Tag: source

Amazon Ec2 goes Red Hat

message from Amazing Amazon’s cloud team- this will also help for #rstats users given that revolution Analytics full versions on RHEL.

—————————————————-

on-demand instances of Amazon EC2 running Red Hat Enterprise Linux (RHEL) for as little as $0.145 per instance hour. The offering combines the cost-effectiveness, scalability and flexibility of running in Amazon EC2 with the proven reliability of Red Hat Enterprise Linux.

Highlights of the offering include:

  • Support is included through subscription to AWS Premium Support with back-line support by Red Hat
  • Ongoing maintenance, including security patches and bug fixes, via update repositories available in all Amazon EC2 regions
  • Amazon EC2 running RHEL currently supports RHEL 5.5, RHEL 5.6, RHEL 6.0 and RHEL 6.1 in both 32 bit and 64 bit formats, and is available in all Regions.
  • Customers who already own Red Hat licenses will continue to be able to use those licenses at no additional charge.
  • Like all services offered by AWS, Amazon EC2 running Red Hat Enterprise Linux offers a low-cost, pay-as-you-go model with no long-term commitments and no minimum fees.

For more information, please visit the Amazon EC2 Red Hat Enterprise Linux page.

which is

Amazon EC2 Running Red Hat Enterprise Linux

Amazon EC2 running Red Hat Enterprise Linux provides a dependable platform to deploy a broad range of applications. By running RHEL on EC2, you can leverage the cost effectiveness, scalability and flexibility of Amazon EC2, the proven reliability of Red Hat Enterprise Linux, and AWS premium support with back-line support from Red Hat.. Red Hat Enterprise Linux on EC2 is available in versions 5.5, 5.6, 6.0, and 6.1, both in 32-bit and 64-bit architectures.

Amazon EC2 running Red Hat Enterprise Linux provides seamless integration with existing Amazon EC2 features including Amazon Elastic Block Store (EBS), Amazon CloudWatch, Elastic-Load Balancing, and Elastic IPs. Red Hat Enterprise Linux instances are available in multiple Availability Zones in all Regions.

Sign Up

Pricing

Pay only for what you use with no long-term commitments and no minimum fee.

On-Demand Instances

On-Demand Instances let you pay for compute capacity by the hour with no long-term commitments.

Region:US – N. VirginiaUS – N. CaliforniaEU – IrelandAPAC – SingaporeAPAC – Tokyo
Standard Instances Red Hat Enterprise Linux
Small (Default) $0.145 per hour
Large $0.40 per hour
Extra Large $0.74 per hour
Micro Instances Red Hat Enterprise Linux
Micro $0.08 per hour
High-Memory Instances Red Hat Enterprise Linux
Extra Large $0.56 per hour
Double Extra Large $1.06 per hour
Quadruple Extra Large $2.10 per hour
High-CPU Instances Red Hat Enterprise Linux
Medium $0.23 per hour
Extra Large $0.78 per hour
Cluster Compute Instances Red Hat Enterprise Linux
Quadruple Extra Large $1.70 per hour
Cluster GPU Instances Red Hat Enterprise Linux
Quadruple Extra Large $2.20 per hour

Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed are billed as full hours.

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and

Available Instance Types

Standard Instances

Instances of this family are well suited for most applications.

Small Instance – default*

1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small

Large Instance

7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large

Extra Large Instance

15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge

Micro Instances

Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.

Micro Instance

613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro

High-Memory Instances

Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.

High-Memory Extra Large Instance

17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge

High-Memory Double Extra Large Instance

34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge

High-Memory Quadruple Extra Large Instance

68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge

High-CPU Instances

Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.

High-CPU Medium Instance

1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium

High-CPU Extra Large Instance

7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge

Cluster Compute Instances

Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.

Cluster Compute Quadruple Extra Large Instance

23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge

Cluster GPU Instances

Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.

Cluster GPU Quadruple Extra Large Instance

22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge

 

Getting Started

To get started using Red Hat Enterprise Linux on Amazon EC2, perform the following steps:

  • Open and log into the AWS Management Console
  • Click on Launch Instance from the EC2 Dashboard
  • Select the Red Hat Enterprise Linux AMI from the QuickStart tab
  • Specify additional details of your instance and click Launch
  • Additional details can be found on each AMI’s Catalog Entry page

The AWS Management Console is an easy tool to start and manage your instances. If you are looking for more details on launching an instance, a quick video tutorial on how to use Amazon EC2 with the AWS Management Console can be found here .
A full list of Red Hat Enterprise Linux AMIs can be found in the AWS AMI Catalog.

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Support

All customers running Red Hat Enterprise Linux on EC2 will receive access to repository updates from Red Hat. Moreover, AWS Premium support customers can contact AWS to get access to a support structure from both Amazon and Red Hat.

