SQL ANALYTICS – Spark Mllib Algorithm Integration E.G

Oracle Machine Learning & Advanced Analytics Data Management Platforms Move the Algorithms; Not the Data!

Charlie Berger, MS Engineering, MBA Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics [email protected] www.twitter.com/CharlieDataMine

Copyright © 2018, 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.

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 2 Machine Learning, Data Mining, Predictive Analytics Automatically sift through large amounts of data to find hidden patterns, discover new insights and make predictions

A1 A2 A3 A4 A5 A6 A7 • 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)

Market Observations: • Machine learning, predictive analytics & “AI” now must-have requirements • Separate islands for data management and data science just don’t work • Enterprises whose data science teams most rapidly extract insights and predictions win Conclusions: • Must “operationalize” ML insights and predictions throughout enterprise • Multilingual Machine Learning: SQL, R, Python, Workflow UI, Notebooks, Embed ML in Appls, • Evolving towards combined Data Management + Machine Learning environment that can essentially to manage and “think” about data

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 4 Oracle’s Data Management and Machine Learning Architectural Strategy • In-Database proprietary implementations of machine learning algorithms • Leverage strengths of the Database and adds new ML tech – Counting, conditional probabilities, sort, rank, partition, group-by, collections, etc. – Parallel execution, bitmap indexes, partitioning, aggregations, recursion w/in parallel infrastructure, IEEE float, frequent itemsets, Automatic Data Preparation (ADP), Text processing, etc. • Focus on intelligent ML defaults, simplification & automation to enable applications – ADP, xforms, binning, missing values, Prediction_Details, Predictive_Queries, Model_views • Machine learning models built via PL/SQL script; scored via SQL functions (1st class DB objects) select cust_id from customers True power evident when scoring where region = ‘US’ models using SQL functions, e.g. and prediction_probability(churnmod, ‘Y’ using *) > 0.8; – “Smart scan” ML model scoring “push down”; Supports OLTP and ATPC environments • Machine Learning and Advanced Analytics are peer to rest of Oracle Data Mgmt features – Security, Back-up, Encryption, Scalability, “Big Data” eco-system, BDA, Big Data SQL, Cloud, Spark, etc. – Best ML & analytical development and deployment platform

Traditional ML Oracle’s in-DB Machine Learning

Major Benefits Data Import

Data Mining . Data remains in Database & Hadoop Model “Scoring”

. Model building and scoring occur in-database Data Prep. & . Use R packages with data-parallel invocations Transformation avings . Leverage investment in Oracle IT Data Mining Model Building . Eliminate data duplication

. Eliminate separate analytical servers Data Prep & . Deliver enterprise-wide applications Transformation . GUI for ML/Predictive Analytics & code gen

Model “Scoring” . R interface leverages database as HPC engine Data Extraction Embedded Data Prep Model Building Data Preparation Hours, Days or Weeks Secs, Mins or Hours

CLASSIFICATION REGRESSION FEATURE EXTRACTION – Naïve Bayes – Linear Model – Principal Comp Analysis (PCA) – Logistic Regression (GLM) – Generalized Linear Model – Non-negative Matrix Factorization – Decision Tree – Support Vector Machine (SVM) – Singular Value Decomposition (SVD) – Random Forest – Stepwise Linear regression – Explicit Semantic Analysis (ESA) – Neural Network – Neural Network – Support Vector Machine – LASSO TEXT MINING SUPPORT – Explicit Semantic Analysis – Algorithms support text type ATTRIBUTE IMPORTANCE A1 A2 A3 A4 A5 A6 A7 – Tokenization and theme extraction CLUSTERING – Minimum Description Length – Explicit Semantic Analysis (ESA) for – Hierarchical K-Means – Principal Comp Analysis (PCA) document similarity – Hierarchical O-Cluster – Unsupervised Pair-wise KL Div – Expectation Maximization (EM) – CUR decomposition for row & AI STATISTICAL FUNCTIONS – Basic statistics: min, max, ANOMALY DETECTION ASSOCIATION RULES median, stdev, t-test, F-test, – One-Class SVM – A priori/ market basket Pearson’s, Chi-Sq, ANOVA, etc. TIME SERIES PREDICTIVE QUERIES R PACKAGES – State of the art forecasting using – Predict, cluster, detect, features – CRAN R Algorithm Packages Exponential Smoothing. through Embedded R Execution – Includes all popular models SQL ANALYTICS – Spark MLlib algorithm integration e.g. Holt-Winters with trends, – SQL Windows, SQL Patterns, seasons, irregularity, missing data SQL Aggregates EXPORTABLE ML MODELS • OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined – REST APIs for deployment • OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc, Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Multiple Data Scientist User Roles Supported Oracle’s Machine Learning/Advanced Analytics

