Part 1 (of a planned series of blog posts):  Here are a few of my favorite Oracle Data Miner demo workflows.  They all are simple, easy to create examples of data mining and predictive analytics using Oracle Advanced Analytics and SQL Developer's Oracle Data Miner extension.

CUST_INSUR_LTV

Oracle Data Miner ships with some small datasets to get users started including INSUR_CUST_LTV_SAMPLE (1,015 records).  While this tiny dataset doesn't bloat the SQL Developer download size and helps get Oracle Data Miner users quickly up and running, the data size is so small that the the resulting predictive models and insights can seem at times a bit trivial.  Hence, I prefer to use larger files that ship with the Oracle 12c Database Sample Examples (SH.schema, MINING_DATA_BUILD, etc.) and this CUST_INSUR_LTV demo data:  

CUST_INSUR_LTV.DMP (~25K records, ~25 attributes)  

CUST_INSUR_LTV_APPLY.DMP (~25K records, ~25 attributes)  

Predicting Insurance Buyers ODMr workflow

You can import the workflow and and datasets and everything should run.  

This workflow includes an Explore node and Graph node that are typically used to visualize the data before performing data mining.  The Explore node step is important to make sure the data you are about to analyze makes sense and seems accurate and reasonable.  For example, AGE should all be positive numbers and range from 0 to say 100+.

The Column Filter node performs data profiling and data quality checks on the data and is also used to perform an Attribute Importance analysis to determine which attributes (or input variables) have the largest correlation with the target attribute (Buy_Insurance).  Sometimes this step alone provides significant value to a company to better understand the key factors, but here, we're also using it to better understand which attributes have the most inpact on our business problem--targeting customers who are likley to buy insurance.  Note:  Each of the OAA/ODM algorithms have their own embedded attribute importance/feature selection capabilties and each can handle hundreds to thousands of input attributes.  However, many times we want to get a feel for what's driving our business problem and learn where we could focus to pull in additional attributes and "engineered features" e.g "AGE/INCOME ratio" or "Maximum_Amount" etc..  

We build four (4) Oracle Data Mining Classification models by default (Decision Tree, Naive Bayes, GLM Logistic Regression and Support Vector Machine (SVM)).  For simplicity, we accept the ODMr defaults for Data Preparation and Algorithm Settings and can be assured that with Oracle Data Miner default settings, we should achieve a "good predictive model".

Decision Trees generally produce good predictive models and have the added benefit of being easy to understand. Notice the IF.... THEN... rules.

Lastly, we use the Apply node and our Classification node to make predictions on our CUST_INSUR_LTV_APPLY table and get our predictions.  

The predictions and associated Prediction_Details are stored inside the Oracle Database and hence easily available for inclusion in any BI Dashboard or real-time application.  

Oracle Data Miner generates the PL/SQL and SQL scripts for accelerating deploying analytical methodologies that leverage the scalability and infrstructure of the Oracle Database.  See this Oracle Data Miner: Use Reposiitory APIs to Manage and Schedule Workflows to Run White Paper for more details on the many model deployment options. 

Hope you enjoy!

Charlie 

Oracle Customer:Zagrebačka Bank (Zagrebačka banka d.d.)
Location:  Zagreb, Croatia
Industry:Financial Services
Employees:  4,500
Annual Revenue:  $1 to $5 Billion

Zagrebačka Bank (Zagrebačka banka d.d., ZABA) is the biggest bank in Croatia, one of the country’s largest employers, and part of the Italian Unicredit Group where it regularly belongs among the most profitable subsidiaries. Through its 130 branches and 850 automatic teller machines (ATM), the bank serves 80,000 corporate customers and more than 1.1 million private customers nationwide, making one in four citizens a ZABA customer. ZABA accounts for 25% of the Croatian banking sector’s total assets and almost 60% of its profits. The bank controls 35% of the country’s investment funds, 41% of obligatory pension funds, and 30% of specialized savings accounts for real estate transactions. Euromoney and Global Finance publications named ZABA Croatia’s best bank in 2011.

 A word from Zagrebačka Bank (Zagrebačka banka d.d.)

“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 Business Intelligence Development, Zagrebačka Bank 

Challenges

• Increase bank performance of statistical modeling and predictive analytics, which can take three days for data preparation and model scoring and at least 24 hours for model building
• Improve ZABA’s business agility by increasing the efficiency of building and testing predictive models in the Oracle Database platform rather than moving large volumes of financial and customer data between servers and databases to generate predictive analytics
• Gain the ability to use predictive analytics for commercial activities in order to better target customers for new banking products and services
• Reduce the risks and costs of executing statistical modeling which requires transfers of data to individual analytical servers with dedicated hardware resources using specialized manpower
• Strengthen prediction power by increasing model hit ratio and making it easy for analysts to refresh scores

Solutions

• Used Oracle Advanced Analytics on Oracle Database to transform traditional predictive analytics that could take days to prepare and execute all tasks, including data import and export, into a seamless process executed in seconds, minutes, or hours—increasing cash loans by 15% within 18 months due to improved hit ratio
• Saved 1,000 man-days in IT maintenance per year by using Oracle Exadata, Oracle GoldenGate, and Oracle Data Integrator for data warehouse integration with extension to predictive and statistical modeling using Oracle Advanced Analytics Database
• Increased prediction performance and delivered comprehensive statistical functionality for in-database computation 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
• Enabled analysts to leverage in-database mining algorithms with both Oracle Advanced Analytics’ Oracle Data Mining and Oracle’ R enterprise components to achieve much faster access to analytics data such as credit risk scoring and customer retention, without needing to transfer data between servers and databases for statistical modeling
• Empowered the bank’s IT team to focus on business-triggered statistical modeling—for example to increase the hit ratio of target customers for a newly developed credit card or to improve customer retention—rather than spending most of their time addressing regulatory projects
• Empowered the organization to improve prediction accuracy by simplifying the development effort for analytics and minimizing developer intervention during model design, making it easy for analysts to refresh scores such as customer retention, with an updated data set
• Strengthened decision-making across the bank’s regulatory and commercial activities by delivering business analytics more rapidly, for example enabling risk managers to improve credit risk scorings with fast ad-hoc analysis
• Facilitated enterprise-wide access to statistics and advanced analytics through delivery of Oracle Advanced Analytics’ actionable insights via Oracle Business Intelligence Enterprise Editiondashboards and business applications
• Improved total cost of ownership by eliminating the need for dedicated analytical servers and improving development and execution time of business analytics by 30%, thanks to Oracle Advanced Analytics in-database architecture
• Incorporated Oracle Database as an enterprisewide analytical platform—eliminating the need for two dedicated administrators focused on server maintenance and administrative tasks
• Ensured compliance with regulatory demands in terms of consistent, punctual filing of regulatory reports—avoiding heavy penalties from the Croatian National Bank
• Why Oracle?