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Resources

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About Red Hat

Red Hat, the world’s leading open source solutions provider, is headquartered in Raleigh, NC with over 50 satellite offices spanning the globe. Red Hat provides high-quality, low-cost technology with its operating system platform, Red Hat Enterprise Linux, together with applications, management and Services Oriented Architecture (SOA) solutions, including the JBoss Enterprise Middleware Suite. Red Hat also offers support, training and consulting services to its customers worldwide.

 

also from Revolution Analytics- in case you want to #rstats in the cloud and thus kill all that talk of RAM dependency, slow R than other softwares (just increase the RAM above in the instances to keep it simple)

,or Revolution not being open enough

http://www.revolutionanalytics.com/downloads/gpl-sources.php

GPL SOURCES

Revolution Analytics uses an Open-Core Licensing model. We provide open- source R bundled with proprietary modules from Revolution Analytics that provide additional functionality for our users. Open-source R is distributed under the GNU Public License (version 2), and we make our software available under a commercial license.

Revolution Analytics respects the importance of open source licenses and has contributed code to the open source R project and will continue to do so. We have carefully reviewed our compliance with GPLv2 and have worked with Mark Radcliffe of DLA Piper, the outside General Legal Counsel of the Open Source Initiative, to ensure that we fully comply with the obligations of the GPLv2.

For our Revolution R distribution, we may make some minor modifications to the R sources (the ChangeLog file lists all changes made). You can download these modified sources of open-source R under the terms of the GPLv2, using either the links below or those in the email sent to you when you download a specific version of Revolution R.

Download GPL Sources

Product Version Platform Modified R Sources
Revolution R Community 3.2 Windows R 2.10.1
Revolution R Community 3.2 MacOS R 2.10.1
Revolution R Enterprise 3.1.1 RHEL R 2.9.2
Revolution R Enterprise 4.0 Windows R 2.11.1
Revolution R Enterprise 4.0.1 RHEL R 2.11.1
Revolution R Enterprise 4.1.0 Windows R 2.11.1
Revolution R Enterprise 4.2 Windows R 2.11.1
Revolution R Enterprise 4.2 RHEL R 2.11.1
Revolution R Enterprise 4.3 Windows & RHEL R 2.12.2

 

 

 

Calling #Rstats lovers and bloggers – to work together on “The R Programming wikibook”

so you think u like R, huh. Well it is time to pay it forward.

Message from a dear R blogger, Tal G from Tel Aviv (creator of R-bloggers.com and SAS-X.com)

———————————————————————————————————-
Calling R lovers and bloggers – to work together on “The R Programming wikibook”
Posted: 20 Jun 2011 07:05 AM PDT

This post is a call for both R community members and R-bloggers, to come and help make The R Programming wikibook be amazing:

Dear R community member – please consider giving a visit to The R Programming wikibook. If you wish to contribute your knowledge and editing skills to the project, then you could learn how to write in wiki-markup here, and how to edit a wikibook here (you can even use R syntax highlighting in the wikibook). You could take information into the site from the (soon to be) growing list of available R resources for harvesting.

Dear R blogger, you can help The R Programming wikibook by doing the following:

Write to your readers about the project and invite them to join.
Add your blog’s R content as an available resource for other editors to use for the wikibook. Here is how to do that:
First, make a clear indication on your blog that your content is licensed under cc-by-sa copyrights (*see what it means at the end of the post). You can do this by adding it to the footer of your blog, or by writing a post that clearly states that this is the case (what a great opportunity to write to your readers about the project…).
Next, go and add a link, to where all of your R content is located on your site, to the resource page (also with a link to the license post, if you wrote one). For example, since I write about other things besides R, I would give a link to my R category page, and will also give a link to this post. If you do not know how to add it to the wiki, just e-mail me about it (tal.galili@gmail.com).
If you are an R blogger, besides living up to the spirit of the R community, you will benefit from joining this project in that every time someone will use your content on the wikibook, they will add your post as a resource. In the long run, this is likely to help visitors of the site get to know about you and strengthen your site’s SEO ranking. Which reminds me, if you write about this, I always appreciate a link back to my blog

* Having a cc-by-sa copyrights means that you will agree that anyone may copy, distribute, display, and make derivative works based on your content, only if they give the author (you) the credits in the manner specified by you. And also that the user may distribute derivative works only under a license identical to the license that governs the original work.

———-

Three more points:

1) This post is a result of being contacted by Paul (a.k.a: PAC2), asking if I could help promote “The R Programming wikibook” among R-bloggers and their readers. Paul has made many contributions to the book so far. So thank you Paul for both reaching out and helping all of us with your work on this free open source project.

2) I should also mention that the R wiki exists and is open for contribution. And naturally, every thing that will help the R wikibook will help the R wiki as well.

3) Copyright notice: I hereby release all of the writing material content that is categoriesed in the R category page, under the cc-by-sa copyrights (date: 20.06.2011). Now it’s your turn!