Additional relevant data Historical or Current Data to and “engineered be “scored” for predictions features” Oracle Historical data Assembled Predictions & Insights historical data Database 12c Sensor data, Text, unstructured data, transactional data, spatial data, etc.

DBAs Application Developers

R, Python Users, Data Scientists OML Notebook Users Data Analysts, Citizen Data Scientists

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | OAA Model Build and Real-time SQL Apply Prediction Simple SQL Syntax ML Model Build (PL/SQL) begin dbms_data_mining.create_model('BUY_INSUR1', 'CLASSIFICATION', 'CUST_INSUR_LTV', 'CUST_ID', 'BUY_INSURANCE', CUST_INSUR_LTV_SET'); end; /

Model Apply (SQL query) Select prediction_probability(BUY_INSUR1, 'Yes' USING 3500 as bank_funds, 825 as checking_amount, 400 as credit_balance, 22 as age, 'Married' as marital_status, 93 as MONEY_MONTLY_OVERDRAWN, 1 as house_ownership) from dual;

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; / Automated Monthly “Application”! Just -- Top 5 most suspicious fraud policy holder claims select * from add: (select POLICYNUMBER, round(prob_fraud*100,2) percent_fraud, Create rank() over (order by prob_fraud desc) rnk from View CLAIMS2_30 (select POLICYNUMBER, prediction_probability(CLAIMSMODEL, '0' using *) prob_fraud As from CLAIMS Select * from CLAIMS2 where PASTNUMBEROFCLAIMS in ('2to4', 'morethan4'))) Where mydate > SYSDATE – 30 where rnk <= 5 order by percent_fraud desc; Time measure: set timing on;

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | ML Model Deployment for Real-Time Scoring Oracle Cloud Real-Time Scoring, Predictions and Recommendations • On-the-fly, single record apply with new data (e.g. from call center)

Select prediction_probability(CLAS_DT_1_15, '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;

Social Media Likelihood to respond: Call Center Get AdviceBranch Office R

Web Mobile

Architecturally, Many Options and Flexibility SQL / R Boil down the Data Lake “Engineered Features” Big Data SQL / R – Derived attributes that reflect domain knowledge—key to best models e.g.: • Counts Object • Totals Store • Changes over time

Algorithm Time (sec.) Number of Attributes SVM SGD Solver 83s 900 GLM Classification 180s 9000 SVM IPM Solver 811s 900 GLM Regression 59s 900 K-Means 168s 9000

• In-memory 640 million rows, Airline On-Time dataset • SPARC M8-2 hardware • Courtesy of the SPARC Performance Team

Random Forest 200 180 160 140 120 100

80 Time(sec.) 60 40 20 0 100 150 200 250 300 350 400 450 500 550 Row Count (M records) • X6-2 hardware • Benchmarks from Oracle Labs, Systems Research Group

? Length of time to put a model into production. Based on 141 respondents who ? stated they are doing this today

