RevoDeployR and commercial BI using R and R based cloud computing using Open CPU

Revolution Analytics has of course had RevoDeployR, and in a  webinar strive to bring it back to center spotlight.

BI is a good lucrative market, and visualization is a strength in R, so it is matter of time before we have more R based BI solutions. I really liked the two slides below for explaining RevoDeployR better to newbies like me (and many others!)

Integrating R into 3rd party and Web applications using RevoDeployR

Please click here to download the PDF.

Here are some additional links that may be of interest to you:


( I still think someone should make a commercial version of Jeroen Oom’s web interfaces and Jeff Horner’s web infrastructure (see below) for making customized Business Intelligence (BI) /Data Visualization solutions , UCLA and Vanderbilt are not exactly Stanford when it comes to deploying great academic solutions in the startup-tech world). I kind of think Google or someone at Revolution  should atleast dekko OpenCPU as a credible cloud solution in R.

I still cant figure out whether Revolution Analytics has a cloud computing strategy and Google seems to be working mysteriously as usual in broadening access to the Google Compute Cloud to the rest of R Community.

Open CPU  provides a free and open platform for statistical computing in the cloud. It is meant as an open, social analysis environment where people can share and run R functions and objects. For more details, visit the websit:

and esp see

Jeff Horner’s

Jerooen Oom’s

R for Business Analytics- Book by Ajay Ohri

So the cover art is ready, and if you are a reviewer, you can reserve online copies of the book I have been writing for past 2 years. Special thanks to my mentors, detractors, readers and students- I owe you a beer!

You can also go here-


R for Business Analytics

R for Business Analytics

Ohri, Ajay

2012, 2012, XVI, 300 p. 208 illus., 162 in color.


ISBN 978-1-4614-4342-1

Due: September 30, 2012


approx. 44,95 €
  • Covers full spectrum of R packages related to business analytics
  • Step-by-step instruction on the use of R packages, in addition to exercises, references, interviews and useful links
  • Background information and exercises are all applied to practical business analysis topics, such as code examples on web and social media analytics, data mining, clustering and regression models

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics.


This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.

Content Level » Professional/practitioner

Keywords » Business Analytics – Data Mining – Data Visualization – Forecasting – GUI – Graphical User Interface – R software – Text Mining

Related subjects » Business, Economics & Finance – Computational Statistics – Statistics


Why R.- R Infrastructure.- R Interfaces.- Manipulating Data.- Exploring Data.- Building Regression Models.- Data Mining using R.- Clustering and Data Segmentation.- Forecasting and Time-Series Models.- Data Export and Output.- Optimizing your R Coding.- Additional Training Literature.- Appendix

Oracle R Updated!

Interesting message from the latest R blog



Oracle just released the latest update to Oracle R Enterprise, version 1.1. This release includes the Oracle R Distribution (based on open source R, version 2.13.2), an improved server installation, and much more.  The key new features include:

  • Extended Server Support: New support for Windows 32 and 64-bit server components, as well as continuing support for Linux 64-bit server components
  • Improved Installation: Linux 64-bit server installation now provides robust status updates and prerequisite checks
  • Performance Improvements: Improved performance for embedded R script execution calculations

In addition, the updated ROracle package, which is used with Oracle R Enterprise, now reads date data by conversion to character strings.

We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for R-related software: Oracle R DistributionOracle R EnterpriseROracleOracle R Connector for Hadoop.  As always, we welcome comments and questions on the Oracle R Forum.



Oracle R Distribution 2-13.2 Update Available

Oracle has released an update to the Oracle R Distribution, an Oracle-supported distribution of open source R. Oracle R Distribution 2-13.2 now contains the ability to dynamically link the following libraries on both Windows and Linux:

  • The Intel Math Kernel Library (MKL) on Intel chips
  • The AMD Core Math Library (ACML) on AMD chips


To take advantage of the performance enhancements provided by Intel MKL or AMD ACML in Oracle R Distribution, simply add the MKL or ACML shared library directory to the LD_LIBRARY_PATH system environment variable. This automatically enables MKL or ACML to make use of all available processors, vastly speeding up linear algebra computations and eliminating the need to recompile R.  Even on a single core, the optimized algorithms in the Intel MKL libraries are faster than using R’s standard BLAS library.