———-

List of R bloggers who have joined: (This list will get updated as this “group writing” project will progress)

R-statistics blog (that’s Tal…)
Decisionstats.com (That’s me)
……………………………………………………………………………….
3) Copyright notice: I hereby release all of the writing material content of this website, under the cc-by-sa copyrights (date: 21.06.2011). Now it’s your turn!

https://decisionstats.com/privacy-3/

Content Licensing-
This website has all content licensed under
http://creativecommons.org/licenses/by-sa/3.0/
You are free:
to Share — to copy, distribute and transmit the work
to Remix — to adapt the work

RStudio 3- Making R as simple as possible but no simpler

From the nice shiny blog at http://blog.rstudio.org/, a shiny new upgraded software (and I used the Cobalt theme)–this is nice!

awesome coding!!!

 

http://www.rstudio.org/download/

Download RStudio v0.94

Diagram desktop

If you run R on your desktop:

Download RStudio Desktop

OR

Diagram server

If you run R on a Linux server and want to enable users to remotely access RStudio using a web browser:

Download RStudio Server

 

RStudio v0.94 — Release Notes

June 15th, 2011

 

New Features and Enhancements

Source Editor and Console

  • Run code:
    • Run all lines in source file
    • Run to current line
    • Run from current line
    • Redefine current function
    • Re-run previous region
    • Code is now run line-by-line in the console
  • Brace, paren, and quote matching
  • Improved cursor placement after newlines
  • Support for regex find and replace
  • Optional syntax highlighting for console input
  • Press F1 for help on current selection
  • Function navigation / jump to function
  • Column and line number display
  • Manually set/switch document type
  • New themes: Solarized and Solarized Dark

Plots

  • Improved image export:
    • Formats: PNG, JPEG, TIFF, SVG, BMP, Metafile, and Postscript
    • Dynamic resize with preview
    • Option to maintain aspect ratio when resizing
    • Copy to clipboard as bitmap or metafile
  • Improved PDF export:
    • Specify custom sizes
    • Preview before exporting
  • Remove individual plots from history
  • Resizable plot zoom window

History

  • History tab synced to loaded .Rhistory file
  • New commands:
    • Load and save history
    • Remove individual items from history
    • Clear all history
  • New options:
    • Load history from working directory or global history file
    • Save history always or only when saving .RData
    • Remove duplicate entries in history
  • Shortcut keys for inserting into console or source

Packages

  • Check for package updates
  • Filter displayed packages
  • Install multiple packages
  • Remove packages
  • New options:
    • Install from repository or local archive file
    • Target library
    • Install dependencies

Miscellaneous

  • Find text within help topic
  • Sort file listing by name, type, size, or modified
  • Set working directory based on source file, files pane, or browsed for directory.
  • Console titlebar button to view current working directory in files pane
  • Source file menu command
  • Replace space and dash with dot (.) in import dataset generated variable names
  • Add decimal separator preference for import dataset
  • Added .tar.gz (Linux) and .zip (Windows) distributions for non-admin installs
  • Read /etc/paths.d on OS X to ensure RStudio has the same path as terminal sessions do
  • Added manifest to rsession.exe to prevent unwanted program files and registry virtualization

Server

  • Break PAM auth into its own binary for improved compatibility with 3rd party PAM authorization modules.
  • Ensure that AppArmor profile is enforced even after reboot
  • Ability to add custom LD library path for all sessions
  • Improved R discovery:
    • Use which R then fallback to scanning for R script
    • Run R discovery unconfined then switch into restricted profile
  • Default to uncompressed save.image output if the administrator or user hasn’t specified their own options (improved suspend/resume performance)
  • Ensure all running sessions are automatically updated during server version upgrade
  • Added verify-installation command to rstudio-server utility for easily capturing configuration and startup related errors

 

Bug Fixes

Source Editor

  • Undo to unedited state clears now dirty bit
  • Extract function now captures free variables used on lhs
  • Selected variable highlight now visible in all themes
  • Syncing to source file updates made outside of RStudio now happens immediately at startup and does not cause a scroll to the bottom of the document.
  • Fixed various issues related to copying and pasting into word processors
  • Fixed incorrect syntax highlighting issues in .Rd files
  • Make sure font size for printed source files matches current editor setting
  • Eliminate conflict with Ctrl+F shortcut key on OS X
  • Zoomed Google Chrome browser no longer causes cursor position to be off
  • Don’t prevent opening of unknown file types in the editor

Console

  • Fixed sporadic missing underscores (and other bottom clipping of text) in console
  • Make sure console history is never displayed offscreen
  • Page Up and Page Down now work properly in the console
  • Substantially improved console performance for both rapid output and large quantities of output

Miscellaneous

  • Install successfully on Windows with special characters in home directory name
  • make install more tolerant of configurations where it can’t write into /usr/share
  • Eliminate spurious stderr output in forked children of multicore package
  • Ensure that file modified times always update in the files pane after a save
  • Always default to installing packages into first writeable path of .libPaths()
  • Ensure that LaTeX log files are always preserved after compilePdf
  • Fix conflicts with zap function from epicalc package
  • Eliminate shortcut key conflicts with Ubuntu desktop workspace switching shortcuts
  • Always prompt when attempting to save files of the same name
  • Maximized main window now properly restored when reopening RStudio
  • PAM authorization works correctly even if account has password expiration warning
  • Correct display of manipulate panel when Plots pane is on the left

 

Previous Release Notes

 

Why open source companies dont dance?

