IBM and Revolution team to create new in-database R

From the Press Release at http://www.revolutionanalytics.com/news-events/news-room/2011/revolution-analytics-netezza-partnership.php

Under the terms of the agreement, the companies will work together to create a version of Revolution’s software that takes advantage of IBM Netezza’s i-class technology so that Revolution R Enterprise can run in-database in an optimal fashion.

About IBM

For information about IBM Netezza, please visit: http://www.netezza.com.
For Information on IBM Information Management, please visit: http://www.ibm.com/software/data/information-on-demand/
For information on IBM Business Analytics, please visit the online press kit: http://www.ibm.com/press/us/en/presskit/27163.wss
Follow IBM and Analytics on Twitter: http://twitter.com/ibmbizanalytics
Follow IBM analytics on Tumblr: http://smarterplanet.tumblr.com/tagged/new_intelligence
IBM YouTube Analytics Channel: http://www.youtube.com/user/ibmbusinessanalytics
For information on IBM Smarter Systems: http://www-03.ibm.com/systems/smarter/

About Revolution Analytics

Revolution Analytics is the leading commercial provider of software and services based on the open source R project for statistical computing.  Led by predictive analytics pioneer Norman Nie, the company brings high performance, productivity and enterprise readiness to R, the most powerful statistics language in the world. The company’s flagship Revolution R product is designed to meet the production needs of large organizations in industries such as finance, life sciences, retail, manufacturing and media.  Used by over 2 million analysts in academia and at cutting-edge companies such as Google, Bank of America and Acxiom, R has emerged as the standard of innovation in statistical analysis. Revolution Analytics is committed to fostering the continued growth of the R community through sponsorship of the Inside-R.org community site, funding worldwide R user groups and offers free licenses of Revolution R Enterprise to everyone in academia.


Netezza, an IBM Company, is the global leader in data warehouse, analytic and monitoring appliances that dramatically simplify high-performance analytics across an extended enterprise. IBM Netezza’s technology enables organizations to process enormous amounts of captured data at exceptional speed, providing a significant competitive and operational advantage in today’s data-intensive industries, including digital media, energy, financial services, government, health and life sciences, retail and telecommunications.

The IBM Netezza TwinFin® appliance is built specifically to analyze petabytes of detailed data significantly faster than existing data warehouse options, and at a much lower total cost of ownership. It stores, filters and processes terabytes of records within a single unit, analyzing only the relevant information for each query.

Using Revolution R Enterprise & Netezza Together

Revolution Analytics and IBM Netezza have announced a partnership to integrate Revolution R Enterprise and the IBM Netezza TwinFin  Data Warehouse Appliance. For the first time, customers seeking to run high performance and full-scale predictive analytics from within a data warehouse platform will be able to directly leverage the power of the open source R statistics language. The companies are working together to create a version of Revolution’s software that takes advantage of IBM Netezza’s i-class technology so that Revolution R Enterprise can run in-database in an optimal fashion.

This partnership integrates Revolution R Enterprise with IBM Netezza’s high performance data warehouse and advanced analytics platform to help organizations combat the challenges that arise as complexity and the scale of data grow.  By moving the analytics processing next to the data, this integration will minimize data movement – a significant bottleneck, especially when dealing with “Big Data”.  It will deliver high performance on large scale data, while leveraging the latest innovations in analytics.

With Revolution R Enterprise for IBM Netezza, advanced R computations are available for rapid analysis of hundreds of terabyte-class data volumes — and can deliver 10-100x performance improvements at a fraction of the cost compared to traditional analytics vendors.

Additional Resources


Zementis partners with R Analytics Vendor- Revo

Logo for R
Image via Wikipedia

Just got a  PR email from Michael Zeller,CEO , Zementis annoucing Zementis (ADAPA) and Revolution  Analytics just partnered up.

Is this something substantial or just time-sharing http://bi.cbronline.com/news/sas-ceo-says-cep-open-source-and-cloud-bi-have-limited-appeal or a Barney Partnership (http://www.dbms2.com/2008/05/08/database-blades-are-not-what-they-used-to-be/)

Summary- Thats cloud computing scoring of models on EC2 (Zementis) partnering with the actual modeling software in R (Revolution Analytics RevoDeployR)

See previous interviews with both Dr Zeller at https://decisionstats.com/2009/02/03/interview-michael-zeller-ceozementis/ ,https://decisionstats.com/2009/05/07/interview-ron-ramos-zementis/ and https://decisionstats.com/2009/10/05/interview-michael-zellerceo-zementis-on-pmml/)

and Revolution guys at https://decisionstats.com/2010/08/03/q-a-with-david-smith-revolution-analytics/

and https://decisionstats.com/2009/05/29/interview-david-smith-revolution-computing/

strategic partnership with Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language. With this partnership, predictive models developed on Revolution R Enterprise are now accessible for real-time scoring through the ADAPA Decisioning Engine by Zementis. 