Domain Knowledge + Data Machine Learning Algorithms Insights, Predictions

Machine Learning Algorithms

A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 A6 A7 A6 A7

Machine Learning Algorithms A “Thinking” Database A1 A2 A3 A4 A5 A6 A7

Easy Fast Elastic • Provision a data warehouse in as little • Up to 14x performance advantage than • Only Pay for What you Use with user as 15-seconds Redshift1 defined sizing, on-demand scaling & • Automated management of database • High concurrency supports multi-user idle shut-off administration access and workloads • Independent scaling of compute and • Simple Load and Go with Automated • Based on Exadata for extreme storage Tuning performance • Instant scaling with zero downtime • Dedicated cloud-ready migration tools including Redshift

High-Performance Queries Highly Elastic and Concurrent Workloads Independently scale compute and Optimized query performance with storage, without having to overpay for preconfigured resource profiles for different fixed blocks of resources types of users Oracle Machine Learning Oracle SQL Built-in Web-Based SQL ML Tool Autonomous DW Cloud is compatible with Apache Zeppelin Oracle Machine Learning all business analytics tools that support notebooks ready to run ML from browser Oracle Database

Self Driving Database migration utility Fully automated database for self-tuning Dedicated cloud-ready migration tools patching and upgrading itself while the for easy migration from Amazon system is running Redshift, SQL Server and other databases

Cloud-Based Data Loading Enterprise Grade Security Fast, scalable data-loading from Oracle Data is encrypted by default in the cloud, Object Store, AWS S3, or on-premises as well as in transit and at rest

Developer Analytics Tools DataAutonomous Warehouse Data Services Warehouse Cloud (EDWs, DW, departmental marts and sandboxes) Service Management Built-in Access Tools Oracle Exadata Cloud Service Oracle SQL Developer Oracle Analytics Cloud Oracle Database Cloud Service Service Console Oracle Machine Learning Express Cloud Service Data Integration Services

Oracle Data Integration Platform Cloud Autonomous Database Cloud

3rd Party DI on Oracle Cloud Compute Oracle Object Storage Cloud Flat Files and Staging

3rd Party DI On-premises

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 21 Oracle Machine Learning Machine Learning Notebook for Autonomous Data Warehouse Cloud Key Features • Collaborative UI for data scientists – Packaged with Autonomous Data Warehouse Cloud (V1) – Easy access to shared notebooks, templates, permissions, scheduler, etc. – SQL ML algorithms API (V1) – Supports deployment of ML analytics

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Oracle Machine Learning Machine Learning Notebook for Autonomous Data Warehouse Cloud Key Features • Collaborative UI for data scientists – Packaged with Autonomous Data Warehouse Cloud (V1) – Easy access to shared notebooks, templates, permissions, scheduler, etc. – SQL ML algorithms API (V1) – Supports deployment of ML analytics

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Additional ML and AA Product Details

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Oracle Data Miner GUI Oracle Cloud Drag and Drop, Workflows, Easy to Use UI 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

SQL Joins and arbitrary SQL Transactional transforms & queries – power of SQL POS data Modeling Approaches

Inline predictive Consider: model to augment • Demographics Generates SQL scripts input data • Past purchases and workflow API for • Recent purchases deployment Unstructured data • Comments & tweets also mined by algorithms

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 54 Oracle Data Miner “Workflow” UI Oracle Cloud Easy to use for “Citizen Data Scientist”; Fast to Deploy via SQL and PL/SQL Scripts • SQL Developer Step 1. “Citizen Data Scientist” builds, tests, applies Extension ML methodologies using DS IDEs (ODMr, RStudio) • Easy to use to define analytical methodologies that can be shared • Workflow API and generates SQL code for deployment Step 2.  Insights & Predictions in Database for OAC/DV user additional Viz/Analytics

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Oracle R Enterprise Oracle Cloud R Language API Component to OAA Machine Learning Functions and SQL • R language – R to SQL transparency layer for “push down” to SQL for in-DB processing • Direct access to in-DB data – ROracle pkg for OCI connectivity • Call outs to R pkgs via Embedded R