I have been pondering on this seemingly logical paradox for some time now-

1) Why are open source solutions considered technically better but not customer friendly.

2) Why do startups and app creators in social media or mobile get much more press coverage than

profitable startups in enterprise software.

3) How does tech journalism differ in covering open source projects in enterprise versus retail software.

4) What are the hidden rules of the game of enterprise software.

Some observations-

1) Open source companies often focus much more on technical community management and crowd sourcing code. Traditional software companies focus much more on managing the marketing community of customers and influencers. Accordingly the balance of power is skewed in favor of techies and R and D in open source companies, and in favor of marketing and analyst relations in traditional software companies.

Traditional companies also spend much more on hiring top notch press release/public relationship agencies, while open source companies are both financially and sometimes ideologically opposed to older methods of marketing software. The reverse of this is you are much more likely to see Videos and Tutorials by an open source company than a traditional company. You can compare the websites of Cloudera, DataStax, Hadapt ,Appistry and Mapr and contrast that with Teradata or Oracle (which has a much bigger and much more different marketing strategy.

Social media for marketing is also more efficiently utilized by smaller companies (open source) while bigger companies continue to pay influential analysts for expensive white papers that help present the brand.

Lack of budgets is a major factor that limits access to influential marketing for open source companies particularly in enterprise software.

2 and 3) Retail software is priced at 2-100$ and sells by volume. Accordingly technology coverage of these software is based on volume.

Enterprise software is much more expensively priced and has much more discreet volume or sales points. Accordingly the technology coverage of enterprise software is more discreet, in terms of a white paper coming every quarter, a webinar every month and a press release every week. Retail software is covered non stop , but these journalists typically do not charge for “briefings”.

Journalists covering retail software generally earn money by ads or hosting conferences. So they have an interest in covering new stuff or interesting disruptive stuff. Journalists or analysts covering enterprise software generally earn money by white papers, webinars, attending than hosting conferences, writing books. They thus have a much stronger economic incentive to cover existing landscape and technologies than smaller startups.

4) What are the hidden rules of the game of enterprise software.

  • It is mostly a white man’s world. this can be proved by statistical demographic analysis
  • There is incestuous intermingling between influencers, marketers, and PR people. This can be proved by simple social network analysis of who talks to who and how much. A simple time series between sponsorship and analysts coverage also will prove this (I am working on quantifying this ).
  • There are much larger switching costs to enterprise software than retail software. This leads to legacy shoddy software getting much chances than would have been allowed in an efficient marketplace.
  • Enterprise software is a less efficient marketplace than retail software in all definitions of the term “efficient markets”
  • Cloud computing, and SaaS and Open source threatens to disrupt the jobs and careers of a large number of people. In the long term, they will create many more jobs, but in the short term, people used to comfortable living of enterprise software (making,selling,or writing) will actively and passively resist these changes to the  paradigms in the current software status quo.
  • Open source companies dont dance and dont play ball. They prefer to hire 4 more college grads than commission 2 more white papers.

and the following with slight changes from a comment I made on a fellow blog-

  • While the paradigm on how to create new software has evolved from primarily silo-driven R and D departments to a broader collaborative effort, the biggest drawback is software marketing has not evolved.
  • If you want your own version of the open source community editions to be more popular, some standardization is necessary for the corporate decision makers, and we need better marketing paradigms.
  • While code creation is crowdsourced, solution implementation cannot be crowdsourced. Customers want solutions to a problem not code.
  • Just as open source as a production and licensing paradigm threatens to disrupt enterprise software, it will lead to newer ways to marketing software given the hostility of existing status quo.

 

 

RapidMiner launches extensions marketplace

For some time now, I had been hoping for a place where new package or algorithm developers get at least a fraction of the money that iPad or iPhone application developers get. Rapid Miner has taken the lead in establishing a marketplace for extensions. Is there going to be paid extensions as well- I hope so!!

This probably makes it the first “app” marketplace in open source and the second app marketplace in analytics after salesforce.com

It is hard work to think of new algols, and some of them can really be usefull.

Can we hope for #rstats marketplace where people downloading say ggplot3.0 atleast get a prompt to donate 99 cents per download to Hadley Wickham’s Amazon wishlist. http://www.amazon.com/gp/registry/1Y65N3VFA613B

Do you think it is okay to pay 99 cents per iTunes song, but not pay a cent for open source software.

