Cloud –
YouTube video

Zagrebačka Bank (biggest bank in Croatia)
Increases Cash Loans by 15% Within 18 Months of Deployment
Objectives
Solution
 “With Oracle Advanced Analytics we execute computations on
thousands of attributes in parallel—impossible with open-source
R. Analyzing in Oracle Database without moving data increases
our agility. Oracle Advanced Analytics enables us to make quality
decisions on time, increasing our cash loans business 15%.”
– Jadranka Novoselovic, Head of BI Dev., Zagrebačka Bank
 Needed to speed up entire advanced analytics process;
data prep was taking 3 days; model building 24 hours
 Faster time to “actionable analytics” for Credit Risk
Modeling and Targeted Customer Campaigns
ZabaBank Oracle Customer Snapshot on OTN
 Zaba migrated from SAS to the Oracle Advanced
Analytics platform for statistical modeling and predictive
analytics
 Increased prediction performance by leveraging the
security, reliability, performance, and scalability of
Oracle Database and Oracle Advanced Analytics for
predictive analytics—running data preparation,
transformation, model building, and model scoring
within the database
“We chose Oracle because our entire data modeling process runs on
the same machine with the highest performance and level of
integration. With Oracle Database we simply switched on the Oracle
Advanced Analytics option and
needed no new tools,”
– Sinisa Behin, ICT coordinator
at BI Dev. Zagrebačka Bank

An Post
Boosts Retail & Postal Services with Big Data & Analytics Platform
Objectives
 Provide a scalable big data and analytics platform to
manage millions of daily transactions, facilitate new
postal services, and align with market needs and the
growth in post office retail services
Solution
 Deployed a big data and analytics platform using Oracle
Exadata Database Machine,Oracle Advanced Analytics, …
to …, combat fraud, and enable readily-available
enterprise wide business …within core areas such as
mails, parcels, and retail
 Enabled An Post to rapidly analyze and respond …using
Oracle Advanced Analytics…
 Vertice (partner) provided expert services including …in-
depth product and data mining expertise services
 “With our Oracle solution for Big Data and Analytics we now have
an analytics platform that has completely transformed our service
delivery model. It provides An Post with a single source of truth
while automatically consolidating data from all post offices
nationwide.”
– John Cronin, Group Chief Information Officer, An Post Limited
http://www.oracle.com/us/corporate/customers/customersearch/an-post-1-big-data-2759887.html
https://www.siliconrepublic.com/enterprise/2016/02/05/an-post-oracle-john-cronin-five-minute-cio
“Big data is a vital part
of our future,” says
John Cronin, CIO of
Ireland’s postal
network.

OAA/Oracle R Enterprise (R integration)

• Strengths
– Powerful & Extensible
– Graphical & Extensive statistics
– Free—open source
• Challenges
– Memory constrained
– Single threaded
– Outer loop—slows down process
– Not industrial strength
R environment
R—Widely Popular
R is a statistics language similar to Base SAS or SPSS statistics

R: Transparency via function overloading
Invoke in-database aggregation function
> aggdata <- aggregate(ONTIME_S$DEST,
+ by = list(ONTIME_S$DEST),
+ FUN = length)
ONTIME_S
In-db
Stats
Oracle Database
Oracle SQL
select DEST, count(*)
from ONTIME_S
group by DEST
ORE Client Packages
Transparency Layer
> class(aggdata)
[1] "ore.frame"
attr(,"package")
[1] "OREbase"
> head(aggdata)
Group.1 x
1 ABE 237
2 ABI 34
3 ABQ 1357
4 ABY 10
5 ACK 3
6 ACT 33
Database Server
Advanced Analytics