Open-source R is linked to NetLib’s BLAS libraries, but they are not multi-threaded and only use one core. While R’s internal BLAS are efficient for most computations, it’s possible to recompile R to link to a different, multi-threaded BLAS library to improve performance on eligible calculations. Compiling and linking to R yourself can be involved, but for many, the significantly improved calculation speed justifies the effort. Oracle R Distribution notably simplifies the process of using external math libraries by enabling R to auto-load MKL orACML. For R commands that don’t link to BLAS code, taking advantage of database parallelism usingembedded R execution in Oracle R Enterprise is the route to improved performance.

For more information about rebuilding R with different BLAS libraries, see the linear algebra section in the R Installation and Administration manual. As always, the Oracle R Distribution is available as a free download to anyone. Questions and comments are welcome on the Oracle R Forum.

Facebook Search- The fall of the machines

Increasingly I am beginning to search more and more on Facebook. This is for the following reasons-

1) Facebook is walled off to Google (mostly). While within Facebook , I get both people results and content results (from Bing).

Bing is an okay alternative , though not as fast as Google Instant.

2) Cleaner Web Results When Facebook increases the number of results from 3 top links to say 10 top links, there should be more outbound traffic from FB search to websites.For some reason Google continues to show 14 pages of results… Why? Why not limit to just one page.

3) Better People Search than  Pipl and Google. But not much (or any) image search. This is curious and I am hoping the Instagram results would be added to search results.

4) I am hoping for any company Facebook or Microsoft to challenge Adsense . Adwords already has rivals. Adsense is a de facto monopoly and my experiences in advertising show that content creators can make much more money from a better Adsense (especially ) if Adsense and Adwords do not have a conflict of interest from same advertisers.

Adwords should have been a special case of Adsense for but it is not.

5) Machine learning can only get you from tau to delta tau. When ad click behavior is inherently dependent on humans who behave mostly on chaotic , or genetic models than linear CPC models. I find FB has an inherent advantage in the quantity and quality of data collected on people behavior rather than click behavior. They are also more aggressive and less apologetic about behavorially targeted  ads.

Additional point- Analytics for Google Analytics is not as rich as analytics from Facebook pages in terms of demographic variables. This can be tested by anyone.


Understanding Indian Govt attitude to Iran and Iraq wars

This is a collection of links for a geo-strategic analysis, and the economics of wars and allies. The author neither condones nor condemns current global dynamics in the balance of power.

nations don’t have friends or enemies…nations only have interests

In 2003

The war in Iraq had a unique Indian angle right at the beginning. Some members of the US administration felt they needed more troops in Iraq, and they started negotiating with India. Those negotiations broke down because the Indians wanted to fight under the UN flag and on MONEY!!

India wanted-

  • More money per soldier deployed,
  • more share in post War Oil Contracts,
  • better diplomatic subtlety
Govt changed in India due to elections in2003 (Muslim voters are critical in any govt forming majority party), and the Iraq war ran its tragic course without any Indian explicit support.
In 26 Nov 2008, Islamic Terrorists killed US, Indian and Israeli citizens in terror strikes in Mumbai Sieze- thus proving that appeasing terrorist nations is just riding a tiger.

NEW DELHI: There will be a lot a Iraq on the menu over the weekend before the Pentagon team arrives here on Monday to talk India into sending troops to the war-torn nation.

Jul 28, 2003, 01.28pm IST

NEW DELHI: Chairman of the US Joint Chiefs of Staff Gen Richard B Myers, who is arriving here on Monday evening on a two-day visit, will request India to reconsider its decision on sending troops to Iraq.


Jul 29, 2003, 07.00pm IST

NEW DELHI: Though Gen Myers flatly denied his visit had anything to do with persuading India to send troops to Iraq, it is evident that the US desperately wants Delhi to contribute a division-level force of over 15,000 combat soldiers.

Sep 10, 2003, 05.34pm IST

NEW DELHI: Even as the US-drafted resolution on Iraq is being heatedly debated in many countries, American Assistant Secretary of State for South Asia Christina Rocca held a series of meetings with External Affairs Ministry officials on Wednesday.

Though it was officially called “a regional dialogue”, the US request to contribute a division-level force of over 15,000 combat soldiers to the “stabilisation force” in Iraq is learnt to have figured in the discussions.