“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,” said Sinisa Behin, ICT coordinator at business intelligence development, Zagrebačka Bank.

“Our plan is to maximize coverage of our commercial activities, so that each process of campaign management and sales of financial products and services is empowered by statistical analysis. Oracle’s engineered high-performance platform met our analytical requirements for a low cost of ownership,” said Lidija Glavinic, ICT coordinator at business intelligence development, Zagrebačka Bank.

Implementation Process

The deployment of Oracle Advanced Analytics represented the final stage of ZABA’s consolidation on Oracle technology. The organization migrated current clients, products, and portfolio models together while starting to develop predictive analytics for its commercial department from scratch. The retention model migration from SAS to the Oracle Advanced Analytics platform was one of the first migrations of this type in the region. Oracle’s Advanced Analytics Development team specifically designed the functionality of clustering variables for ZABA and implemented the Oracle R Enterprise Varclus package in the 1.5 release.

Partners:

Zagrebačka Bank collaborated with Oracle Partner Combis to successfully migrate its predictive models from SAS to the Oracle Advanced Analytics platform. Combis completed the migration on time and trained ZABA employees in the use of Oracle tools.

Multicom d.o.o
Combis d.o.o
Neos d.o.o

Oracle Product and Services

• Oracle Database
• Oracle Advanced Analytics
• Oracle GoldenGate
• Oracle Business Intelligence Enterprise Edition
• Oracle Enterprise Performance Management
• Oracle Exadata

Oracle Advanced Analytics (OAA) Database Option leverages Oracle Text, a free feature of the Oracle Database, to pre-process (tokenize) unstructured data for ingestion by the OAA data mining algorithms.  By moving, parallelized implementations of  machine learning algorithms inside the Oracle Database, data movement is eliminated and we can leverage other strengths of the Database such as Oracle Text (not to mention security, scalability, auditing, encryption, back up, high availability, geospatial data, etc.

This Mining Structured Data and Unstructured Data using Oracle Advanced Analytics 12c YouTube video presents an overview of the capabilities for combining and performing data mining on both structured and unstructured data.  The YouTube includes several quick demonstrations on classification and clustering using unstructured data and provides instructions and links on how to get started--either on premise or on the Oracle Cloud.  

I hope you find this helpful and a pleasure to watch.   

Presentation Slides.

You can also access similar YouTube videos at this Oracle Data Mining at the Movies blog posting.

Follow CharlieDataMine on Twitter.

Thanks for watching!

Charlie

Sr. Dir. of Product Management, Oracle Advanced Analytics and Data Mining

charlie.berger@oracle.com 

Machine Learning in the Cloud from Oracle?   Yes!  

Did you know that Oracle's Database as a a Cloud Service High and Extreme Editions bundle Oracle Advanced Analytics Database Option?

High Performance: Multitenant, Partitioning, Real Application Testing, Advanced Compression, Advanced Security, Label Security, Database Vault, OLAP, Advanced Analytics, Spatial and Graph, Diagnostics Pack, Tuning Pack, Database Lifecycle Management Pack, Data Masking & Subsetting Pack and Cloud Management Pack for Oracle Database.

Extreme Performance: In-Memory Database, Active Data Guard, Multitenant, Partitioning, Real Application Testing, Advanced Compression, Advanced Security, Label Security, Database Vault, OLAP, Advanced Analytics, Spatial and Graph, Diagnostics Pack, Tuning Pack, Database Lifecycle Management Pack, Data Masking & Subsetting Pack and Cloud Management Pack for Oracle Database.

Now, you can get a "Smart" Database in the Cloud with Oracle Advanced Analytics.  

After all, who wants a "Dumb" Database anyway?!  

For more information on Oracle Advanced Analytics on-premise or in the Cloud, visit Oracle Advanced Analytics on the Oracle Technology Network (OTN) or view this latest Oracle's Advanced Analytics - Making Big Data + Analytics Simple! presentation.

Also, you might be interested in the DX MarketingOAA in the Cloud Customer Success Story.  

There are two YouTube testimonials as well as the story write up.

See everyone on the Oracle Advanced Analytics machine learning Cloud!

Charlie 

THE Big Data + Analytics + Spatial + Cloud + IoT + Everything “Cool"
Oracle User Conference 2017

January 31 – February 2, 2017

Oracle Conference Center at Oracle Head Quarters Campus, Redwood Shores, CA

What Oracle Big Data + Analytics + Spatial + Cloud + IoT + Everything “Cool” Successes Can You Share?

We want to hear your story. Submit your proposal today for OracleBIWA Summit 2017, January 31– February 2, 2017 and share your successes with Oracle technology. Speaker proposals now are being accepted through October 1, 2016. Submit now for possible early acceptance and publication in OracleBIWA Summit 2017promotion materials.

Presentations must be non-commercial. Sales promotions for products or services disguised as proposals will be eliminated. Speakers whose abstracts are accepted will be expected to submit at a later date a presentation outline and presentation PDF slide deck. Accompanying technical and use case papers are encouraged, but not required.

Click HERE  to submit your abstract(s) for OracleBIWA Summit 2017.

BIWA Summits are organized and managed by the Oracle Business Intelligence, Data Warehousing and Analytics (BIWA) SIG, the Oracle Spatial and Graph SIG—both Special Interest Groups in the Independent Oracle User Group (IOUG), and the Oracle Northern California User Group. BIWA Summits attract presentations and talks from the top BI, DW, Advanced Analytics, Spatial, and Big Data experts. The 3-day BIWA Summit 2016 event involved Keynotes by Industry experts, Educational sessions, Hands-on Labs and networking events. Click HERE to see presentations and content from BIWA Summit 2016.

Call for Speaker DEADLINE is October 1, 2016 at midnight Pacific Time.

Complimentary registration to OracleBIWA Summit 2017 is provided to the primary speaker of each accepted abstract. 

Note: One complimentary registration per accepted session will be provided. Any additional co-presenters need to register for the event separately and provide appropriate registration fees. It is up to the co-presenters’ discretion which presenter to designate for the complimentary registration.

