Sugar CRM: Forrester Webinar

Analytické CRM
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https://sugarcrmevents.webex.com/mw0306lb/mywebex/default.do?nomenu=true&siteurl=sugarcrmevents&service=6&main_url=https://sugarcrmevents.webex.com/ec0605lb/eventcenter/event/eventAction.do%3FtheAction%3Ddetail%26confViewID%3D279191911%26siteurl%3Dsugarcrmevents%26%26%26

Date and time:

Thursday, December 2, 2010 11:00 am 
Pacific Standard Time (San Francisco, GMT-08:00) 
Change time zone

Thursday, December 2, 2010 2:00 pm 
Eastern Standard Time (New York, GMT-05:00)
Thursday, December 2, 2010 7:00 pm
Western European Time (London, GMT)
Thursday, December 2, 2010 8:00 pm
Europe Time (Berlin, GMT+01:00)
Duration: 1 hour
Description:
Every organization wants to improve the way they manage their customer relationships. But until recently, adding robust CRM tools to your organization was a time consuming and cost prohibitive endeavor for many resources-constrained organizations. Until Now. On December 2 join us to learn how new developments in technology like open source, cloud computing and web 2.0 – are making it easier than ever to add a top notch CRM system to your operations. 

 

This live webinar hosted by SugarCRM will feature Forrester Research, Inc. Vice President William Band, named one CRM Magazine’s 2007 Influential Leaders. Mr. Band will discuss the current state of the market, review the major trends affecting the CRM landscape, and discuss some criteria you can use to ensure your next CRM decision is the right one.

In addition, all attendees of the live webinar will receive a complimentary download a recent Forrester Wave™ Report! Register today!

Speakers:

William Band, Vice President, Forrester Research
Martin Schneider, Sr. Director Communications, SugarCRM

Who Should Attend:
VP Sales, VP Marketing, CIO’s, Head of Customer Support and other technical decision makers

Amazon goes HPC and GPU: Dirk E to revise his R HPC book

Looking south above Interstate 80, the Eastsho...
Image via Wikipedia

Amazon just did a cluster Christmas present for us tech geek lizards- before Google could out doogle them with end of the Betas (cough- its on NDA)

Clusters used by Academic Departments now have a great chance to reduce cost without downsizing- but only if the CIO gets the email.

While Professor Goodnight of SAS / North Carolina University is still playing time sharing versus mind sharing games with analytical birdies – his 70 mill server farm set in Feb last is about to get ready

( I heard they got public subsidies for environment- but thats historic for SAS– taking public things private -right Prof as SAS itself began as a publicly funded project. and that was in the 1960s and they didnt even have no lobbyists as well. )

In realted R news, Dirk E has been thinking of a R HPC book without paying attention to Amazon but would now have to include Amazon

(he has been thinking of writing that book for 5 years, but hey he’s got a day job, consulting gigs with revo, photo ops at Google, a blog, packages to maintain without binaries, Dirk E we await thy book with bated holes.

Whos Dirk E – well http://dirk.eddelbuettel.com/ is like the Terminator of R project (in terms of unpronounceable surnames)

Back to the cause du jeure-

 

From http://aws.amazon.com/ec2/hpc-applications/ but minus corporate buzz words.

 

Unique to Cluster Compute and Cluster GPU instances is the ability to group them into clusters of instances for use with HPC

applications. This is particularly valuable for those applications that rely on protocols like Message Passing Interface (MPI) for tightly coupled inter-node communication.

Cluster Compute and Cluster GPU instances function just like other Amazon EC2 instances but also offer the following features for optimal performance with HPC applications:

  • When run as a cluster of instances, they provide low latency, full bisection 10 Gbps bandwidth between instances. Cluster sizes up through and above 128 instances are supported.
  • Cluster Compute and Cluster GPU instances include the specific processor architecture in their definition to allow developers to tune their applications by compiling applications for that specific processor architecture in order to achieve optimal performance.

The Cluster Compute instance family currently contains a single instance type, the Cluster Compute Quadruple Extra Large with the following specifications:

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

The Cluster GPU instance family currently contains a single instance type, the Cluster GPU Quadruple Extra Large with the following specifications:

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

.

Sign Up for Amazon EC2

PAWCON -This week in London

Watch out for the twitter hash news on PAWCON and the exciting agenda lined up. If your in the City- you may want to just drop in

http://www.predictiveanalyticsworld.com/london/2010/agenda.php#day1-7

Disclaimer- PAWCON has been a blog partner with Decisionstats (since the first PAWCON ). It is vendor neutral and features open source as well proprietary software, as well case studies from academia and Industry for a balanced view.

 

Little birdie told me some exciting product enhancements may be in the works including a not yet announced R plugin 😉 and the latest SAS product using embedded analytics and Dr Elder’s full day data mining workshop.