R: Transparency via function overloading
Invoke in-database Data Mining model (Support Vector Machine)
> svm_mod <- ore.odmSVM(BUY~INCOME+YRS_CUST+MARITAL_STATUS,data=CUST,
"classification", kernel="linear")
CUST
In-db
Mining
Model
Oracle Database
Oracle PL/SQL
BEGIN
DBMS_DATA_MINING.CREATE_MODEL(
model_name => ’SVM_MOD’,
mining_function =>
dbms_data_mining.classification
...
ORE Client Packages
Transparency Layer
> summary(svm_mod)
Call:
ore.odmSVM(formula = BUY ~ INCOME + YRS_CUST + MARITAL_STATUS, data = CUST,
type = "classification", kernel.function = "linear")
Settings:
value
prep.auto on
active.learning al.enable
complexity.factor 46.044899
conv.tolerance 1e-04
kernel.function linear
Coefficients:
class variable value estimate
1 0 INCOME 5.204561e-05
2 0 MARITAL_STATUS M -4.531359e-05
3 0 MARITAL_STATUS S 4.531359e-05
4 0 YRS_CUST 1.264948e-04
5 0 (Intercept) 9.999269e-01
6 1 INCOME 2.032340e-05
7 1 MARITAL_STATUS M 2.636552e-06
8 1 MARITAL_STATUS S -2.636555e-06
9 1 YRS_CUST -1.588211e-04
10 1 (Intercept) -9.999324e-01
Database Server

Oracle Advanced Analytics for Hadoop
Predictive algorithms that execute in a parallel/distributed manner on Hadoop with
data in HDFS

Hadoop Cluster
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
R Client
SQL Developer
Other SQL Apps
SQL Client
HQL
Oracle Database
with Advanced Analytics option
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
Advanced Analytics

Logistic Regression ORAAH
Regression
Linear Regression
Classification
MLP Neural Networks ORAAH
Attribute Importance
LASSO
Clustering
K-Means
Feature Extraction
Non-negative Matrix
Factorization
Collaborative Filtering
(LMF)
AA Algorithms in a Hadoop Cluster: Map-Reduce and Spark (2.7)
56
Basic Statistics
Correlation/Covariance
GLM ORAAH
Random Forests
Decision Trees
K-Means
Principal Components Analysis
Ridge Regression
Support Vector Machines
Support Vector Machines
Random Forest
LASSO
Gaussian Mixture Models
Principal Components Analysis

Predictive Applications + OBIEE Integration

Enabling “Predictive” Enterprise Applications
Oracle Applications Using Oracle Advanced Analytics—Partial List
• Oracle HCM Fusion
– Employee turnover and performance
prediction and “What if?” analysis
• Oracle CRM Fusion
– Prediction of sales opportunities,
what to sell, amount, timing, etc.
• Oracle Industry Data Models
– Communications Data 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.
• Oracle Retail Insights Cloud Services
– Market Basket Analysis Insights
– Customer Insights & Clustering
• Oracle Customer Support
– Predictive Incident Monitoring (PIM)
• Oracle Spend Classification
– Real-time and batch flagging of
noncompliance and anomalies
in expense submissions
• Oracle FinServ Analytic Applications
– Customer Insight, Enterprise Risk Management,
Enterprise Performance, Financial Crime
and Compliance
• Oracle Adaptive Access Manager
– Real-time security and fraud analytics

• Oracle Advanced Analytics factory-
installed predictive analytics
• Employees likely to leave and
predicted performance
• Top reasons, expected behavior
• Real-time "What if?" analysis
Human Capital Management
Powered by OAA
HCM Predictive Workforce
Predictive Analytics Applications
Link to Oracle HCM on O.com
HCM Predictive Workforce demo

• 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
Link to OCDM on OTN

Oracle Communications Data Model
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
Pre-Built Data Mining Models
Link to OCDM on OTN

Oracle Advanced Analytics 12.2,
Oracle Data Miner 4.2 and ORAAH 2.6
New Features + Road Map

New Oracle Database Features
• Major Performance Improvements for Algorithms
– New parallel model build / apply infrastructure to enable faster algorithm introduction
– Scale to larger data volumes found in big data and cloud use cases
• Unsupervised Feature Selection
– Uses unsupervised pair-wise Kullback-Leibler Divergence (KLD - correlations analysis - numeric
and categorical attributes) to find highest “information containing” attributes
• Association Rules Enhancements
– Adds calculation of values associated with AR rules such as sales amount to indicate the
value of co-occurring items in baskets
• Partitioned Models
– Instead of building, naming and referencing 10s or 1000s of models, a partitioned model
will organize and represent these multiple models as partitions in a single model entity