Please submit speaker proposals in one of the following tracks:

• Advanced Analytics
• Business Intelligence
• Big Data + Data Discovery
• Data Warehousing and ETL
• Cloud
• Internet of Things
• Spatial and Graph
• …Anything else “Cool” using Oracle technologies in “novel and interesting” ways
  
  

Learn from Industry Experts from Oracle, Partners, and Customers

Come join hundreds of professionals with shared interests in the successful deployment of Oracle Business Intelligence, Data Warehousing, IoT and Analytical products:

What To Expect

500+ Attendees | 90+ Speakers | Hands on Labs | Technical Content| Networking

Exciting Topics Include: 

·Database, Data Warehouse, and Cloud, Big Data Architecture

·Deep Dives on existing Oracle BI, DW and Analytics products and Hands on Labs

·Updates on the latest Oracle products and technologies e.g. Oracle Big Data Discovery, Oracle Visual Analyzer, Oracle Big Data SQL

·Novel and Interesting Use Cases of Everything! Spatial, Text, Data Mining, ETL, Security, Cloud

·Working with Big Data: Hadoop, "Internet of Things", SQL, R, Sentiment Analysis

·Oracle Big Data Discovery, Oracle Business Intelligence (OBIEE), Oracle Spatial and Graph, Oracle Advanced Analytics—AllBetter Together

Example Talks from BIWA Summit 2016: (Need to update pictures from 2016)

 [Visit www.biwasummit.org to see the last year’s Full Agenda from BIWA’16 and to download copies of BIWA’16 presentations and HOLs.]

Advanced Analytics

§Dogfooding – How Oracle Uses Oracle Advanced Analytics To Boost Sales Efficiency, Frank Heilland, Oracle Sales and Support

§Fiserv Case Study: Using Oracle Advanced Analytics for Fraud Detection in Online Payments, Julia Minkowski, Fiserv

§Enabling Clorox as Data Driven Enterprise, Yigal Gur, Clorox

§Big Data Analytics with Oracle Advanced Analytics 12c and Big Data SQL and the Cloud, Charlie Berger, Oracle

§Stubhub and Oracle Advanced Analytics, Brian Motzer, Stubhub

§Fault Detection using Advanced Analytics at CERN's Large Hadron Collider: Too Hot or Too Cold, Mark Hornick, Oracle

§Large Scale Machine Learning with Big Data SQL, Hadoop and Spark, Marcos Arancibia, Oracle

§Oracle R Enterprise 1.5 - Hot new features!, Mark Hornick, Oracle

BI and Visualization

§Electoral fraud location in Brazilian General Elections 2014, Alex Cordon, Henrique Gomes, CDS

§See What’s There and What’s Coming with BICS & Data Visualization, Philippe Lions, Oracle

§Optimize Oracle Business Intelligence Analytics with Oracle 12c In-Memory Database option, Kai Yu, Dell

§BI Movie Magic: Maps, Graphs, and BI Dashboards at AMC Theatres, Tim Vlamis, Vlamis

§Defining a Roadmap for Migrating to Oracle BI Applications on ODI, Patrick Callahan, AST Corp.

§Free form Data Visualization, Mashup BI and Advanced Analytics with BI 12c, Philippe Lions, Oracle

Big Data

§How to choose between Hadoop, NoSQL or Oracle Database , Jean-Pierre Djicks, Oracle

§Enrich, Transform and Analyse Big Data using Big Data Discovery and Visual Analyzer, Mark Rittman, Rittman Mead

§Oracle Big Data: Strategy and Roadmap, Neil Mendelson, Oracle

§High Speed Video Processing for Big Data Applications, Melliyal Annamalai, Oracle

§How to choose between Hadoop, NoSQL or Oracle Database, Shyam Nath, General Electric

§What's New With Oracle Business Intelligence 12c, Stewart Bryson, Red Pill

§Leveraging Oracle Big Data Discovery to Master CERN’s Control Data, Antonio Romero Marin, CERN

Cloud Computing

§Hybrid Cloud Using Oracle DBaaS: How the Italian Workers Comp Authority Uses Graph Technology, Giovanni Corcione, Oracle

§Oracle DBaaS Migration Road Map, Daniel Morgan, Forsythe Meta7

§Safe Passage to the CLOUD – Analytics, Rich Solari, Privthi Krishnappa, Deloitte

§Oracle BI Tools on the Cloud--On Premise vs. Hosted vs. Oracle Cloud, Jeffrey Schauer, JS Business Intelligence

Data Warehousing and ETL

§Making SQL Great Again (SQL is Huuuuuuuuuuuuuuuge!) , Panel Discussion, Andy Mendelsohn, Oracle, Steve Feuerstein, Oracle, George Lumpkin, Oracle

§The Place of SQL in the Hybrid World, Kerry Osborne and Tanel Poder, Accenture Enkitec Group

§Is Oracle SQL the best language for Statistics, Brendan Tierney, Oralytics

§Taking Full Advantage of the PL/SQL Compiler, Iggy Ferenandez, Oracle

Internet of Things

§Industrial IoT and Machine Learning - Making Wind Energy Cost Competitive, Robert Liekar, M&S Consulting

Spatial Summit

§Utilizing Oracle Spatial and Graph with Esri for Pipeline GIS and Linear Asset Management, Dave Ellerbeck, Global Information Systems

§Oracle Spatial and Graph: New Features for 12.2, Siva Ravada, Oracle

§High Performance Raster Database Manipulation and Data Processing with Oracle Spatial and Graph, Qingyun (Jeffrey) Xie, Oracle

Example Hands-on Labs from BIWA Summit 2016:

§Scaling R to New Heights with Oracle Database, Mark Hornick, Oracle, Tim Vlamis, Vlamis Software

§Learn Predictive Analytics in 2 hours!! Oracle Data Miner 4.1, Charlie Berger, Oracle, Brendan Tierney, Oralytics, Karl Rexer, Rexer Analytics

§Predictive Analytics using SQL and PL/SQL, Oracle Brendan Tierney, Oralytics, Charlie Berger, Oracle

§Oracle Data Visualization Cloud Service Hands-On Lab with Customer Use Cases, Pravin Patil, Kapstone

Lunch & Partner Lightning Rounds

§Fast and Fun 5 Minute Presentations from Each Partner--Must See!

Submit your abstract(s) today, good luck and hope to see you there!

See last year’s Full Agenda from BIWA’16.

