PAW Reception and R Meetup

New DC meetup for R Users-

source- http://www.meetup.com/R-users-DC/calendar/14236478/

October’s R meet-up will be co-located with the Predictive Analytics World Conference (http://www.predictive…) taking place in Washington DC October 19-20. PAW is the premiere business-focused event for predictive analytics professionals, managers and commercial practitioners.

Agenda:

6:30 – 7:30 PAW Reception (open to meet-up attendees)
7:30 – 9:00 DC-R Meetup

Talks:
“How to speak ggplot2 like a native”
Harlan D. Harris, PhD @HarlanH

“Saving the world with R”
Michael Milton @michaelmilton

Important Registration Instructions:
You are welcome to RSVP here at meetup. The PAW organizers have requested that we register in the PAW site for the R meetup so they can provide badges to members which will give you access to the reception. There is no charge to register using the PAW site. Please click here to register.


Speaker Bios

Harlan D. Harris, PhD, is a statistical data scientist working for Kaplan Test Prep and Admissions in New York City. He has degrees from the University of Wisconsin-Madison and the University of Illinois at Urbana-Champaign. Prior to turning to the private sector, he worked as a researcher and lecturer in various areas of Artificial Intelligence and Cognitive Science at the University of Illinois, Columbia University, the University of Connecticut, and New York University.

Harlan’s talk is titled “How to speak ggplot2 like a native.”. One of the most innovative ideas in data visualization in recent years is that graphical images can be described using a grammar. Just as a fluent speaker of a language can talk more precisely and clearly than someone using a tourist phrasebook, graphics based on a grammar can yield more insights than graphics based on a limited set of templates (bar chart, pie graph, etc.). There are at least two implementations of the Grammar of Graphics idea in R, of which the most popular is the ggplot2 package written by Prof. Hadley Wickham. Just as with natural languages, ggplot2 has a surface structure made up of R vocabulary elements, as well as a deep structure that mediates the link between the vocabulary and the “semantic” representation of the data shown on a computer screen. In this introductory presentation, the links among these levels of representation are demonstrated, so that new ggplot2 users can build the mental models necessary for fluent and creative visualization of their data.

Michael Milton is a Client Manager at Blue State Digital. When he’s not saving the world by designing interactive marketing strategies that connect passionate users with causes and organizations, he writes about data and analytics. For O’Reilly Media, he wrote Head First Data Analysis and Head First Excel and has created the videos Great R: Level 1 and Getting the Most Out of Google Apps for Business.

Michael’s talk is called “How to Save the World Using R.” In this wide-ranging discussion, Michael will highlight individuals and organizations who are using R to help others as well as ways in which R can be used to promote good statistical thinking.

Red Hat worth 7.8 Billion now

I was searching for a Linux install of Revolution’s latest enterprise version, but it seems version 4 will be available on Red Hat Enterprise Linux only by Decemebr 2010. Also even though Revolution once opted for co branding with Canonical’s Karmic Koala, they seem to have ignored Ubuntu from the Enterprise version of Revolution R.

http://www.revolutionanalytics.com/why-revolution-r/which-r-is-right-for-me.php

Base R Revolution R Community Revolution R Enterprise
Buy Now
Target Use Open Source Product Evaluation & Simple Prototyping Business, Research & Academics
Software
100% Compatible with R language X X X
Certified for Stability X X
Command-Line Programming X X X
Getting Started Guide X X
Performance & Scalability
Analyze larger data sets with 64-bit RAM X X
Optimized for Multi-processor workstations X X
Multi-threaded Math libraries X X
Parallel Programming (Single Workstation) X X
Out-of-the-Box Cluster-Ready X
“Big Data” Analysis
Terabyte-Class File Structures X
Specialized “Big Data” Algorithms X
Integrated Web Services
Scalable Web Services Platform X*
User Interface
Visual IDE X
Comprehensive Data Analysis GUI X*
Technical Support
Discussion Forums X X X
Online Support Mailing List Forum X
Email Support X
Phone Support X
Support for Base & Recommended R Packages X X X
Authorized Training & Consulting X
Platforms
Single User X X X
Multi-User Server X X
32-bit Windows X X X
64-bit Windows X X
Mac OS X X X
Ubuntu Linux X X
Red Hat Enterprise Linux X
Cloud-Ready X

and though the page on RED HAT’s Partner page for Revolution seems old/not so updated

https://www.redhat.com/wapps/partnerlocator/web/home.html;#productId=188

, I was still curious to see what the buzz about Red Hat is all about.