The penny wise -pound foolish attitude of then Def Secretary Rumsfield led to break down in negotiations.

“Those who fail to learn from history are doomed to repeat it.” Sir Winston Churchill

In 2012

Indian govt again faces elections and we have 150 million Muslim voters just like other countries have influential lobbies.

and while Israelis are being targeted again in attacks in India-

India is still seeking money-

India has struck a defiant tone over new financial sanctions imposed by the United States and European Union to punish Iran for its nuclear programme, coming up with elaborate trade and barter arrangements to pay for oil supplies.

However, the president of the All India Rice Exporters’ Association, said Monday’s attack on the wife of an Israeli diplomat in the Indian capital will damage trade with Iran and may complicate efforts to resolve an impasse over Iranian defaults on payments for rice imports worth around $150 million.

India buys $ 5  billion worth of oil from Iran. Annually. Clearly it is a critical financial trading partner to Iran.

It has now gotten extra sops from Iran to continue trading-and is now waiting for a sweeter monetary offer from US and/or Israel to even consider thinking about going through the pain of unchanging the inertia of ties with Iran.

There are some aspects of political corruption as well, as Indian political establishment  is notoriously prone to corruption by lobbyists (apparently there   is a global war on lobbyists that needs to happen)

 Feb 14, 2012, 05.54PM ISTUnfazed by US sanctions, India to step up ties with Iran
India is set to ramp up its energy and business ties with Iran. (AFP Photo)
NEW DELHI: Unfazed by US sanctions and Israel linking Tehran to the attack on an Israeli embassy car here, India is set to ramp up its energy and business ties with Iran, with a commerce ministry team heading to Tehran to explore fresh business opportunities. 

The team is expected to go to Tehran later this month to discuss steps to expand India’s trade with Iran, part of a larger strategy to pay for Iranian oil, said highly-placed sources. 

Despite the US and European Union sanctions on Iran, India recently sealed a payment mechanism under which Indian companies will pay for 45 percent of their crude oil imports from Iran in rupees. 

So diplomats with argue over money in Israel, Indian and US while terrorists will kill.

Against Stupidity- The Gods Themselves -Contend in Vain

Jim Kobielus on 2012

Jim Kobielus revisits the predictions he made in 2011 (and a summary of 2010) , and makes some fresh ones for 2012. For technology watchers, this is an article by one of the gurus of enterprise software.


All of those trends predictions (at ) came true in 2011, and are in full force in 2012 as well.Here are my predictions for 2012, and the links to the 3 blogposts in which I made them last month:


The Year Ahead in Next Best Action? Here’s the Next Best Thing to a Crystal Ball!

  • The next-best-action market will continue to coalesce around core solution capabilities.
  • Data scientists will become the principal application developers for next best action.
  • Real-world experiments will become the new development paradigm in next best action.

The Year Ahead in Advanced Analytics? Advances on All Fronts!

  • Open-source platforms will expand their footprint in advanced analytics.
  • Data science centers of excellence will spring up everywhere.
  • Predictive analytics and interactive exploration will enter the mainstream BI user experience:

The Year Ahead In Big Data? Big, Cool, New Stuff Looms Large!

  • Enterprise Hadoop deployments will expand at a rapid clip.
  • In-memory analytics platforms will grow their footprint.
  • Graph databases will come into vogue.


And in an exclusive and generous favor for DecisionStats, Jim does some crystal gazing for the cloud computing field in 2012-

Cloud/SaaS EDWs will cross the enterprise-adoption inflection point. In 2012, cloud and software-as-a-service (SaaS) enterprise data warehouses (EDWs), offered on a public subscription basis, will gain greater enterprise adoption as a complement or outright replacement for appliance- and software-based EDWs. A growing number of established and startup EDW vendors will roll out cloud/SaaS “Big Data” offerings. Many of these will supplement and extend RDBMS and columnar technologies with Hadoop, key-value, graph, document, and other new database architectures.


James G. Kobielus James G. Kobielus
Senior Analyst



James serves Business Process & Application Development & Delivery Professionals. He is a leading expert on data warehousing, predictive analytics, data mining, and complex event processing. In addition to his core coverage areas, James contributes to Forrester’s research in business intelligence, data integration, data quality, and master data management.