Dan Vlamis and Shyam Nath , Oracle BIWA Summit '17Conference Co-Chairs

BIWA Summit’17- THE BigData + Analytics+ Spatial+ Cloud +IoT + Everything CoolOracle User Conference

January 31 - February 2, 2017 @ the OracleHQ Conference Center in Redwood Shores, CA.

The BIWA Summit’17, held in conjunction with Spatial Summit’17 at the Oracle HQ Conference Center, annually draws several hundred customers, partners and Oracle experts who share best practices, “novel and interesting” use cases, customer case studies, and present on emerging technologies.   Featured talks by Oracle executives, technical sessions delivered by experts and user friendly introductory talks make this a great event for customers.  There are several 2 hour Hands on Labs running Oracle products on the Oracle Public Cloud where users can learn how to use Oracle software.   

See www.biwasummit.org for full agenda and registration information.

BIWA and Spatial Summit’17 Agenda: Tuesday, January 31 + Reception sponsored by Deloitte

BIWA and Spatial Summit’17 Agenda: Wednesday, February 1 + Reception sponsored by L&T InfoTech

BIWA and Spatial Summit’17 Agenda: Thursday, February 2

NOTE:  BIWA and Spatial IOUG SIGs are managed and run as independent organizations.  Oracle Employees (who are not speakers) must register as any customer does.  However, you can use the BIWAALUMNI discount code when registering to receive a $150 discount.

Hope to see everyone at BIWA Summit’17!

Charlie

Official Event Page

“THE Big Data + Analytics + Spatial + Cloud + IoT + Everything Cool User Conference”

• Date:
  January 31st – February 2nd
• Location:
  Oracle Convention Center
       Oracle Campus
       350 Oracle Pkwy
       Redwood City, Ca

New Blog Template Coming Soon....  I don't know much more than that, but Oracle blogger HQ informs us that we'll be receiving a new blog template and process, so stay tuned.  Hopefully it is better!

Charlie 

Oracle's Machine Learning and Advanced Analytics 12.2 andOracle Data Miner 4.2 New Features.  Thispresentation highlights the new machine learning algorithms, features,functions and "differentiators" added to Oracle Database Release 12.2and Oracle SQL Developer4.2. Thesefeatures and functioned are "packaged" as part of the Oracle AdvancedAnalytics Database Option and Oracle Data Miner workflow UI on-premise and inthe Oracle Database Cloud Service High and Extreme Editions. I hope you enjoy the video!

Oracle's Machine Learning and Advanced Analytics 12.2 and Oracle Data Miner 4.2 New Features! on YouTube 

Download the Oracle's Machine Learning and Advanced Analytics 12.2 and Oracle Data Miner 4.2 New Features .pdf presentation.

Slide excerpts:

Charlie Berger

charlie.berger@oracle.com

Blog Posting By Alex Sakharov, Principal Member of Technical Staff, Data Mining Technologies

Explicit Semantic Analysis (ESA), a new feature in Oracle Advanced Analytics Release 12.2, uses concepts of an existing knowledge base as features rather than latent features derived by latent semantic analysis methods such as Singular Value Decomposition and Latent Dirichlet Allocation. Each row e.g. a document in the training data maps to a feature, i.e. a concept. ESA works best with concepts represented by text documents. It has multiple applications in the area of text processing, most notably semantic relatedness (similarity) and explicit topic modeling.   Text similarity use cases might involve e.g. resume matching, searching for similar blog postings, etc..  OAA’s ESA derived similarity indexes can be used as added new features for other records e.g. Candidate, Age, Income, Job_description Similarity_index_score.

The ESA model is basically an inverted index that maps words to relevant concepts of the knowledge base. This inverted index also incorporates weights reflecting the strength of association between words and concepts. ESA does not project the original feature space and does not reduce its dimensionality except for filtering out features with uninformative text.

There exist vast amounts of knowledge represented as text. Textual knowledge bases are normally collections of common or domain-specific articles, and every article defines one concept. These textual knowledge bases such as Wikipedia usually serve as sources for ESA models. Wikipedia is particularly good as a source for a general-purpose ESA model because Wikipedia is a comprehensive knowledge base.  Users can develop and add and use their own custom, domain specific ESA models e.g. medical, homeland security, research & development, etc.

Please refer to https://docs.oracle.com/database/122/DMAPI/explicit-semantic-analysis.htm for more information about ESA.

Distribution

Oracle distributes an ESA model built in 12.2.0.1 from the following 2016 Wikipedia dump https://dumps.wikimedia.org/enwiki/.  The dump dated November 1, 2016 was used for the building of this ESA model.

See Oracle Machine Models to download the ESA Model 1.0 EN.  The model file is wiki_model12.2.0.1.dmp. The distribution includes two scripts: wiki_esa_setup.sql, wiki_esa_demo.sql. The wiki_esa_setup.sql script defines a text policy. The wiki_esa_demo.sql script contains sample queries for the model.

Setup

This is how to load this model into your DB given that you use the scott/tiger account provided in Oracle DBs. It is done similarly for other accounts. First, you execute the following sql as sysdba in order to grant necessary privileges to this account:

SQL> GRANT CREATE ANY DIRECTORY TO SCOTT;
Grant succeeded.

SQL> GRANT EXECUTE ON CTXSYS.CTX_DDL TO SCOTT;
Grant succeeded.

SQL> GRANT CREATE MINING MODEL TO SCOTT;
Grant succeeded.

The minimum recommended size of the tablesspace is 1G.

SQL> CREATE TABLESPACE <your tablespace> DATAFILE '<directory>/<file>' SIZE 1G REUSE AUTOEXTEND ON MAXSIZE UNLIMITED;
Tablespace created.

It is necessary to to define a DB directory in order to import a model.

SQL> CREATE OR REPLACE DIRECTORY DBDIR AS '<directory>';
Directory created.

SQL> ALTER USER SCOTT QUOTA UNLIMITED ON <your tablespace>;
User altered.

Second, you need to copy wiki_model12.2.0.1.dmp to your directory. After that, you execute this command in the shell:

impdp scott/tiger dumpfile=wiki_model12.2.0.1.dmp directory=DBDIR remap_schema=DMUSER:SCOTT remap_tablespace=TBS_1:TBS

Alternatively, you may execute the following sql code to achieve the same result:

SQL> begin
dbms_data_mining.import_model (
             filename => 'wiki_model12.2.0.1.dmp',
             directory =>'DBDIR',
             schema_remap => 'DMUSER:SCOTT',
             tablespace_remap => 'TBS_1:TBS'
);
end; 
/
PL/SQL procedure successfully completed.