And one of the answers is Red Hat is now a 7.8 Billion Dollar Company.

http://www.redhat.com/about/news/prarchive/2010/Q2_2011.html

Red Hat Reports Second Quarter Results

  • Revenue of $220 million, up 20% from the prior year
  • GAAP operating income up 24%, non-GAAP operating income up 25% from the prior year
  • Deferred revenue of $650 million, up 12% from the prior year

RALEIGH, NC – Sept 22, 2010 – Red Hat, Inc. (NYSE: RHT), the world’s leading provider of open source solutions, today announced financial results for its fiscal year 2011 second quarter ended August 31, 2010.

Total revenue for the quarter was $219.8 million, an increase of 20% from the year ago quarter. Subscription revenue for the quarter was $186.2 million, up 19% year-over-year.

and the stock goes zoom 48 % up for the year

http://www.google.com/finance?chdnp=1&chdd=1&chds=1&chdv=1&chvs=maximized&chdeh=0&chfdeh=0&chdet=1285505944359&chddm=98141&chls=IntervalBasedLine&cmpto=INDEXDJX:.DJI;NASDAQ:ORCL;NASDAQ:MSFT;NYSE:IBM&cmptdms=0;0;0;0&q=NYSE:RHT&ntsp=0

(Note to Google- please put the URL shortener on Google Finance as well)

The software is also reasonably priced starting from 80$ onwards.

https://www.redhat.com/apps/store/desktop/

Basic Subscription

Web support, 2 business day response, unlimited incidents
1 Year
$80
Multi-OS with Basic SubscriptionWeb support, 2 business day response, unlimited incidents
1 Year
$120
Workstation with Basic Subscription
Web support, 2 business day response, unlimited incidents
1 Year
$179
Workstation and Multi-OS with Basic Subscription
Web support, 2 business day response, unlimited incidents
1 Year
$219
Workstation with Standard Subscription
Business Hours phone support, web support, unlimited incidents
1 Year
$299
Workstation and Multi-OS with Standard Subscription
Business Hours phone support, web support, unlimited incidents
1 Year
$339
——————————————————————————————
That should be a good enough case for open source as a business model.




Interview Stephanie McReynolds Director Product Marketing, AsterData

Here is an interview with Stephanie McReynolds who works as as Director of Product Marketing with AsterData. I asked her a couple of questions about the new product releases from AsterData in analytics and MapReduce.

Ajay – How does the new Eclipse Plugin help people who are already working with huge datasets but are new to AsterData’s platform?

Stephanie- Aster Data Developer Express, our new SQL-MapReduce development plug-in for Eclipse, makes MapReduce applications easy to develop. With Aster Data Developer Express, developers can develop, test and deploy a complete SQL-MapReduce application in under an hour. This is a significant increase in productivity over the traditional analytic application development process for Big Data applications, which requires significant time coding applications in low-level code and testing applications on sample data.

Ajay – What are the various analytical functions that are introduced by you recently- list say the top 10.

Stephanie- At Aster Data, we have an intense focus on making the development process easier for SQL-MapReduce applications. Aster Developer Express is a part of this initiative, as is the release of pre-defined analytic functions. We recently launched both a suite of analytic modules and a partnership program dedicated to delivering pre-defined analytic functions for the Aster Data nCluster platform. Pre-defined analytic functions delivered by Aster Data’s engineering team are delivered as modules within the Aster Data Analytic Foundation offering and include analytics in the areas of pattern matching, clustering, statistics, and text analysis– just to name a few areas. Partners like Fuzzy Logix and Cobi Systems are extending this library by delivering industry-focused analytics like Monte Carlo Simulations for Financial Services and geospatial analytics for Public Sector– to give you a few examples.