Citation-

http://www.predictiveanalyticsworld.com/london/2010/agenda.php#day1-7

Monday November 15, 2010
All conference sessions take place in Edward 5-7

8:00am-9:00am

Registration, Coffee and Danish
Room: Albert Suites


9:00am-9:50am

Keynote
Five Ways Predictive Analytics Cuts Enterprise Risk

All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.

Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a “loss customer” such as a bad debtor or an insurance policy-holder with high claims.

In this keynote session, Dr. Eric Siegel will reveal:

  • Five ways predictive analytics evolves your enterprise to reduce risk
  • Hidden sources of risk across operational functions
  • What every business should learn from insurance companies
  • How advancements have reversed the very meaning of fraud
  • Why “man + machine” teams are greater than the sum of their parts for
  • enterprise decision support

 

Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World

Top of this page ] [ Agenda overview ]


IBM9:50am-10:10am

Platinum Sponsor Presentation
The Analytical Revolution

The algorithms at the heart of predictive analytics have been around for years – in some cases for decades. But now, as we see predictive analytics move to the mainstream and become a competitive necessity for organisations in all industries, the most crucial challenges are to ensure that results can be delivered to where they can make a direct impact on outcomes and business performance, and that the application of analytics can be scaled to the most demanding enterprise requirements.

This session will look at the obstacles to successfully applying analysis at the enterprise level, and how today’s approaches and technologies can enable the true “industrialisation” of predictive analytics.

Speaker: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd

Top of this page ] [ Agenda overview ]


Deloitte10:10am-10:20am

Gold Sponsor Presentation
How Predictive Analytics is Driving Business Value

Organisations are increasingly relying on analytics to make key business decisions. Today, technology advances and the increasing need to realise competitive advantage in the market place are driving predictive analytics from the domain of marketers and tactical one-off exercises to the point where analytics are being embedded within core business processes.

During this session, Richard will share some of the focus areas where Deloitte is driving business transformation through predictive analytics, including Workforce, Brand Equity and Reputational Risk, Customer Insight and Network Analytics.

Speaker: Richard Fayers, Senior Manager, Deloitte Analytical Insight

Top of this page ] [ Agenda overview ]


10:20am-10:45am

Break / Exhibits
Room: Albert Suites


10:45am-11:35am
Healthcare
Case Study: Life Line Screening
Taking CRM Global Through Predictive Analytics

While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK.

Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc.

Speaker: Trish Mathe, Life Line Screening

Top of this page ] [ Agenda overview ]


11:35am-12:25pm
Open Source Analytics; Healthcare
Case Study: A large health care organization
The Rise of Open Source Analytics: Lowering Costs While Improving Patient Care

Rapidminer and R were the number 1 and 2 in this years annual KDNuggets data mining tool usage poll, followed by Knime on place 4 and Weka on place 6. So what’s going on here? Are these open source tools really that good or is their popularity strongly correlated with lower acquisition costs alone? This session answers these questions based on a real world case for a large health care organization and explains the risks & benefits of using open source technology. The final part of the session explains how these tools stack up against their traditional, proprietary counterparts.

Speaker: Jos van Dongen, Associate & Principal, DeltIQ Group

Top of this page ] [ Agenda overview ]


12:25pm-1:25pm

Lunch / Exhibits
Room: Albert Suites


1:25pm-2:15pm
Keynote
Thought Leader:
Case Study: Yahoo! and other large on-line e-businesses
Search Marketing and Predictive Analytics: SEM, SEO and On-line Marketing Case Studies

Search Engine Marketing is a $15B industry in the U.S. growing to double that number over the next 3 years. Worldwide the SEM market was over $50B in 2010. Not only is this a fast growing area of marketing, but it is one that has significant implications for brand and direct marketing and is undergoing rapid change with emerging channels such as mobile and social. What is unique about this area of marketing is a singularly heavy dependence on analytics:

 

  • Large numbers of variables and options
  • Real-time auctions/bids and a need to adjust strategies in real-time
  • Difficult optimization problems on allocating spend across a huge number of keywords
  • Fast-changing competitive terrain and heavy competition on the obvious channels
  • Complicated interactions between various channels and a large choice of search keyword expansion possibilities
  • Profitability and ROI analysis that are complex and often challenging

 

The size of the industry, its growing importance in marketing, its upcoming role in Mobile Advertising, and its uniquely heavy reliance on analytics makes it particularly interesting as an area for predictive analytics applications. In this session, not only will hear about some of the latest strategies and techniques to optimize search, you will hear case studies that illustrate the important role of analytics from industry practitioners.