The imported model name is WIKI_MODEL. You can explore the imported model via view DM$VAWIKI_MODEL. If you use your own DB account, then make sure that the same privileges as for SCOTT are granted to that account.

Now you can run:

SQL> @wiki_esa_setup.sql

This script sets up the text policy wiki_txtpol which is referred to from the model. Make sure that the size of SGA is sufficient for fast scoring. The minimum recommended settings are:

sga_max_size=1G

sga_target=1G

Once SGA is properly sized, you can run sample scoring queries:

SQL> @wiki_esa_scoring.sql

Scoring

All queries against WIKI_MODEL score textual data. These data should be given as one column named TEXT. If your textual data comes from a table column, this column should be aliased to TEXT. Text policy wiki_txtpol should be defined before scoring. Scoring function feature_set is used for topic modeling, and function feature_compare is used for semantic similarity.

Explicit topic modeling

The ESA Wikipedia model helps discover the most relevant topics for a given text document. It could be a short text such as a singular word or a long document. Please see relevant Wikipedia topics for word 'bank':

SQL> select s.feature_id, s.value from
    (select feature_set(wiki_model, 10 using *) fset from
    (SELECT 'bank' AS text FROM dual)) t,
    table(t.fset) s order by s.value desc;

FEATURE_ID                              VALUE
----------------------------------------  ------
Bank                                            .101
Bank of America                          .099
National bank                               .099
Central bank                                .099
National Bank Act                        .096

The next example shows Wikipedia topics for one sentence:

SQL> select s.feature_id, s.value from
      (select feature_set(wiki_model, 10 using *) fset from
       (SELECT 'A group of European-led astronomers has made a photograph of what appears to be a planet orbiting another star. If so, it would be the first confirmed picture of a world beyond our solar system.'
  AS text FROM dual)) t, table(t.fset) s order by s.value desc;

FEATURE_ID                                                  VALUE
----------------------------------------                      ------
Solar System                                                  .144
Exoplanet                                                        .138
Planet                                                              .138
Formation and evolution of the Solar System .127
Planetary system                                            .127

Here is yet another example in which topic modeling is done for a paragraph:

SQL> select s.feature_id, s.value from
    (select feature_set(wiki_model, 10 using *) fset from
    (SELECT 'The more things change... Yes, I''m inclined to agree, especially with regards to the historical relationship between stock prices and bond yields. The two have generally traded together, rising during periods of economic growth and falling during periods of contraction. Consider the period from 1998 through 2010, during which the U.S. economy experienced two expansions as well as two recessions: Then central banks came to the rescue. Fed Chairman Ben Bernanke led from Washington with the help of the bank''s current $3.6T balance sheet. He''s accompanied by Mario Draghi at the European Central Bank and an equally forthright Shinzo Abe in Japan. Their coordinated monetary expansion has provided all the sugar needed for an equities moonshot, while they vowed to hold global borrowing costs at record lows' AS text FROM dual)) t,
table(t.fset) s order by s.value desc;

FEATURE_ID                                 VALUE
----------------------------------------     ------
Recession                                       .147
Mario Draghi                                   .138
Lost Decade (Japan)                      .132
Ben Bernanke                                .120
Federal Open Market Committee   .093

Semantic similarity

The ESA Wikipedia model can be used to calculate semantic similarity. One can score semantic similarity for short and long documents alike. The following two queries capture the fact that words 'street' and 'avenue' are semantically closer than 'street' and 'farm'.

SQL> select 1-feature_compare(wiki_model using 'street' as text and using 'avenue' as text) comp from dual;
  COMP
------
  .235
SQL>
SQL> select 1-feature_compare(wiki_model using 'street' as text and using 'farm' as text) comp from dual;
  COMP
------
  .004

In the next example, the first pair of sentences scores higher because Nick Price is a golfer born in South Africa. Note that the two sentences from the first pair have no common words.

SQL> SELECT 1-FEATURE_COMPARE(wiki_model USING 'There are several PGA tour golfers from South Africa' text AND USING 'Nick Price won the 2002 Mastercard Colonial Open' text) comp FROM DUAL;
  COMP
------
  .119
SQL>
SQL> SELECT 1-FEATURE_COMPARE(wiki_model USING 'There are several PGA tour golfers from South Africa' text AND USING 'John Elway played quarterback for the Denver Broncos' text) comp FROM DUAL;
  COMP
------
  .003

In the following example, one paragraph referring to al Qa ida and Saudi Arabia is compared to two other paragraphs. The first counterpart paragraph refers to similar matters even though it does not mention al Qa ida or Osama bin Laden. The first pair of paragraphs scores a high similarity according to a Wikipedia-based ESA model. The second counterpart paragraph refers to unrelated topics. The second pair of paragraphs scores low as expected.

SQL> select 1-feature_compare(wiki_model using
  '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.' as text
  and using
  '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.' as text) comp from dual;
  COMP
------
  .583
SQL>
SQL> select 1-feature_compare(wiki_model using
  '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.' as text
  and using
  'Russia defended itself against U.S. criticism of its economic ties with countries like Iraq, saying attempts to mix business and ideology were misguided. "Mixing ideology with economic ties, which was characteristic of the Cold War that Russia and the United States worked to end, is a thing of the past," Russian Foreign Ministry spokesman Boris Malakhov said Saturday, reacting to U.S. Defense Secretary Donald Rumsfeld statement that Moscow economic relationships with such countries sends a negative signal.' as text) comp from dual;
  COMP
------
  .095

References

E. Gabrilovich and S. Markovitch. Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis, IJCAI, v. 7, pp. 1606-1611, 2007

E. Gabrilovich and S. Markovitch. Wikipedia-based Semantic Interpretation for Natural Language Processing. Journal of Artificial Intelligence Research, v. 34, pp. 443-498, 2009.

The Big Data + Cloud + Machine Learning + Spatial + Graph + Analytics + IoT
Oracle User Conference

featuring Oracle Spatial and Graph Summit

March 20 - 22, 2018

Oracle Conference Center at Oracle Headquarters Campus, Redwood Shores, CA

Share your successes…

We want to hear your story. Submit your proposal today for OracleBIWA Summit 2018, featuring Oracle Spatial and Graph Summit, March 20 - 22, 2018 and share your successes with Oracle technology. The call for speakers is now open through December 3, 2017.  Submit now for possible early acceptance and publication in OracleBIWA Summit 2018 promotion materials. 