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

Database Server

Oracle Database 12c Other R R-> SQL R R Engine Other R packages packages

Oracle R Enterprise (ORE) packages Oracle R Enterprise packages Results Results

1 R-> SQL Transparency “Push-Down” 2 In-Database Adv Analytical SQL Functions 3 Embedded R Package Callouts • R language for interaction with the database • 30+ Powerful data mining algorithms • R Engine(s) spawned by Oracle DB for • R-SQL Transparency Framework overloads R (regression, clustering, AR, DT, etc._ database-managed parallelism functions for scalable in-database execution • Run Oracle Data Mining SQL data mining • ore.groupApply high performance scoring • Function overload for data selection, functioning (ORE.odmSVM, ORE.odmDT, etc.) • Efficient data transfer to spawned R manipulation and transforms • Speak “R” but executes as proprietary in- engines • Interactive display of graphical results and database SQL functions—machine learning • Emulate map-reduce style algorithms and flow control as in standard R algorithms and statistical functions applications • Submit user-defined R functions for • Leverage database strengths: SQL parallelism, • Enables production deployment and execution at database server under control scale to large datasets, security automated execution of R scripts of Oracle Database • Access big data in Database and Hadoop via SQL, R, and Big Data SQL

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Oracle R Advanced Analytics for Hadoop (ORAAH) Oracle Cloud R Language API Component to OAA Machine Learning Functions and SQL • R interface on BDA/BDA Cloud – Manipulate data stored in a local File System, HDFS, HIVE, Impala or JDBC sources – General computation framework where users invoke parallel, distributed MapReduce jobs from R, writing custom mappers and reducers in R while also leveraging open source CRAN packages. • Parallel and distributed Machine Learning algorithms • Leverage Hadoop nodes and Spark for cluster scalable, high performance • Functions use the expressive R formula object for Spark parallel execution –ORAAH's custom LM/GLM/MLP NN algorithms scale better and run faster than the open- source Spark Mllib. Also Expose Mllib algorithms • Availability • On premise: Part of Oracle Big Data Connectors license Oracle BDA • Cloud: Part of Oracle Big Data Cloud Service and Oracle Big Data Cloud at Customer

Objectives . “Oracle Advanced Analytics’ data mining capabilities and Oracle Exalytics’ performance really impressed us. The overall solution is . Use new insight to help identify cost savings and meet goals very fast, and our investment very quickly provided value. We can . Identify and prevent healthcare fraud and benefit eligibility now do so much more with our data, resulting in significant errors to save costs savings for the NHS as a whole” . Leverage existing data to transform business and productivity – Nina Monckton, Head of Information Services, NHS Business Services Authority Solution

. Identified up to GBP100 million (US$156 million) potentially Update: £300M confirmed fraud saved through benefit fraud and error reduction £700M additional potential identified . Used anomaly detection to uncover fraudulent activity where some dentists split a single course of treatment into multiple in savings parts and presented claims for multiple treatments £1 Billion . Analyzed billions of records at one time to measure longer- …Moving to Cloud term patient journeys and to analyze drug prescribing patterns Oracle Exadata Database Oracle Exalytics In-Memory to improve patient care Machine Machine Oracle Endeca Information Oracle Advanced Discovery Analytics Oracle Business Intelligence EE

Objectives . “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 . Cloud-based solution board faster. This has helped boost revenues by 25% in the six . Increase revenue months since using Oracle’s DBCS..” . Reduce time-to-market – DX Marketing Solution

Oracle Cloud

Oracle Advanced Analytics

DX Marketing Expands Customer Acquisition with Oracle Cloud – YouTube video

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

Objectives . “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 . Prevent $200M in losses every year using data to with production processes.” monitor, understand and anticipate fraud – Miguel Barrera, Director of Risk Analytics, Fiserv Inc. – Julia Minkowski, Risk Analytics Manager, Fiserv Inc. Solution