ADAPA is an extremely fast and scalable predictive platform. Models deployed in ADAPA are automatically available for execution in real-time and batch-mode as Web Services. ADAPA allows Revolution R Enterprise to leverage the Predictive Model Markup Language (PMML) for better decision management. With PMML, models built in R can be used in a wide variety of real-world scenarios without requiring laborious or expensive proprietary processes to convert them into applications capable of running on an execution system.

partnership

“By partnering with Zementis, Revolution Analytics is building an end-to-end solution for moving enterprise-level predictive R models into the execution environment,” said Jeff Erhardt, Revolution Analytics Chief Operation Officer. “With Zementis, we are eliminating the need to take R applications apart and recode, retest and redeploy them in order to obtain desirable results.”

 

Got demo? 

Yes, we do! Revolution Analytics and Zementis have put together a demo which combines the building of models in R with automatic deployment and execution in ADAPA. It uses Revolution Analytics’ RevoDeployR, a new Web Services framework that allows for data analysts working in R to publish R scripts to a server-based installation of Revolution R Enterprise.

Action Items:

  1. Try our INTERACTIVE DEMO
  2. DOWNLOAD the white paper
  3. Try the ADAPA FREE TRIAL

RevoDeployR & ADAPA allow for real-time analysis and predictions from R to be effectively used by existing Excel spreadsheets, BI dashboards and Web-based applications, all in real-time.

RevoADAPAPredictive analytics with RevoDeployR from Revolution Analytics and ADAPA from Zementis put model building and real-time scoring into a league of their own. Seriously!

Interview Anne Milley JMP

Here is an interview with Anne Milley, a notable thought leader in the world of analytics. Anne is now Senior Director, Analytical Strategy in Product Marketing for JMP , the leading data visualization software from the SAS Institute.

Ajay-What do you think are the top 5 unique selling points of JMP compared to other statistical software in its category?

Anne-

JMP combines incredible analytic depth and breadth with interactive data visualization, creating a unique environment optimized for discovery and data-driven innovation.

With an extensible framework using JSL (JMP Scripting Language), and integration with SAS, R, and Excel, JMP becomes your analytic hub.

JMP is accessible to all kinds of users. A novice analyst can dig into an interactive report delivered by a custom JMP application. An engineer looking at his own data can use built-in JMP capabilities to discover patterns, and a developer can write code to extend JMP for herself or others.

State-of-the-art DOE capabilities make it easy for anyone to design and analyze efficient experiments to determine which adjustments will yield the greatest gains in quality or process improvement – before costly changes are made.

Not to mention, JMP products are exceptionally well designed and easy to use. See for yourself and check out the free trial at www.jmp.com.

Download a free 30-day trial of JMP.

Ajay- What are the challenges and opportunities of expanding JMP’s market share? Do you see JMP expanding its conferences globally to engage global audiences?

Anne-

We realized solid global growth in 2010. The release of JMP Pro and JMP Clinical last year along with continuing enhancements to the rest of the JMP family of products (JMP and JMP Genomics) should position us well for another good year.

With the growing interest in analytics as a means to sustained value creation, we have the opportunity to help people along their analytic journey – to get started, take the next step, or adopt new paradigms speeding their time to value. The challenge is doing that as fast as we would like.

We are hiring internationally to offer even more events, training and academic programs globally.

Ajay- What are the current and proposed educational and global academic initiatives of JMP? How can we see more JMP in universities across the world (say India- China etc)?

Anne-

We view colleges and universities both as critical incubators of future JMP users and as places where attitudes about data analysis and statistics are formed. We believe that a positive experience in learning statistics makes a person more likely to eventually want and need a product like JMP.

For most students – and particularly for those in applied disciplines of business, engineering and the sciences – the ability to make a statistics course relevant to their primary area of study fosters a positive experience. Fortunately, there is a trend in statistical education toward a more applied, data-driven approach, and JMP provides a very natural environment for both students and researchers.

Its user-friendly navigation, emphasis on data visualization and easy access to the analytics behind the graphics make JMP a compelling alternative to some of our more traditional competitors.

We’ve seen strong growth in the education markets in the last few years, and JMP is now used in nearly half of the top 200 universities in the US.

Internationally, we are at an earlier stage of market development, but we are currently working with both JMP and SAS country offices and their local academic programs to promote JMP. For example, we are working with members of the JMP China office and faculty at several universities in China to support the use of JMP in the development of a master’s curriculum in Applied Statistics there, touched on in this AMSTAT News article.

Ajay- What future trends do you see for 2011 in this market (say top 5)?