Click HERE  to submit your abstract(s) for OracleBIWA Summit 2018.

Oracle Spatial and Graph Summit will be held in partnership with BIWA Summit.  BIWA Summits are organized and managed by the Oracle Business Intelligence, Data Warehousing and Analytics (BIWA) User Community and the Oracle Spatial and Graph SIG– a Special Interest Group in the Independent Oracle User Group (IOUG).

BIWA Summits attract presentations and talks from the top Business Intelligence, Data Warehousing, Advanced Analytics, Spatial and Graph, and Big Data experts. The 3-day BIWA Summit 2017 event involved Keynotes by Industry experts, Educational sessions, Hands-on Labs and networking events. Click HERE to see presentations and content from BIWA Summit 2017.

Call for Speaker DEADLINE is December 3, 2017 at midnight Pacific Time.

Presentations and Hands-on Labs must be non-commercial. Sales promotions for products or services disguised as proposals will be eliminated.  Speakers whose abstracts are accepted will be expected to submit their presentation as PDF slide deck for posting on the BIWA Summit conference website.  Accompanying technical and use case papers are encouraged, but not required.

Complimentary registration to OracleBIWA Summit 2018 is provided to the primary speaker of each accepted presentation. Note:  Any additional co-presenters need to register for the event separately and provide appropriate registration fees.   

Please submit session proposals in one of the following areas:

• Machine Learning
• Analytics
• Big Data
• Data Warehousing and ETL
• Cloud
• Internet of Things
• Spatial and Graph (Oracle Spatial and Graph Summit)
• …Anything else “Cool” using Oracle technologies in “novel and interesting” ways

Proposals that cover multiple areas are acceptable and highly encouraged.  On your submission, please indicate a primary track and any secondary tracks for consideration.  The content committee strongly encourages technical/how to sessions, strategic guidance sessions, and real world customer end user case studies, all using Oracle technologies.

If you submitted a session last year, your login should carry over for 2018. We will be accepting abstracts on a rolling basis, so please submit your abstracts as soon as possible.

Learn from Industry Experts from Oracle, Partners, and Customers

Come join hundreds of professionals with shared interests in the successful deployment
of Oracle technology on premises, on Cloud, hybrid Cloud, and infrastructure:

• Database  Cloud Service

• Big Data Cloud Service
• Data Visualization Cloud Service
• Hadoop
• Spark
• Big Data Connectors
  (Hadoop & R)
• IaaS, PaaS, SaaS

• Spatial and Graph for Big Data and Database
• GIS and smart cities features
• Location intelligence
• Geocoding & routing
• Property graph DB
• Social network, fraud detection, deep learning graph analytics
• RDF graph

• Oracle Data Visualization
• Big Data Discovery
• OBIEE
• OBIA Applications
• Exalytics
• Real-Time Decisions

• Machine Learning

• Advanced Analytics

• Data Mining

• R Enterprise

• Fraud detection

• Text Mining

• SQL Patterns

• Clustering

• Market Basket Analysis

• Big Data Preparation

• Big Data from sensors
• Edge Analytics
• Industrial Internet
• IoT Cloud
• Monetizing IoT
• Security
• Standards

What To Expect

400+ Attendees | 90+ Speakers | Hands on Labs | Technical Content| Networking

New at this year’s BIWA Summit:

• Strategy track– targeted at the C-level audience, how to assess and plan for new Oracle Technology in meeting enterprise objectives
• Oracle Global Leaders track– sessions by Oracle’s Global Leader customers on their use of Oracle Technology, and targeted product managers on latest Oracle products and features
• Grad-student track– sessions on cutting edge university work using Oracle Technology, continuing Oracle Academy’s sponsorship of graduate student participation

 Exciting Topics Include: 

• Database, Data Warehouse, and Cloud, Big Data Architecture
• Deep Dives on existing Oracle BI, DW and Analytics products and Hands on Labs
• Updates on the latest Oracle products and technologies e.g. Oracle Big Data Discovery, Oracle Visual Analyzer, Oracle Big Data SQL
• Novel and Interesting Use Cases of Spatial and Graph, Text, Data Mining, ETL, Security, Cloud
• Working with Big Data:  Hadoop, "Internet of Things", SQL, R, Sentiment Analysis
• Oracle Business Intelligence (OBIEE), Oracle Spatial and Graph, Oracle Advanced Analytics
  —AllBetter Together

Example Talks from BIWA Summit 2017:

 [Visit www.biwasummit.org to see the  Full Agenda from BIWA’17 and to download copies of BIWA’17 presentations and HOLs.]

Machine Learning

• Taking R to new heights for scalability and performance
• Introducing Oracle Machine Learning Zeppelin Notebooks
• Oracle's Advanced Analytics 12.2c New Features & Road Map: Bigger, Better, Faster, More!
• An Post -- Big Data Analytics platform and use of Oracle Advanced Analytics
• Customer Analytics POC for a global retailer, using Oracle Advanced Analytics
• Oracle Marketing Advanced Analytics Use of OAA in Propensity to Buy Models
• Clustering Data with Oracle Data Mining and Oracle Business Intelligence
• How Option Traders leverage Oracle R Enterprise to maximize trading strategies
• From Beginning to End - Oracle's Cloud Services and New Customer Acquisition Marketing
• K12 Student Early Warning System
• Business Process Optimization Using Reinforcement Learning
• Advanced Analytics & Graph: Transparently taking advantage of HW innovations in the Cloud
• Dynamic Traffic Prediction in Road Networks
• Context Aware GeoSocial Graph Mining

Analytics

• Uncovering Complex Spatial and Graph Relationships: On Database, Big Data, and Cloud
• Make the most of Oracle DV (DVD / DVCS / BICS)
• Data Visualization at SoundExchange – A Case Study
• Custom Maps in Oracle Big Data Discovery with Oracle Spatial and Graph 12c
• Does Your Data Have a Story? Find out with Oracle Data Visualization Desktop
• Social Services Reporting, Visualization, and Analytics Using OBIEE
• Leadership Essentials in Successful Business Intelligence (BI) Programs

Big Data

• Uncovering Complex Spatial and Graph Relationships: On Database, Big Data, and Cloud
• Why Apache Spark has become the darling in Big Data space?
• Custom Maps in Oracle Big Data Discovery with Oracle Spatial and Graph 12c
• A Shortest Path to Using Graph Technologies– Best Practices in Graph Construction, Indexing, Analytics and Visualization