I dont know- but I am just a capitalist born in a country that was socialist for the first 13 years of my life. Congratulations once again to Rapid Miner for innovating and leading the way.

http://rapid-i.com/component/option,com_myblog/show,Rapid-I-Marketplace-Launched.html/Itemid,172

RapidMiner, Marketplace, Extensions 30 May 2011
Rapid-I Marketplace Launched by Simon Fischer

Over the years, many of you have been developing new RapidMiner Extensions dedicated to a broad set of topics. Whereas these extensions are easy to install in RapidMiner – just download and place them in the plugins folder – the hard part is to find them in the vastness that is the Internet. Extensions made by ourselves at Rapid-I, on the other hand,  are distributed by the update server making them searchable and installable directly inside RapidMiner.

We thought that this was a bit unfair, so we decieded to open up the update server to the public, and not only this, we even gave it a new look and name. The Rapid-I Marketplace is available in beta mode at http://rapidupdate.de:8180/ . You can use the Web interface to browse, comment, and rate the extensions, and you can use the update functionality in RapidMiner by going to the preferences and entering http://rapidupdate.de:8180/UpdateServer/ as the update server URL. (Once the beta test is complete, we will change the port back to 80 so we won’t have any firewall problems.)

As an Extension developer, just register with the Marketplace and drop me an email (fischer at rapid-i dot com) so I can give you permissions to upload your own extension. Upload is simple provided you use the standard RapidMiner Extension build process and will boost visibility of your extension.

Looking forward to see many new extensions there soon!

Disclaimer- Decisionstats is a partner of Rapid Miner. I have been liking the software for a long long time, and recently agreed to partner with them just like I did with KXEN some years back, and with Predictive AnalyticsConference, and Aster Data until last year.

I still think Rapid Miner is a very very good software,and a globally created software after SAP.

Here is the actual marketplace

http://rapidupdate.de:8180/UpdateServer/faces/index.xhtml

Welcome to the Rapid-I Marketplace Public Beta Test

The Rapid-I Marketplace will soon replace the RapidMiner update server. Using this marketplace, you can share your RapidMiner extensions and make them available for download by the community of RapidMiner users. Currently, we are beta testing this server. If you want to use this server in RapidMiner, you must go to the preferences and enter http://rapidupdate.de:8180/UpdateServer for the update url. After the beta test, we will change the port back to 80, which is currently occupied by the old update server. You can test the marketplace as a user (downloading extensions) and as an Extension developer. If you want to publish your extension here, please let us know via the contact form.

Hot Downloads
«« « 1 2 3 » »»
[Icon]The Image Processing Extension provides operators for handling image data. You can extract attributes describing colour and texture in the image, you can make several transformation of a image data which allows you to perform segmentation and detection of suspicious areas in image data.The extension provides many of image transformation and extraction operators ranging from Wavelet Decomposition, Hough Circle to Block Difference of Inverse probabilities.
[Icon]RapidMiner is unquestionably the world-leading open-source system for data mining. It is available as a stand-alone application for data analysis and as a data mining engine for the integration into own products. Thousands of applications of RapidMiner in more than 40 countries give their users a competitive edge.

  • Data Integration, Analytical ETL, Data Analysis, and Reporting in one single suite
  • Powerful but intuitive graphical user interface for the design of analysis processes
  • Repositories for process, data and meta data handling
  • Only solution with meta data transformation: forget trial and error and inspect results already during design time
  • Only solution which supports on-the-fly error recognition and quick fixes
  • Complete and flexible: Hundreds of data loading, data transformation, data modeling, and data visualization methods
[Icon]All modeling methods and attribute evaluation methods from the Weka machine learning library are available within RapidMiner. After installing this extension you will get access to about 100 additional modelling schemes including additional decision trees, rule learners and regression estimators.This extension combines two of the most widely used open source data mining solutions. By installing it, you can extend RapidMiner to everything what is possible with Weka while keeping the full analysis, preprocessing, and visualization power of RapidMiner.
[Icon]Finally, the two most widely used data analysis solutions – RapidMiner and R – are connected. Arbitrary R models and scripts can now be directly integrated into the RapidMiner analysis processes. The new R perspective offers the known R console together with the great plotting facilities of R. All variables and R scripts can be organized in the RapidMiner Repository.A directly included online help and multi-line editing makes the creation of R scripts much more comfortable.
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#Rstats for Business Intelligence

This is a short list of several known as well as lesser known R ( #rstats) language codes, packages and tricks to build a business intelligence application. It will be slightly Messy (and not Messi) but I hope to refine it someday when the cows come home.

It assumes that BI is basically-

a Database, a Document Database, a Report creation/Dashboard pulling software as well unique R packages for business intelligence.

What is business intelligence?

Seamless dissemination of data in the organization. In short let it flow- from raw transactional data to aggregate dashboards, to control and test experiments, to new and legacy data mining models- a business intelligence enabled organization allows information to flow easily AND capture insights and feedback for further action.