Anne-

Growing complexity of data (text, image, audio…) drives the need for more and better visualization and analysis capabilities to make sense of it all.

More “chief analytics officers” are making better use of analytic talent – people are the most important ingredient for success!

JMP has been on the vanguard of 64-bit development, and users are now catching up with us as 64-bit machines become more common.

Users should demand easy-to-use, exploratory and predictive modeling tools as well as robust tools to experiment and learn to help them make the best decisions on an ongoing basis.

All these factors and more fuel the need for the integration of flexible, extensible tools with popular analytic platforms.

Ajay-You enjoy organic gardening as a hobby. How do you think hobbies and unwind time help people be better professionals?

Anne-

I am lucky to work with so many people who view their work as a hobby. They have other interests too, though, some of which are work-related (statistics is relevant everywhere!). Organic gardening helps me put things in perspective and be present in the moment. More than work defines who you are. You can be passionate about your work as well as passionate about other things. I think it’s important to spend some leisure time in ways that bring you joy and contribute to your overall wellbeing and outlook.

Btw, nice interviews over the past several months—I hadn’t kept up, but will check it out more often!

Biography–  Source- http://www.sas.com/knowledge-exchange/business-analytics/biographies.html

  • Anne Milley

    Anne Milley

    Anne Milley is Senior Director of Analytics Strategy at JMP Product Marketing at SAS. Her ties to SAS began with bank failure prediction at Federal Home Loan Bank Dallas and continued at 7-Eleven Inc. She has authored papers and served on committees for F2006, KDD, SIAM, A2010 and several years of SAS’ annual data mining conference. Milley is a contributing faculty member for the International Institute of Analytics. anne.milley@jmp.com

R Graphs Resources

Relevant GUI-

GrapheR and Deducer

https://rforanalytics.wordpress.com/graphical-user-interfaces-for-r/

Websites-


Graphics by Examples

. UCLA: Academic Technology Services,  Statistical Consulting Group. from https://www.ats.ucla.edu/stat/R/gbe/default.htm (accessed Feb 10, 2011)

https://www.ats.ucla.edu/stat/R/gbe/default.htm

Quick-R

http://www.statmethods.net/graphs/

Graph Gallery

http://addictedtor.free.fr/graphiques/allgraph.php

Frank McCown

https://www.harding.edu/fmccown/r/

Detailed Tutorial

https://math.illinoisstate.edu/dhkim/rstuff/rtutor.html

Advanced Data Visualization

Hadley Wickham

Courses- http://had.co.nz/stat645/

and Package-  http://had.co.nz/ggplot2/

example-

http://had.co.nz/ggplot2/geom_density.html

Using R from other Software

Bridge to R for WPS

http://www.minequest.com/Bridge2R.html

SAS/IML Interface to R

http://www.sas.com/technologies/analytics/statistics/iml/index.html

Official Screenshot-

RapidMiner Extension to R

https://rapid-i.com/content/view/202/206/lang,en/#r

(UN)Official Screenshot-

IBM SPSS plugin for R

https://www.spss.com/software/statistics/developer/

and

https://www.spss.com/devcentral/index.cfm?pg=rresources

Tutorial-

https://sites.google.com/site/r4statistics/running-r-from-spss

http://rwiki.sciviews.org/doku.php?id=tips:callingr:spss

(UN)official Screenshot

Knime

http://www.knime.org/downloads/extensions

Official Screenshot-

Oracle Data Miner

http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html

Official Screenshot-

JMP

http://jmp.com/software/jmp9/keyfeatures.shtml

and

http://www.jmp.com/applications/analytical_apps/

Tutorial

http://blogs.sas.com/jmp/index.php?/archives/298-JMP-Into-R!.html

Screenshot-

PSPP – SPSS 's Open Source Counterpart

A Bold GNU Head
Image via Wikipedia

New Website for Windows Installers for PSPP– try at your own time if you are dedicated to either SPSS or free statistical computing.

http://pspp.awardspace.com/

This page is intended to give a stable root for downloading the PSPP-for-Windows setup from free mirrors.

Highlights of the current PSPP-for-Windows setup
PSPP info:

Current version: Master version = 0.7.6
Release date: See filenames
Information about PSPP: http://www.gnu.org/software/pspp
PSPP Manual: PDF or HTML
(current version will be installed on your PC by the installer package)
Package info:

Windows version: Windows XP and newer
Package Size: 15 Mb
Size on disk: 34 Mb
Technical: MinGW based
Cross-compiled on openSUSE 11.3

Downloads:
There are issues with the latest build. Some users report crashes on their systems on other systems it works fine.