Speaker: Usama Fayyad, , Ph.D., CEO, Open Insights

Top of this page ] [ Agenda overview ]


SAS2:15pm-2:35pm

Platinum Sponsor Presentation
Creating a Model Factory Using in-Database Analytics

With the ever-increasing number of analytical models required to make fact-based decisions, as well as increasing audit compliance regulations, it is more important than ever that these models can be created, monitored, retuned and deployed as quickly and automatically as possible. This paper, using a case study from a major financial organisation, will show how organisations can build a model factory efficiently using the latest SAS technology that utilizes the power of in-database processing.

Speaker: John Spooner, Analytics Specialist, SAS (UK)

Top of this page ] [ Agenda overview ]


2:35pm-2:45pm

Session Break
Room: Albert Suites


2:45pm-3:35pm

Retail
Case Study: SABMiller
Predictive Analytics & Global Marketing Strategy

Over the last few years SABMiller plc, the second largest brewing company in the world operating in 70 countries, has been systematically segmenting its markets in different countries globally in order optimize their portfolio strategy & align it to their long term country specific growth strategy. This presentation talks about the overall methodology followed and the challenges that had to be overcome both from a technical as well as from a change management stand point in order to successfully implement a standard analytics approach to diverse markets and diverse business positions in a highly global setting.

The session explains how country specific growth strategies were converted to objective variables and consumption occasion segments were created that differentiated the market effectively by their growth potential. In addition to this the presentation will also provide a discussion on issues like:

  • The dilemmas of static vs. dynamic solutions and standardization vs. adaptable solutions
  • Challenges in acceptability, local capability development, overcoming implementation inertia, cost effectiveness, etc
  • The role that business partners at SAB and analytics service partners at AbsolutData together play in providing impactful and actionable solutions

 

Speaker: Anne Stephens, SABMiller plc

Speaker: Titir Pal, AbsolutData

Top of this page ] [ Agenda overview ]


3:35pm-4:25pm

Retail
Case Study: Overtoom Belgium
Increasing Marketing Relevance Through Personalized Targeting

 

Since many years, Overtoom Belgium – a leading B2B retailer and division of the French Manutan group – focuses on an extensive use of CRM. In this presentation, we demonstrate how Overtoom has integrated Predictive Analytics to optimize customer relationships. In this process, they employ analytics to develop answers to the key question: “which product should we offer to which customer via which channel”. We show how Overtoom gained a 10% revenue increase by replacing the existing segmentation scheme with accurate predictive response models. Additionally, we illustrate how Overtoom succeeds to deliver more relevant communications by offering personalized promotional content to every single customer, and how these personalized offers positively impact Overtoom’s conversion rates.

Speaker: Dr. Geert Verstraeten, Python Predictions

Top of this page ] [ Agenda overview ]


4:25pm-4:50pm

Break / Exhibits
Room: Albert Suites


4:50pm-5:40pm
Uplift Modelling:
Case Study: Lloyds TSB General Insurance & US Bank
Uplift Modelling: You Should Not Only Measure But Model Incremental Response

Most marketing analysts understand that measuring the impact of a marketing campaign requires a valid control group so that uplift (incremental response) can be reported. However, it is much less widely understood that the targeting models used almost everywhere do not attempt to optimize that incremental measure. That requires an uplift model.

This session will explain why a switch to uplift modelling is needed, illustrate what can and does go wrong when they are not used and the hugely positive impact they can have when used effectively. It will also discuss a range of approaches to building and assessing uplift models, from simple basic adjustments to existing modelling processes through to full-blown uplift modelling.

The talk will use Lloyds TSB General Insurance & US Bank as a case study and also illustrate real-world results from other companies and sectors.

 

Speaker: Nicholas Radcliffe, Founder and Director, Stochastic Solutions

Top of this page ] [ Agenda overview ]


5:40pm-6:30pm

Consumer services
Case Study: Canadian Automobile Association and other B2C examples
The Diminishing Marginal Returns of Variable Creation in Predictive Analytics Solutions

 

Variable Creation is the key to success in any predictive analytics exercise. Many different approaches are adopted during this process, yet there are diminishing marginal returns as the number of variables increase. Our organization conducted a case study on four existing clients to explore this so-called diminishing impact of variable creation on predictive analytics solutions. Existing predictive analytics solutions were built using our traditional variable creation process. Yet, presuming that we could exponentially increase the number of variables, we wanted to determine if this added significant benefit to the existing solution.

Speaker: Richard Boire, BoireFillerGroup

Top of this page ] [ Agenda overview ]


6:30pm-7:30pm

Reception / Exhibits
Room: Albert Suites


Tuesday November 16, 2010
All conference sessions take place in Edward 5-7

8:00am-9:00am

Registration, Coffee and Danish
Room: Albert Suites


9:00am-9:55am
Keynote
Multiple Case Studies: Anheuser-Busch, Disney, HP, HSBC, Pfizer, and others
The High ROI of Data Mining for Innovative Organizations

Data mining and advanced analytics can enhance your bottom line in three basic ways, by 1) streamlining a process, 2) eliminating the bad, or 3) highlighting the good. In rare situations, a fourth way – creating something new – is possible. But modern organizations are so effective at their core tasks that data mining usually results in an iterative, rather than transformative, improvement. Still, the impact can be dramatic.