James has a long history in IT research and consulting and has worked for both vendors and research firms. Most recently, he was at Current Analysis, an IT research firm, where he was a principal analyst covering topics ranging from data warehousing to data integration and the Semantic Web. Prior to that position, James was a senior technical systems analyst at Exostar (a hosted supply chain management and eBusiness hub for the aerospace and defense industry). In this capacity, James was responsible for identifying and specifying product/service requirements for federated identity, PKI, and other products. He also worked as an analyst for the Burton Group and was previously employed by LCC International, DynCorp, ADEENA, International Center for Information Technologies, and the North American Telecommunications Association. He is both well versed and experienced in product and market assessments. James is a widely published business/technology author and has spoken at many industry events.

Contact –


Ads Alliance on Internet

Just saw

the Digital Advertising Alliance’s (DAA) Self-Regulatory Program for Online Behavioral Advertising.

Multi-Site Data Collection Principles Broaden Self Regulation Beyond Online Behavioral Advertising

The new Principles consist of the following specific requirements:

  1. Transparency and consumer control for purposes other than OBA – The Multi-Site Data Principles call for organizations that collect Multi-Site Data for purposes other than OBA to provide transparency and control regarding Internet surfing across unrelated Websites.
  2. Collection / use of data for eligibility determination – The Multi-Site Data Principles prohibit the collection, use or transfer of Internet surfing data across Websites for determination of a consumer’s eligibility for employment, credit standing, healthcare treatment and insurance.
  3. Collection / use of children’s data – The Multi-Site Data Principles state that organizations must comply with the Children’s Online Privacy Protection Act (COPPA).
  4. Meaningful accountability – The Multi-Site Data Principles are subject to enforcement through strong accountability mechanisms.

The DAA Self-Regulatory Principles


The cross-industry Self-Regulatory Principles for Multi-Site Data augment the Self-Regulatory   Principles for Online Behavioral Advertising  (OBA)  by covering the prospective  collection of Web site   data beyond that collected for OBA purposes.  The existing OBA  Principles and definitions  remain in   full force and effect and are not limited by the new  principles.

The cross-industry Self-Regulatory Principles for Online Behavioral Advertising was developed by   leading industry associations to apply  consumer-friendly standards to online  behavioral advertising  across the Internet. Online behavioral advertising increasingly supports the convenient access to  content, services, and applications over the Internet that consumers have come to expect at no cost   to them.

The Education Principle calls for organizations to participate in efforts to educate individuals and businesses about online behavioral advertising and the Principles.

The Transparency Principle calls for clearer and easily accessible disclosures to consumers about data collection and use practices associated with online behavioral advertising. It will result in new, enhanced notice on the page where data is collected through links embedded in or around advertisements, or on the Web page itself.

The Consumer Control Principle provides consumers with an expanded ability to choose whether data is collected and used for online behavioral advertising purposes. This choice will be available through a link from the notice provided on the Web page where data is collected.

The Consumer Control Principle requires “service providers”, a term that includes Internet access service providers and providers of desktop applications software such as Web browser “tool bars” to obtain the consent of users before engaging in online behavioral advertising, and take steps to de-identify the data used for such purposes.

The Data Security Principle calls for organizations to provide appropriate security for, and limited retention of data, collected and used for online behavioral advertising purposes.

The Material Changes Principle calls for obtaining consumer consent before a Material Change is made to an entity’s Online Behavioral Advertising data collection and use policies unless that change will result in less collection or use of data.

The Sensitive Data Principle recognizes that data collected from children and used for online behavioral advertising merits heightened protection, and requires parental consent for behavioral advertising to consumers known to be under 13 on child-directed Web sites. This Principle also provides heightened protections to certain health and financial data when attributable to a specific individual.

The Accountability Principle calls for development of programs to further advance these Principles, including programs to monitor and report instances of uncorrected non-compliance with these Principles to appropriate government agencies. The CBBB and DMA have been asked and agreed to work cooperatively to establish accountability mechanisms under the Principles.


Ajay- So why the self regulations?

Answer- Shoddy Maths in behaviorally targeted ads is leading to a very high glut in targeted ads, more than can be reasonably expected to click based on consumer spending. On the internet- unlike on television- cost is less of a barrrier to OVER ADVERTISING.