Ajay – So okay I want to do a K Means Cluster on say a million rows (and say 200 columns) using the Aster method. How do I go about it using the new plug-in as well as your product.

Stephanie- The power of the Aster Data environment for analytic application development is in SQL-MapReduce. SQL is a powerful analytic query standard because it is a declarative language. MapReduce is a powerful programming framework because it can support high performance parallel processing of Big Data and extreme expressiveness, by supporting a wide variety of programming languages, including Java, C/C#/C++, .Net, Python, etc. Aster Data has taken the performance and expressiveness of MapReduce and combined it with the familiar declarativeness of SQL. This unique combination ensures that anyone who knows standard SQL can access advanced analytic functions programmed for Big Data analysis using MapReduce techniques.

kMeans is a good example of an analytic function that we pre-package for developers as part of the Aster Data Analytic Foundation. What does that mean? It means that the MapReduce portion of the development cycle has been completed for you. Each pre-packaged Aster Data function can be called using standard SQL, and executes the defined analytic in a fully parallelized manner in the Aster Data database using MapReduce techniques. The result? High performance analytics with the expressiveness of low-level languages accessed through declarative SQL.

Ajay – I see an an increasing focus on Analytics. Is this part of your product strategy and how do you see yourself competing with pure analytics vendors.

Stephanie – Aster Data is an infrastructure provider. Our core product is a massively parallel processing database called nCluster that performs at or beyond the capabilities of any other analytic database in the market today. We developed our analytics strategy as a response to demand from our customers who were looking beyond the price/performance wars being fought today and wanted support for richer analytics from their database provider. Aster Data analytics are delivered in nCluster to enable analytic applications that are not possible in more traditional database architectures.

Ajay – Name some recent case studies in Analytics of implementation of MR-SQL with Analytical functions

Stephanie – There are three new classes of applications that Aster Data Express and Aster Analytic Foundation support: iterative analytics, prediction and optimization, and ad hoc analysis.

Aster Data customers are uncovering critical business patterns in Big Data by performing hypothesis-driven, iterative analytics. They are exploring interactively massive volumes of data—terabytes to petabytes—in a top-down deductive manner. ComScore, an Aster Data customer that performs website experience analysis is a good example of an Aster Data customer performing this type of analysis.

Other Aster Data customers are building applications for prediction and optimization that discover trends, patterns, and outliers in data sets. Examples of these types of applications are propensity to churn in telecommunications, proactive product and service recommendations in retail, and pricing and retention strategies in financial services. Full Tilt Poker, who is using Aster Data for fraud prevention is a good example of a customer in this space.

The final class of application that I would like to highlight is ad hoc analysis. Examples of ad hoc analysis that can be performed includes social network analysis, advanced click stream analysis, graph analysis, cluster analysis and a wide variety of mathematical, trigonometry, and statistical functions. LinkedIn, whose analysts and data scientists have access to all of their customer data in Aster Data are a good example of a customer using the system in this manner.

While Aster Data customers are using nCluster in a number of other ways, these three new classes of applications are areas in which we are seeing particularly innovative application development.

Biography-

Stephanie McReynolds is Director of Product Marketing at Aster Data, where she is an evangelist for Aster Data’s massively parallel data-analytics server product. Stephanie has over a decade of experience in product management and marketing for business intelligence, data warehouse, and complex event processing products at companies such as Oracle, Peoplesoft, and Business Objects. She holds both a master’s and undergraduate degree from Stanford University.

Big Data and R: New Product Release by Revolution Analytics

Press Release by the Guys in Revolution Analytics- this time claiming to enable terabyte level analytics with R. Interesting stuff but techie details are awaited.