BI software has lately meant to be just reporting software- and Business Analytics has meant to be primarily predictive analytics. the terms are interchangeable in my opinion -as BI reports can also be called descriptive aggregated statistics or descriptive analytics, and predictive analytics is useless and incomplete unless you measure the effect in dashboards and summary reports.

Data Mining- is a bit more than predictive analytics- it includes pattern recognizability as well as black box machine learning algorithms. To further aggravate these divides, students mostly learn data mining in computer science, predictive analytics (if at all) in business departments and statistics, and no one teaches metrics , dashboards, reporting  in mainstream academia even though a large number of graduates will end up fiddling with spreadsheets or dashboards in real careers.

Using R with

1) Databases-

I created a short list of database connectivity with R here at https://rforanalytics.wordpress.com/odbc-databases-for-r/ but R has released 3 new versions since then.

The RODBC package remains the package of choice for connecting to SQL Databases.

http://cran.r-project.org/web/packages/RODBC/RODBC.pdf

Details on creating DSN and connecting to Databases are given at  https://rforanalytics.wordpress.com/odbc-databases-for-r/

For document databases like MongoDB and CouchDB

( what is the difference between traditional RDBMS and NoSQL if you ever need to explain it in a cocktail conversation http://dba.stackexchange.com/questions/5/what-are-the-differences-between-nosql-and-a-traditional-rdbms

Basically dispensing with the relational setup, with primary and foreign keys, and with the additional overhead involved in keeping transactional safety, often gives you extreme increases in performance

NoSQL is a kind of database that doesn’t have a fixed schema like a traditional RDBMS does. With the NoSQL databases the schema is defined by the developer at run time. They don’t write normal SQL statements against the database, but instead use an API to get the data that they need.

instead relating data in one table to another you store things as key value pairs and there is no database schema, it is handled instead in code.)

I believe any corporation with data driven decision making would need to both have atleast one RDBMS and one NoSQL for unstructured data-Ajay. This is a sweeping generic statement 😉 , and is an opinion on future technologies.

  • Use RMongo

From- http://tommy.chheng.com/2010/11/03/rmongo-accessing-mongodb-in-r/

http://plindenbaum.blogspot.com/2010/09/connecting-to-mongodb-database-from-r.html

Connecting to a MongoDB database from R using Java

http://nsaunders.wordpress.com/2010/09/24/connecting-to-a-mongodb-database-from-r-using-java/

Also see a nice basic analysis using R Mongo from

http://pseudofish.com/blog/2011/05/25/analysis-of-data-with-mongodb-and-r/

For CouchDB

please see https://github.com/wactbprot/R4CouchDB and

http://digitheadslabnotebook.blogspot.com/2010/10/couchdb-and-r.html

  • First install RCurl and RJSONIO. You’ll have to download the tar.gz’s if you’re on a Mac. For the second part, we’ll need to installR4CouchDB,

2) External Report Creating Software-

Jaspersoft- It has good integration with R and is a certified Revolution Analytics partner (who seem to be the only ones with a coherent #Rstats go to market strategy- which begs the question – why is the freest and finest stats software having only ONE vendor- if it was so great lots of companies would make exclusive products for it – (and some do -see https://rforanalytics.wordpress.com/r-business-solutions/ and https://rforanalytics.wordpress.com/using-r-from-other-software/)

From

http://www.jaspersoft.com/sites/default/files/downloads/events/Analytics%20-Jaspersoft-SEP2010.pdf

we see

http://jasperforge.org/projects/rrevodeployrbyrevolutionanalytics

RevoConnectR for JasperReports Server

RevoConnectR for JasperReports Server RevoConnectR for JasperReports Server is a Java library interface between JasperReports Server and Revolution R Enterprise’s RevoDeployR, a standardized collection of web services that integrates security, APIs, scripts and libraries for R into a single server. JasperReports Server dashboards can retrieve R charts and result sets from RevoDeployR.

http://jasperforge.org/plugins/esp_frs/optional_download.php?group_id=409

 

Using R and Pentaho
Extending Pentaho with R analytics”R” is a popular open source statistical and analytical language that academics and commercial organizations alike have used for years to get maximum insight out of information using advanced analytic techniques. In this twelve-minute video, David Reinke from Pentaho Certified Partner OpenBI provides an overview of R, as well as a demonstration of integration between R and Pentaho.
and from
R and BI – Integrating R with Open Source Business
Intelligence Platforms Pentaho and Jaspersoft
David Reinke, Steve Miller
Keywords: business intelligence
Increasingly, R is becoming the tool of choice for statistical analysis, optimization, machine learning and
visualization in the business world. This trend will only escalate as more R analysts transition to business
from academia. But whereas in academia R is often the central tool for analytics, in business R must coexist
with and enhance mainstream business intelligence (BI) technologies. A modern BI portfolio already includes
relational databeses, data integration (extract, transform, load – ETL), query and reporting, online analytical
processing (OLAP), dashboards, and advanced visualization. The opportunity to extend traditional BI with
R analytics revolves on the introduction of advanced statistical modeling and visualizations native to R. The
challenge is to seamlessly integrate R capabilities within the existing BI space. This presentation will explain
and demo an initial approach to integrating R with two comprehensive open source BI (OSBI) platforms –
Pentaho and Jaspersoft. Our efforts will be successful if we stimulate additional progress, transparency and
innovation by combining the R and BI worlds.
The demonstration will show how we integrated the OSBI platforms with R through use of RServe and
its Java API. The BI platforms provide an end user web application which include application security,
data provisioning and BI functionality. Our integration will demonstrate a process by which BI components
can be created that prompt the user for parameters, acquire data from a relational database and pass into
RServer, invoke R commands for processing, and display the resulting R generated statistics and/or graphs
within the BI platform. Discussion will include concepts related to creating a reusable java class library of
commonly used processes to speed additional development.