. We installed OAA analytics for model development Oracle Advanced Analytics during 2014 . When choosing the tools for fraud management, speed is a critical factor . 3 months OAA provided a fast and flexible solution for model to run & deploy 1 day to estimate and building, visualization and integration with production Logistic 1 week to deploy Regression estimate, 1 Trees + GLM models processes (using SAS) 1 month to estimate and week to install (using Oracle Advanced deploy Trees and rules Analytics) GLM in online application

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | New in 18c: Machine Learning and Advanced Analytics • Autonomous Data Warehouse Cloud + OML • In-DB Machine Learning Algorithms (SQL API) – Scalable Random Forests for Classification – Scalable Neural Networks for both classification and regression – CUR decomposition algorithm for attribute and row importance – Time Series: State of the art forecasting1 using Exponential Smoothing. • Includes all popular models e.g. Holt-Winters. Supports trend, damped trend and seasonality. Performs automatic aggregations based on user define time periods or handles irregularly spaced data as direct input. Handles missing values. • OML Microservices for applications deployment

1 Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Gartner paper Oracle Advanced Analytics 12.2 New Oracle Database Features • Significant Performance Improvements for all Algorithms – New parallel model build / apply redesigned infrastructure to enable faster new algorithm introduction – Scale to larger data volumes found in big data and cloud use cases • Unsupervised Feature Selection – Unsupervised algorithm for pair-wise correlations (Kullback-Leibler Divergence (KLD)) for numeric & 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 – Can filter input items prior to market basket analysis Partition 1 • Partitioned Models – Instead of building, naming and referencing 10s or 1000s of models, partitioned Partition 2… models organize and represent multiple models as partitions in a single model entity Partition …N

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Oracle Advanced Analytics 12.2 New Oracle Database Features • Explicit Semantic Analysis (ESA) algorithm – Useful technique for extracting meaningful, interpretable features; better than LDA – English Wikipedia is Text corpus default to equate tokens with human identifiable features and concepts – ESA improves text processing, classification, document similarity and topic identification – Compare documents that may not even mention same topics e.g. al-Qa ida or Osama bin Laden: 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 Score = 0.62

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | New in 18c: Oracle Advanced Analytics Oracle R Enterprise 1.5.1 • New algorithms via ORE API: – Expectation Maximization – Explicit Semantic Analysis – Text mining capabilities • OREdplyr support for dplyr functionality using ore.frames • R version compatibility upgraded to R 3.3.0 • Faster ORE launch and connection times • OAAgraph for Parallel Graph Analytics

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Oracle R Advanced Analytics for Hadoop (ORAAH) New Features in ORAAH 2.8 • ORAAH analytics as standalone Java library • New MPI/Spark-based Algorithms – ELM and H-ELM on Spark+MPI – Distributed Stochastic PCA & SVD • Updated ORAAH GLM and LM algorithms • Neural Networks full formula processing in Spark • Full formula support and summary for orch.lm2() and orch.glm2 • Support for IMPALA and Spark DataFrames • Gradient Boosted Trees (Spark MLlib)

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 74 ORAAH: Spark + MPI Combined Performance • Spark Strengths – Excellent for embarrassingly parallel problems – Poor at iterative problems • MPI Strengths – Excels for highly interconnected problems with thousands of nodes – Strong scaling for iterative problems + • Spark + MPI Combined Performance – FASTER! Best of both worlds

HCM Cloud Industry Data Models Workforce Predictions Comms, SNA, Utilities, Airlines, Retail, … CRM Sales Cloud Sales Prediction Retail GBU Oracle E-Business Suite Customer Insights, Spend Classification Customer Segmentation Oracle Identity Mgmt Adaptive Intelligent Appl Adaptive Access Mgmt for Manufacturing FSGBU CPQ Cloud Analytical Applications Configure, Price, Quote Infrastructure

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Enabling Predictive Enterprise Applications HCM Predictive Workforce • Integrated data management + embedded predictive analytics • Full 360 degree employee view • Single source of HCM data data • Interactive dashboards and “What if” analysis

Additional relevant data Historical or Current Data to and “engineered features” • Customizable if desired to add input be “scored” for predictions variables to predictive models Oracle Database 12c

Historical data Assembled Predictions & Insights • Mobile + Oracle Cloud solutions historical data

Sensor data, Text, unstructured data, transactional data, spatial Link to HCM Predictive Workforce demo data, etc.