Version Installer for multi-user installation.
Administrator privileges required.
Recommended version.
Installer for single-user installation.
No administrator privileges required
0.7.6-g38ba1e-blp-build20101116
0.7.5-g805e7e-blp-build20100908
0.7.5-g7803d3-blp-build20100820
0.7.5-g333ac4-blp-build20100727
PSPP-Master-2010-11-16
PSPP-Master-2010-09-08
PSPP-Master-2010-08-20
PSPP-Master-2010-07-27
PSPP-Master-single-user-2010-11-16
PSPP-Master-single-user-2010-09-08
PSPP-Master-single-user-2010-08-20
PSPP-Master-single-user-2010-07-27

 

Sources can be found here.

Also see http://en.wikipedia.org/wiki/PSPP

At the user’s choice, statistical output and graphics are done in ASCIIPDFPostScript or HTML formats. A limited range of statistical graphs can be produced, such as histogramspie-charts and np-charts.

PSPP can import GnumericOpenDocument and Excel spreadsheetsPostgres databasescomma-separated values– and ASCII-files. It can export files in the SPSS ‘portable’ and ‘system’ file formats and to ASCII files. Some of the libraries used by PSPP can be accessed programmatically; PSPP-Perl provides an interface to the libraries used by PSPP.

and

http://www.gnu.org/software/pspp/

A brief list of some of the features of PSPP follows:

  • Supports over 1 billion cases.
  • Supports over 1 billion variables.
  • Syntax and data files are compatible with SPSS.
  • Choice of terminal or graphical user interface.
  • Choice of text, postscript or html output formats.
  • Inter-operates with GnumericOpenOffice.Org and other free software.
  • Easy data import from spreadsheets, text files and database sources.
  • Fast statistical procedures, even on very large data sets.
  • No license fees.
  • No expiration period.
  • No unethical “end user license agreements”.
  • Fully indexed user manual.
  • Free Software; licensed under GPLv3 or later.
  • Cross platform; Runs on many different computers and many different operating systems.

 

PSPP – SPSS ‘s Open Source Counterpart

A Bold GNU Head
Image via Wikipedia

New Website for Windows Installers for PSPP– try at your own time if you are dedicated to either SPSS or free statistical computing.

http://pspp.awardspace.com/

This page is intended to give a stable root for downloading the PSPP-for-Windows setup from free mirrors.

Highlights of the current PSPP-for-Windows setup
PSPP info:

Current version: Master version = 0.7.6
Release date: See filenames
Information about PSPP: http://www.gnu.org/software/pspp
PSPP Manual: PDF or HTML
(current version will be installed on your PC by the installer package)
Package info:

Windows version: Windows XP and newer
Package Size: 15 Mb
Size on disk: 34 Mb
Technical: MinGW based
Cross-compiled on openSUSE 11.3

Downloads:
There are issues with the latest build. Some users report crashes on their systems on other systems it works fine.

Version Installer for multi-user installation.
Administrator privileges required.
Recommended version.
Installer for single-user installation.
No administrator privileges required
0.7.6-g38ba1e-blp-build20101116
0.7.5-g805e7e-blp-build20100908
0.7.5-g7803d3-blp-build20100820
0.7.5-g333ac4-blp-build20100727
PSPP-Master-2010-11-16
PSPP-Master-2010-09-08
PSPP-Master-2010-08-20
PSPP-Master-2010-07-27
PSPP-Master-single-user-2010-11-16
PSPP-Master-single-user-2010-09-08
PSPP-Master-single-user-2010-08-20
PSPP-Master-single-user-2010-07-27

 

Sources can be found here.

Also see http://en.wikipedia.org/wiki/PSPP

At the user’s choice, statistical output and graphics are done in ASCIIPDFPostScript or HTML formats. A limited range of statistical graphs can be produced, such as histogramspie-charts and np-charts.

PSPP can import GnumericOpenDocument and Excel spreadsheetsPostgres databasescomma-separated values– and ASCII-files. It can export files in the SPSS ‘portable’ and ‘system’ file formats and to ASCII files. Some of the libraries used by PSPP can be accessed programmatically; PSPP-Perl provides an interface to the libraries used by PSPP.

and

http://www.gnu.org/software/pspp/

A brief list of some of the features of PSPP follows:

  • Supports over 1 billion cases.
  • Supports over 1 billion variables.
  • Syntax and data files are compatible with SPSS.
  • Choice of terminal or graphical user interface.
  • Choice of text, postscript or html output formats.
  • Inter-operates with GnumericOpenOffice.Org and other free software.
  • Easy data import from spreadsheets, text files and database sources.
  • Fast statistical procedures, even on very large data sets.
  • No license fees.
  • No expiration period.
  • No unethical “end user license agreements”.
  • Fully indexed user manual.
  • Free Software; licensed under GPLv3 or later.
  • Cross platform; Runs on many different computers and many different operating systems.