If you know Java- try http://ramanareddyg.blog.com/2010/07/03/integrating-r-and-pentaho-data-integration/

 

and I like this list by two venerable powerhouses of the BI Open Source Movement

http://www.openbi.com/demosarticles.html

Open Source BI as disruptive technology

http://www.openbi.biz/articles/osbi_disruption_openbi.pdf

Open Source Punditry

TITLE AUTHOR COMMENTS
Commercial Open Source BI Redux Dave Reinke & Steve Miller An review and update on the predictions made in our 2007 article focused on the current state of the commercial open source BI market. Also included is a brief analysis of potential options for commercial open source business models and our take on their applicability.
Open Source BI as Disruptive Technology Dave Reinke & Steve Miller Reprint of May 2007 DM Review article explaining how and why Commercial Open Source BI (COSBI) will disrupt the traditional proprietary market.

Spotlight on R

TITLE AUTHOR COMMENTS
R You Ready for Open Source Statistics? Steve Miller R has become the “lingua franca” for academic statistical analysis and modeling, and is now rapidly gaining exposure in the commercial world. Steve examines the R technology and community and its relevancy to mainstream BI.
R and BI (Part 1): Data Analysis with R Steve Miller An introduction to R and its myriad statistical graphing techniques.
R and BI (Part 2): A Statistical Look at Detail Data Steve Miller The usage of R’s graphical building blocks – dotplots, stripplots and xyplots – to create dashboards which require little ink yet tell a big story.
R and BI (Part 3): The Grooming of Box and Whiskers Steve Miller Boxplots and variants (e.g. Violin Plot) are explored as an essential graphical technique to summarize data distributions by categories and dimensions of other attributes.
R and BI (Part 4): Embellishing Graphs Steve Miller Lattices and logarithmic data transformations are used to illuminate data density and distribution and find patterns otherwise missed using classic charting techniques.
R and BI (Part 5): Predictive Modelling Steve Miller An introduction to basic predictive modelling terminology and techniques with graphical examples created using R.
R and BI (Part 6) :
Re-expressing Data		 Steve Miller How do you deal with highly skewed data distributions? Standard charting techniques on this “deviant” data often fail to illuminate relationships. This article explains techniques to re-express skewed data so that it is more understandable.
The Stock Market, 2007 Steve Miller R-based dashboards are presented to demonstrate the return performance of various asset classes during 2007.
Bootstrapping for Portfolio Returns: The Practice of Statistical Analysis Steve Miller Steve uses the R open source stats package and Monte Carlo simulations to examine alternative investment portfolio returns…a good example of applied statistics using R.
Statistical Graphs for Portfolio Returns Steve Miller Steve uses the R open source stats package to analyze market returns by asset class with some very provocative embedded trellis charts.
Frank Harrell, Iowa State and useR!2007 Steve Miller In August, Steve attended the 2007 Internation R User conference (useR!2007). This article details his experiences, including his meeting with long-time R community expert, Frank Harrell.
An Open Source Statistical “Dashboard” for Investment Performance Steve Miller The newly launched Dashboard Insight web site is focused on the most useful of BI tools: dashboards. With this article discussing the use of R and trellis graphics, OpenBI brings the realm of open source to this forum.
Unsexy Graphics for Business Intelligence Steve Miller Utilizing Tufte’s philosophy of maximizing the data to ink ratio of graphics, Steve demonstrates the value in dot plot diagramming. The R open source statistical/analytics software is showcased.
I think that the report generation package Brew would also qualify as a BI package, but large scale implementation remains to be seen in
a commercial business environment
  • brew: Creating Repetitive Reports
 brew: Templating Framework for Report Generation

brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module. http://bit.ly/jINmaI
  • Yarr- creating reports in R
to be continued ( when I have more time and the temperature goes down from 110F in Delhi, India)
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Interview with Rob La Gesse Chief Disruption Officer Rackspace

Here is an interview with Rob La Gesse ,Chief Disruption Officer ,Rackspace Hosting.
Ajay- Describe your career  journey from not finishing college to writing software to your present projects?
Rob- I joined the Navy right out of High School. I had neither the money for college, or a real desire for it. I had several roles in the Navy, to include a Combat Medic station with the US Marine Corps and eventually becoming a Neonatal Respiratory Therapist.