• Explicit Semantic Analysis (ESA) algorithm
– Useful technique for extracting meaningful, interpretable features, better than LDA
– Use ESA for document similarity and topic identification
– Wikipedia provides a large corpus of existing documents to provide sensible features and topics
– Document 1
– 'Senior members of the Saudi royal family paid at least $560 million to Osama bin Laden terror group and the Taliban for an
agreement his forces would not attack targets in Saudi Arabia, according to court documents. The papers, filed in a $US3000 billion
($5500 billion) lawsuit in the US, allege the deal was made after two secret meetings between Saudi royals and leaders of al-Qa ida,
including bin Laden. The money enabled al-Qa ida to fund training camps in Afghanistan later attended by the September 11
hijackers. The disclosures will increase tensions between the US and Saudi Arabia. '
Document 2
– 'The Saudi Interior Ministry on Sunday confirmed it is holding a 21-year-old Saudi man the FBI is seeking for alleged links to the Sept.
11 hijackers. Authorities are interrogating Saud Abdulaziz Saud al-Rasheed "and if it is proven that he was connected to terrorism,
he will be referred to the sharia (Islamic) court," the official Saudi Press Agency quoted an unidentified ministry official as saying.'
ESA Similarity 0.62

• Extensibility for R Models
– Register R models as in-database models for build, apply, settings, and viewing
– Supports data with “nested” attributes, handling text and aggregated transactional data for open source
R packages
– Extends ease of advanced analytics development from R to Oracle Database
– Enables R users to roll out new analytics and more rapidly take advantage of existing R packages

Oracle Data Miner
4.2 New Features
NEW IN
4.2

Oracle Data Miner 4.2
New Features for OAA
• Add/Expose
all 12.2
features in
Oracle Data
Miner UI
NEW IN
4.2

Previewing a 4.2 Feature
Workflow Scheduler
NEW IN
4.2

• Updated ORAAH GLM and LM algorithms which are much faster, stable and light on
memory than comparable GLM and LM methods from Spark Mllib
• Both methods also bring a new summary feature that makes them comparable to
solutions from open-source R glm and lm, but capable of handling Big Data at enterprise
scale
• The Neural Networks algorithm has been enhanced to support the full formula
processing and a full build and scoring in Spark
• The new Gaussian Mixture Models is an addition to the set of algorithms supported in
Spark Mllib
• ORAAH's Spark-based LM with full formula support and summary - orch.lm2()
• ORAAH's Spark-based GLM with full formula support and summary - orch.glm2()
74
New Features in ORAAH 2.7

Oracle Advanced Analytics Strategy & Road Map
• One server side product, with a single analytic library, supporting multiple
platforms, UIs and deployment strategies
Hadoop Relational
Algorithms
Common core, parallel, distributed
SQL RGUI
Cloud
Advanced Analytics

Getting started

Getting started: OAA Links and Resources
Oracle Advanced Analytics Overview:
 OAA presentation— Big Data Analytics with Oracle Advanced Analytics …or just watch Watch YouTube video
presentation and demo(s)
 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
 Oracle Advanced Analytics Customer Successes
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 OAA/Oracle Data Miner Workflow GUI Online (free) Tutorial Series on OTN
 Link to OAA/Oracle R Enterprise (free) Tutorial Series on OTN
 Link to Free Oracle Advanced Analytics "Test Drives" on Oracle Cloud via Vlamis Partner
 Link to Getting Started w/ ODM blog entry
 Link to New OAA/Oracle Data Mining 2-Day Instructor Led Oracle University course.
 Oracle Data Mining Sample Code Examples
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
 Oracle R Advanced Analytics for Hadoop (ORAAH) on OTN
 Business Intelligence, Warehousing & Analytics—BIWA Summit’17, Jan 31, Feb 1 & 2,
2017 at Oracle HQ Conference Center (w/ links to customer presentations)

Oracle’s Advanced Analytics & Machine Learning 12.2c New Features & Road Map; Bigger, Better, Faster, More!

Oracle’s Advanced Analytics & Machine Learning 12.2c New Features & Road Map; Bigger, Better, Faster, More!

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