Cloud Computing

• Oracle Big Data Management in the Cloud
• Oracle Cloud Cookbook for Professionals
• Uncovering Complex Spatial and Graph Relationships: On Database, Big Data, and Cloud
• Deploying Oracle Database in the Cloud with Exadata: Technical Deep Dive
• Employee Onboarding: Onboard – Faster, Smarter & Greener
• Deploying Spatial Applications in Oracle Public Cloud
• Analytics in the Oracle Cloud: A Case Study Deploying SAS Retail Analytics in the Oracle Cloud
• BICS - For Departmental Data Mart or Enterprise Data Warehouse?
• Cloud Transition and Lift and Shift of Oracle BI Applications

Data Warehousing and ETL

• Business Analytics in the Oracle 12.2 Database: Analytic Views
• Maximizing Join and Sort Performance in Oracle Data Warehouses
• Turbocharging Data Visualization and Analyses with Oracle In-Memory 12.2
• Oracle Data Integrator 12c: Getting Started
• Analytic Functions in SQL
• My Favorite Scripts 2017

Internet of Things

• Introduction to IoT and IoT Platforms
• The State of Industrial IoT
• Complex Data Mashups: an Example Use Case from the Transportation Industry
• Monetizable Value Creation from Industrial-IoT Analytics

Spatial and Graph Summit

• Uncovering Complex Spatial and Graph Relationships: On Database, Big Data, and Cloud
• A Shortest Path to Using Graph Technologies– Best Practices in Graph Construction, Indexing, Analytics and Visualization
• Build Recommender Systems, Detect Fraud, and Integrate Deep Learning with Graph Technologies
• Building a Tax Fraud Detection Platform with Big Data Spatial and Graph technologies
• Maps, 3-D, Tracking, JSON, and Location Analysis: What’s New with Oracle’s Spatial Technologies
• Deploying Spatial Applications in Oracle Public Cloud
• RESTful Spatial services with Oracle Database as a Service and ORDS
• Custom Maps in Oracle Big Data Discovery with Oracle Spatial and Graph 12c
• Smart Parking for a Smart City
• Using Oracle Spatial and Graph at Los Angeles and Munich Airports
• Analysing the Panama Papers with Oracle Big Data Spatial and Graph
• Apply Location Intelligence and Spatial Analysis to Big Data with Java

 Example Hands-on Labs from BIWA Summit 2017:

• Using R for Big Data Advanced Analytics and Machine Learning
• Learn Predictive Analytics in 2 hours!  Oracle Data Miner Hands on Lab
• Deploy Custom Maps in OBIEE for Free
• Apply Location Intelligence and Spatial Analysis to Big Data with Java
• Use Oracle Big Data SQL to Analyze Data Across Oracle Database, Hadoop, and NoSQL
• Make the most of Oracle DV (DVD / DVCS / BICS)
• Analyzing a social network using Big Data Spatial and Graph Property Graph

Submit your abstract(s) today, good luck and hope to see you there!

See last year’s Full Agenda from BIWA’17.

Dan Vlamis and Shyam Nath , Oracle BIWA Summit '18 Conference Co-Chairs

It's easy to evaluate Oracle Advanced Analytics!  There are multiple possibilities. 

1. Get onto an Oracle Cloud.  Depending on your configuration, you may also want to connect to the Cloud using the Oracle Data Miner "workflow" UI.  To get Oracle Data Miner, you'll need SQL Developer and then will need to configure SQL Developer for Oracle Data Miner.  To get started, you'll need to enter a credit card but you'll get $300 of Cloud credits.   
   
    
2. Download and Install an evaluation copy of the Oracle Database 12.2 This may actually be the easiest, simplest way to get started as you can download and install the Oracle Database in about an hour these days and you can evaluate it for free!  Oracle has a honor system so when you are done "evaluating" and are actually using the Oracle software for you job, you are supposed to see your Rep and pay for it.  
   
    
3. Download SQL Developer.  The latest release is now called 17.2 but was previously 4.2.  SQL Developer is the popular IDE for working with the Oracle Database.  Oracle Data Miner "workflow" UI is packaged as an extension so it ships with SQL Developer, but you still have to configure it for data mining/data scientist users.  
   
4. Follow the Tutorials.   There are several free tutorials that show users how to get started.  They are excellent learning vehicles for both machine learning/data mining and how to use Oracle's product functionality.  Several use cases are presented.  You MUST start with the first tutorial to Setting Up SQL Developer for Oracle Data Miner.
   
5. Follow the Other Tutorials.   The Using Oracle Data Miner is the first, easiest and best tutorial for getting started.  It's fast, easy and fun!  Once you follow the first tutorials you are must better prepared to begin using Oracle Advanced Analytics and Oracle Data Miner on your own data.  
   

Good luck!

Charlie

There were a number of great presentations  on Oracle's Machine Learning and Advanced Analytics at Oracle Open World 2017.  Here are links to many of them.   I'll update this list posting as I collect more and can post the great presentations and resources.

Hope you enjoy!

Case Study:  Oracle’s Advanced Analytics at UK National Health Service

The Naked Future: What Happens in a World that Anticipates Your Every Move? 

Operationalizing Machine Learning into “Predictive” Enterprise Applications

Predictive HCM Using Machine Learning Data Management Platforms 

Transformational Machine Learning Use Cases You Can Deploy Now

Siebel CRM CAB

Advanced Analytics at EnergyAustralia

Is SQL the Best Language for Statistics and Machine Learning?

SQL: One Language to Rule All Your Data

Ireland's An Post: Customer Analytics Using Oracle Analytics Cloud

Extending Garanti Bank’s Data Management Platform with Oracle Big Data SQL

General Session: Data and Analytics Power Your Success

Big Data and Machine Learning and the Cloud, Oh My!

Big Data Best Practices: A Workflow from Object Store to Analytics in the Cloud

Larry Ellison's Sunday Night Keynote:  Oracle Cloud:  Industry's Broadest, Most Integrated

Thomas Kurian's Keynote:  Oracle's Integrated Cloud Platform

Demo Pod:  Oracle’s Machine Learning for the Cloud, Databases and Big Data using SQL, R and Notebooks

Charlie Berger | Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics
Charlie.berger@oracle.com
Oracle Machine Learning and Advanced Analytics on OTN
Oracle Big Data Blog, Oracle Data Mining Blog, Twitter:  CharlieDataMine

Analytics and Data Summit 2018  All Analytics.  All Data.  No Nonsense.  User Conference, Mar 20-22, 2018 - Join us!