After the Navy I worked as a Respiratory Therapist, a roofer, and I repaired print shop equipment. Basically whatever it took to make a buck or two.  Eventually I started selling computers.  That led me to running a multi-line dial-up BBS and I taught myself how to program.  Eventually that led to a job with a small engineering company where we developed WiFi.

After the WiFi project I started consulting on my own.  I used Rackspace to host my clients, and eventually they hired me.  I’ve been here almost three years and have held several roles. I currently manage Social Media, building 43 and am involved in several other projects such as the Rackspace Startup Program.

Ajay-  What is building43 all about ?

Rob- Building43 is a web site devoted to telling the stories behind technology startups. Basically, after we hired Robert Scoble and Rocky Barbanica we were figuring out how best we could work with them to both highlight Rackspace and customers.  That idea expanded beyond customers to highlighting anyone doing something incredible in the technology industry – mostly software startups.  We’ve had interviews with people like Mark Zuckerberg, CEO and Founder of FaceBook.  We’ve broken some news on the site, but it isn’t really a news site. It is a story telling site.

Rackspace has met some amazing new customers through the relationships that started with an interview.

Ajay-  How is life as Robert Scoble’s boss. Is he an easy guy to work with? Does he have super powers while he types?

Rob- Robert isn’t much different to manage than the rest of my employees. He is a person – no super powers.  But he does establish a unique perspective on things because he gets to see so much new technology early.  Often earlier than almost anyone else. It helps him to spot trends that others might not be seeing yet.
Ajay – Hosting companies are so so many. What makes Rackspace special for different kinds of customers?
Rob- I think what we do better than anyone is add that human touch – the people really care about your business.  We are a company that is focused on building one of the greatest service companies on the planet.  We sell support.  Hosting is secondary to service. Our motto is Fanatic Support®

and we actually look for people focused on delivering amazing customer experiences during our interviewing and hiring practices. People that find a personal sense of pride and reward by helping others should apply at
Rackspace.  We are hiring like crazy!

Ajay – Where do you see technology and the internet 5 years down the line? (we will visit the answers in 5 years 🙂 )?
Rob- I think the shift to Cloud computing is going to be dramatic.  I think in five years we will be much further down that path.  The scaling, cost-effectiveness, and on-demand nature of the Cloud are just too compelling for companies not to embrace. This changes business in fundamental ways – lower capital expenses, no need for in house IT staff, etc will save companies a lot of money and let them focus more on their core businesses. Computing will become another utility.  I also think mobile use of computing will be much more common than it is today.  And it is VERY common today.  Phones will replace car keys and credit cards (they already are). This too will drive use of Cloud computing  because we all want our data wherever we are – on whatever computing device we happen tobe using.
Ajay- GoDaddy CEO shoots elephants. What do you do in your  spare time, if any.
Rob- Well, I don’t hunt.  We do shoot a lot of video though! I enjoy playing poker, specifically Texas Hold ’em.  It is a very people oriented game, and people are my passion.

Brief Biography- (in his own words from http://www.lagesse.org/about/)

My technical background includes working on the development of WiFi, writing wireless applications for the Apple Newton, mentoring/managing several software-based start-ups, running software quality assurance teams and more. In 2008 I joined Rackspace as an employee – a “Racker”.  I was previously a 7 year customer and the company impressed me. My initial role was as Director of Software Development for the Rackspace Cloud.  It was soon evident that I was better suited to a customer facing role since I LOVE talking to customers. I am currently the Director of Customer Development Chief Disruption Officer.  I manage building43 and enjoy working with Robert Scoble and Rocky Barbanica to make that happen.  The org chart says they work for me.  Reality tells me the opposite :)

Go take a look – I’m proud of what we are building there (pardon the pun!).

I do a lot of other stuff at Rackspace – mostly because they let me!  I love a company that lets me try. Rackspace does that.Going further back, I have been a Mayor (in Hawaii). I have written successful shareware software. I have managed employees all over the world. I have been all over the world. I have also done roofing, repaired high end print-shop equipment, been a Neonatal Respiratory Therapist, done CPR on a boat, in a plane, and in a hardware store (and of course in hospitals).

I have treated jumpers from the Golden Gate Bridge – and helped save a few. I have lived in Illinois (Kankakee), California (San Diego, San Francisco and Novato), Texas (Corpus Christi and San Antonio), Florida (Pensacola and Palm Bay), Hawaii (Honolulu/Fort Shafter) and several other places for shorter durations.

For the last 8+ years I have been a single parent – and have done an amazing job (yes, I am a proud papa) thanks to having great kids.  They are both in College now – something I did NOT manage to accomplish. I love doing anything someone thinks I am not qualified to do.

I can be contacted at rob (at) lagesse (dot) org

you can follow Rob at http://twitter.com/kr8tr

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