Revolution Analytics Brings

Big Data Analysis to R

The world’s most powerful statistics language can now tackle terabyte-class data sets using

Revolution R Enterpriseat a fraction of the cost of legacy analytics products


JSM 2010 – VANCOUVER (August 3, 2010) — Revolution Analytics today introduced ‘Big Data’ analysis to its Revolution R Enterprise software, taking the popular R statistics language to unprecedented new levels of capacity and performance for analyzing very large data sets. For the first time, R users will be able to process, visualize and model terabyte-class data sets in a fraction of the time of legacy products—without employing expensive or specialized hardware.

The new version of Revolution R Enterprise introduces an add-on package called RevoScaleR that provides a new framework for fast and efficient multi-core processing of large data sets. It includes:

  • The XDF file format, a new binary ‘Big Data’ file format with an interface to the R language that provides high-speed access to arbitrary rows, blocks and columns of data.
  • A collection of widely-used statistical algorithms optimized for Big Data, including high-performance implementations of Summary Statistics, Linear Regression, Binomial Logistic Regressionand Crosstabs—with more to be added in the near future.
  • Data Reading & Transformation tools that allow users to interactively explore and prepare large data sets for analysis.
  • Extensibility, expert R users can develop and extend their own statistical algorithms to take advantage of Revolution R Enterprise’s new speed and scalability capabilities.

“The R language’s inherent power and extensibility has driven its explosive adoption as the modern system for predictive analytics,” said Norman H. Nie, president and CEO of Revolution Analytics. “We believe that this new Big Data scalability will help R transition from an amazing research and prototyping tool to a production-ready platform for enterprise applications such as quantitative finance and risk management, social media, bioinformatics and telecommunications data analysis.”

Sage Bionetworks is the nonprofit force behind the open-source collaborative effort, Sage Commons, a place where data and disease models can be shared by scientists to better understand disease biology. David Henderson, Director of Scientific Computing at Sage, commented: “At Sage Bionetworks, we need to analyze genomic databases hundreds of gigabytes in size with R. We’re looking forward to using the high-speed data-analysis features of RevoScaleR to dramatically reduce the times it takes us to process these data sets.”

Take Hadoop and Other Big Data Sources to the Next Level

Revolution R Enterprise fits well within the modern ‘Big Data’ architecture by leveraging popular sources such as Hadoop, NoSQL or key value databases, relational databases and data warehouses. These products can be used to store, regularize and do basic manipulation on very large datasets—while Revolution R Enterprise now provides advanced analytics at unparalleled speed and scale: producing speed on speed.

“Together, Hadoop and R can store and analyze massive, complex data,” said Saptarshi Guha, developer of the popular RHIPE R package that integrates the Hadoop framework with R in an automatically distributed computing environment. “Employing the new capabilities of Revolution R Enterprise, we will be able to go even further and compute Big Data regressions and more.”

Platforms and Availability

The new RevoScaleR package will be delivered as part of Revolution R Enterprise 4.0, which will be available for 32-and 64-bit Microsoft Windows in the next 30 days. Support for Red Hat Enterprise Linux (RHEL 5) is planned for later this year.

On its website (http://www.revolutionanalytics.com/bigdata), Revolution Analytics has published performance and scalability benchmarks for Revolution R Enterprise analyzing a 13.2 gigabyte data set of commercial airline information containing more than 123 million rows, and 29 columns.

Additionally, the company will showcase its new Big Data solution in a free webinar on August 25 at 9:00 a.m. Pacific.

Additional Resources

•      Big Data Benchmark whitepaper

•      The Revolution Analytics Roadmap whitepaper

•      Revolutions Blog

•      Download free academic copy of Revolution R Enterprise

•      Visit Inside-R.org for the most comprehensive set of information on R

•      Spread the word: Add a “Download R!” badge on your website

•      Follow @RevolutionR on Twitter

About Revolution Analytics

Revolution Analytics (http://www.revolutionanalytics.com) is the leading commercial provider of software and support for the popular open source R statistics language. Its Revolution R products help make predictive analytics accessible to every type of user and budget. The company is headquartered in Palo Alto, Calif. and backed by North Bridge Venture Partners and Intel Capital.