Please Help Spread the Word to Customers and Partners!  [OK to share this blog link]  

We’ve changed our name! Formerly called the BIWA Summit with the Spatial and Graph Summit.

Same great technical content – great new name!

Submit your proposal today for Analytics and Data Summit 2018, and share your successes with Oracle technology. Submit now for possible early acceptance and publication in Oracle Analytics and Data Summit 2018 promotion materials.   See guidelines for submissions. 

Hope you can present your “novel and interesting use cases” of Oracle’s various Analytics and Data technologies or, at least, join us at Oracle HQ in Redwood Shores March 20-22 to hear stories, lessons learned and new developments as shared by leading Oracle customers, partners and technical experts!   There will also be a number of 2 hour Hands on Labs, taught by experts, to get fast training on Oracle products and technologies—including Cloud, Machine Leaning, Spatial, Graph, Analytics, SQL, R, Spark, Hadoop, etc.

Background:  The Analytics and Data Summit(formerly called the BIWA Summit) is run and managed by the BIWA User Group, Inc. in collaboration with the IOUG Spatial and Graph SIG, customers, partners and Oracle Dev. and PM volunteers.  It is not an Oracle Corp. run event.  As such, to attend Oracle Employees must REGISTER like any customer or partner or individual.  Should anyone wish to negotiate pre-purchased Analytics and Data Summit registrations, that can be explored with BIWA UG Inc..   SPONSORSHIP opportunities are still available.

Hope to see everyone at the Analytics and Data Summit March 20-22, 2018, at Oracle HQ, Redwood Shores, CA.!

Charlie

Charlie Berger | Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics
Charlie.berger@oracle.com
Oracle Machine Learning and Advanced Analytics on OTN
Oracle Big Data Blog, Oracle Data Mining Blog, Twitter:  CharlieDataMine

Many times I'm asked for more information on how to get started with Oracle’s Machine Learning and Advanced Analytics.

I put together this simple guide of the most popular and useful, in my opinion, links to product Information and getting started links and resources including:

• Oracle Advanced Analytics Database Option (OAA), included in Oracle Database Cloud High and Extreme Editions
  • Oracle Data Mining (SQL API Machine Learning functions)
  • Oracle Data Miner"workflow" UI (for Citizen Data Scientists) SQL Developer extension
  • Oracle R Enterprise (R API to ODM SQL ML functions, R to SQL "push down" and R integration)
• Oracle R Advanced Analytics for Hadoop (ORAAH) (part of the Big Data Connectors)

 OOW'17 Oracle's Machine Learning & Advanced Analytics Presentations

   Oracle Advanced Analytics Overview Information 

• Oracle's Machine Learning and Advanced Analytics 12.2c and Oracle Data Miner 4.2 New Featurespresentation
• Oracle Advanced Analytics Public Customer References
• Oracle’s Machine Learning and Advanced Analytics Data Management Platforms:  Move the Algorithms;  Not the Data 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 17.2 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
• ORAAH Online Training

   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 2018, March 20-22, 2018, at Oracle HQ in Redwood Shores, CA.
  All Analytics. All Data. No Nonsense.  User Conference
  March 20 - 22, 2018, Redwood Shores, CA

Hope this helps!

Charlie

Charlie Berger | Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics,
Oracle Corporation
Phone: +7817440324 | Mobile: +6033204560 |
10 Van de Graaff Drive | Burlington, MA 01803
LinkedIn:  www.linkedin.com/in/CharlieDataMine
Oracle Machine Learning and Advanced Analytics on OTN
Oracle Big Data Blog, Oracle Data Mining Blog, Twitter:  CharlieDataMine
Oracle Advanced Analytics internal PM Beehive Workspace
Analytics and Data Summit 2018  All Analytics.  All Data.  No Nonsense.  User Conference, Mar 20-22, 2018 - Join us!

Oracle Machine Learning is a new SQL notebook interface for data scientists to perform machine learning in the Oracle Autonomous Data Warehouse Cloud (ADWC).  Notebook technologies support the creation of scripts while supporting the documentation of assumptions, approaches and rationale to increase data science team productivity.  Oracle Machine Learning SQL notebooks, based on Apache Zeppelin technology, enable teams to collaborate to build, evaluate and deploy predictive models and analytical methodologies in the Oracle ADWC. Multi-user collaboration enables the same notebook to be opened simultaneously by different users.  Changes made by one user are immediately updated for other team members. 

Oracle Machine Learning SQL notebooks provide easy access to Oracle's parallelized, scalable in-database implementations of a library of Oracle Advanced Analytics' machine learning algorithms (classification, regression, anomaly detection, clustering, associations, attribute importance, feature extraction, times series, etc.), SQL, PL/SQL and Oracle's statistical and analytical SQL functions.  Oracle Machine Learning SQL notebooks and Oracle Advanced Analytics' library of machine learning SQL functions combined with PL/SQL allow companies to automate their discovery of new insights, generate predictions and add "AI" to data viz dashboards and enterprise applications.

To support enterprise requirements for security, authentication, and auditing, Oracle Machine Learning SQL notebooks adhere to all Oracle standards and supports privilege-based access to data, models, and notebooks.

Presentation:  Oracle Machine Learning SQL Notebook - Included in Oracle Autonomous Data Warehouse Cloud

• Collaborative SQL notebook UI for data scientists
• Packaged with Autonomous Data Warehouse Cloud 
• Easy access to shared notebooks, templates, permissions, scheduler, etc
• Access to 30+ parallel, scalable in-database implementations of machine learning algorithms
• SQL and PL/SQL scripting language supported
• Enables and Supports Deployments of Enterprise Machine Learning Methodologies in ADWC

Disclaimer:  Product details/functionality subject to change.

Oracle Machine Learning enables data science teams to collaboratively build machine learning methodologies in the Oracle ADWC.

OML notebooks provide easy access to data managed in the Oracle ADWC for quick analysis, simple visualizations and building machine learning solutions.

Oracle Machine Learning SQL notebook starting page.

Create a new Oracle Machine Learning notebook.

Create simple visualizations of data managed in Oracle ADWC.

Easily perform quick statistical analyses using Oracle's in-database SQL statistical functions.

Build, evaluate and deploy machine learning methodologies in the Oracle ADWC using Oracle Advanced Analytics' parallelized, in-database implementations of machine learning algorithms.