• Identify employees likelihood to leave and predicted performance • Top reasons for expected behavior • Interactive "What if?“ analysis

Link to Oracle HCM on O.com Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | HCM Predictive Workforce demo Oracle Adaptive Intelligent (AI) Apps for Manufacturing Achieve Manufacturing Operational Excellence using Machine Learning & AI ML Use Cases • Insights (Patterns and Correlations Analysis) – Discover key influencers and patterns that affect yield & quality Screen shots, conceptual • Predictive Analytics diagram – Predictive critical outcomes during manufacturing to minimize losses Reasons why using/like OAA’s ML • Easy-to-integrate R & PL/SQL APIs for many ML algorithms • In-database execution & scalable performance • Enterprise grade support for OAA ML Screen shots, conceptual GA Q4FY18 diagram

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Configure, Price, Quote (CPQ) Accelerate Sales, Improve Margins, and Control Sales Operations Functionality • CPQ Cloud introduced machine learning Price Optimization for Transactions • Predicts the likelihood that a customer will buy at the current level of pricing of a Transaction • Win Probability feature to guide sales users in their negotiation of pricing and discounts Advantages of Using OAA • In-Database; • Ease of development, integration and deployment GA December 2017

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Application Store-Level Customer Segmentation Retail Store Assortments Based on Local Customer Preferences Machine Learning Use Cases • Optimize store product assortments • Coordinate marketing and merchandising • Guide 1-to-1 promotions Reasons why using • Eliminate data movement and scale to 50 million Sku/store combinations • One version of truth from central data warehouse • Security

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Market Basket and Demand Transference Insights Demand Transference Analysis Appl’s ML Use Cases • Gain actionable insights into customer behavior • Map product attribute affinities to customer attributes • Optimize shelf assortment breadth and depth Reasons why using/like OAA’s ML in Appl • Easy citizen scientist user workflow • Algorithmic extensibility • Security

“Why Oracle? Because that’s where the data is!”

– Larry Ellison, Executive Chairman and CTO of Oracle Corporation

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Getting Started—Oracle ML/AA Resources & Links Oracle Advanced Analytics Overview Information • Oracle's Machine Learning and Advanced Analytics 12.2c and Oracle Data Miner 4.2 New Features preso • Oracle Advanced Analytics Public Customer References • Oracle’s Machine Learning and Advanced Analytics Data Management Platforms white paper on OTN • Oracle INTERNAL ONLY OAA Product Management Wiki and Beehive Workspace (contains latest presentations, demos, product, etc. information) YouTube recorded Oracle Advanced Analytics Presentations and Demos, White Papers • Oracle's Machine Learning & Advanced Analytics 12.2 & Oracle Data Miner 4.2 New Features YouTube video • Library of YouTube Movies on Oracle Advanced Analytics, Data Mining, Machine Learning (7+ “live” Demos e.g. Oracle Data Miner 4.0 New Features, Retail, Fraud, Loyalty, Overview, etc.) • Overview YouTube video of Oracle’s Advanced Analytics and Machine Learning Getting Started/Training/Tutorials  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 Try the Oracle Cloud Now!  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, Documentation & OTN Discussion Forums • 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

Analytics and Data Summit , All Analytics, All Data, No Nonsense. March 12-14, 2019, Redwood Shores, CA •

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | www.biwasummit.org Formerly called the BIWA Summit with the Spatial and Graph Summit. Same great technical content – great new name!