Media Contact

Chantal Yang
Page One PR, for Revolution Analytics
Tel: +1 415-875-7494

Email:  revolution@pageonepr.com

R Oracle Data Mining

Here is a new package called R ODM and it is an interface to do Data Mining via Oracle Tables through R. You can read more here http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html and here http://cran.fhcrc.org/web/packages/RODM/RODM.pdf . Also there is a contest for creative use of R and ODM.

R Interface to Oracle Data Mining

The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies.

R-ODM is especially useful for:

  • Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application
  • Scripting of “production” data mining methodologies
  • Customizing graphics of ODM data mining results (examples: classificationregressionanomaly detection)

The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc.

R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment’s Comprehensive R Archive Network ( CRAN). For information about the R environment, R packages and CRAN, see www.r-project.org.

and

Present and win an Apple iPod Touch!
The BI, Warehousing and Analytics (BIWA) SIG is giving an Apple iPOD Touch to the best new presenter. Be part of the TechCast series and get a chance to win!

Consider highlighting a creative use of R and ODM.

BIWA invites all Oracle professionals (experts, end users, managers, DBAs, developers, data analysts, ISVs, partners, etc.) to submit abstracts for 45 minute technical webcasts to our Oracle BIWA (IOUG SIG) Community in our Wednesday TechCast series. Note that the contest is limited to new presenters to encourage fresh participation by the BIWA community.

Also an interview with Oracle Data Mining head, Charlie Berger https://decisionstats.wordpress.com/2009/09/02/oracle/

Best Internet Site of 2009

Here is the best internet site of 2009.
It basically shows how many jobs have been created per dollar spent.
Funded by the debt of American Treasuries………

Here is the best internet site of 2009.
It basically shows how many jobs have been created per dollar spent.
Funded by the debt of American Treasuries
sold to Chinese.

Remember the Chinese Opium Wars.
Well the Chinese are hooked to American Treasuries and they probably need a Warship with Admiral to open their markets and currency. Oui!

Well anyway the website is called http://Recovery.gov

The Great Game- How social media changes the Intelligence Industry

Since time immemorial, countries and corporations have used spies to displace existing equilibriums in balance of power or market share dynamics. An integral part of that was technology. From the pox infested rugs given to natives, to the plague rats, to the smuggling of the secret of silk and gunpowder from China to the West to the latest research in cloud seeding by China and Glaciars melting by India- technology espionage has been an integral part in keeping up with each other.

For the first time in history, technology has evolved to the point where tools for communicating securely , storing data has become cheap to the point of just having a small iPhone 3GS with applications for secure transmission. From an analytical purpose the need for analyzing signal from noise and the criticality in mapping chatter with events (like Major Hasan’s online activities)  has also created an opportunity for social media as well as an headache for the people involved. With Citizen Journalism, foreign relations office, and ambassadors with their bully pulpits have been brought down to defending news leaked by Twitter ( Iran) You Tube ( Thailand/Burma/Tibet) and Blogs ( Russia/Georgia). The rise of bot nets, dark clouds to create disruptions as well as hack into accounts for enhancing favourable noise and reducing unfavourable signals has only increased. Blogs have potential to influence customer behavior as they are seen more credible than public relations which is mostly public and rarely on relations.

Techniques like sentiment analysis , social network analysis, text mining and co relation of keywords to triggers remain active research points.

[tweetmeme=”decisionstats”]

The United States remains a leader as you can only think creatively out of a box if you are permitted to behave accordingly out of the box. The remaining countries are torn between a  mix of admiration , envy and plain old copy cat techniques. The rising importance of communities that act more tribal than hitherto loyal technology user lists is the reason almost all major corporates actively seek to cultivate social media communities. The market for blogs and twitter in China or Iran or Russia will have impacts on those government’s efforts to manage their growth as per their national strategic interests. Just like the title of an old and quaint novel- “The Brave New World” of social media and it’s convergence with increasing amounts of text data generated on customers, or citizens is evolving into creating new boundaries and space for itself.A fascinating Great Game in itself.

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