Dr. Elder will share the story (problem, solution, and effect) of nine projects conducted over the last decade for some of America’s most innovative agencies and corporations:

    Streamline:

  • Cross-selling for HSBC
  • Image recognition for Anheuser-Busch
  • Biometric identification for Lumidigm (for Disney)
  • Optimal decisioning for Peregrine Systems (now part of Hewlett-Packard)
  • Quick decisions for the Social Security Administration
    Eliminate Bad:

  • Tax fraud detection for the IRS
  • Warranty Fraud detection for Hewlett-Packard
    Highlight Good:

  • Sector trading for WestWind Foundation
  • Drug efficacy discovery for Pharmacia & UpJohn (now Pfizer)

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: John Elder, Ph.D., Elder Research, Inc.

Also see Dr. Elder’s full-day workshop

 

Top of this page ] [ Agenda overview ]


9:55am-10:30am

Break / Exhibits
Room: Albert Suites


10:30am-11:20am
Telecommunications
Case Study: Leading Telecommunications Operator
Predictive Analytics and Efficient Fact-based Marketing

The presentation describes what are the major topics and issues when you introduce predictive analytics and how to build a Fact-Based marketing environment. The introduced tools and methodologies proved to be highly efficient in terms of improving the overall direct marketing activity and customer contact operations for the involved companies. Generally, the introduced approaches have great potential for organizations with large customer bases like Mobile Operators, Internet Giants, Media Companies, or Retail Chains.

Main Introduced Solutions:-Automated Serial Production of Predictive Models for Campaign Targeting-Automated Campaign Measurements and Tracking Solutions-Precise Product Added Value Evaluation.

Speaker: Tamer Keshi, Ph.D., Long-term contractor, T-Mobile

Speaker: Beata Kovacs, International Head of CRM Solutions, Deutsche Telekom

Top of this page ] [ Agenda overview ]


11:20am-11:25am

Session Changeover


11:25am-12:15pm
Thought Leader
Nine Laws of Data Mining

Data mining is the predictive core of predictive analytics, a business process that finds useful patterns in data through the use of business knowledge. The industry standard CRISP-DM methodology describes the process, but does not explain why the process takes the form that it does. I present nine “laws of data mining”, useful maxims for data miners, with explanations that reveal the reasons behind the surface properties of the data mining process. The nine laws have implications for predictive analytics applications: how and why it works so well, which ambitions could succeed, and which must fail.

 

Speaker: Tom Khabaza, khabaza.com

 

Top of this page ] [ Agenda overview ]


12:15pm-1:30pm

Lunch / Exhibits
Room: Albert Suites


1:30pm-2:25pm
Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream

Predictive analytics has taken off, across industry sectors and across applications in marketing, fraud detection, credit scoring and beyond. Where exactly are we in the process of crossing the chasm toward pervasive deployment, and how can we ensure progress keeps up the pace and stays on target?

This expert panel will address:

  • How much of predictive analytics’ potential has been fully realized?
  • Where are the outstanding opportunities with greatest potential?
  • What are the greatest challenges faced by the industry in achieving wide scale adoption?
  • How are these challenges best overcome?

 

Panelist: John Elder, Ph.D., Elder Research, Inc.

Panelist: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd

Panelist: Udo Sglavo, Global Analytic Solutions Manager, SAS

Panel moderator: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World


2:25pm-2:30pm

Session Changeover


2:30pm-3:20pm
Crowdsourcing Data Mining
Case Study: University of Melbourne, Chessmetrics
Prediction Competitions: Far More Than Just a Bit of Fun

Data modelling competitions allow companies and researchers to post a problem and have it scrutinised by the world’s best data scientists. There are an infinite number of techniques that can be applied to any modelling task but it is impossible to know at the outset which will be most effective. By exposing the problem to a wide audience, competitions are a cost effective way to reach the frontier of what is possible from a given dataset. The power of competitions is neatly illustrated by the results of a recent bioinformatics competition hosted by Kaggle. It required participants to pick markers in HIV’s genetic sequence that coincide with changes in the severity of infection. Within a week and a half, the best entry had already outdone the best methods in the scientific literature. This presentation will cover how competitions typically work, some case studies and the types of business modelling challenges that the Kaggle platform can address.

Speaker: Anthony Goldbloom, Kaggle Pty Ltd

Top of this page ] [ Agenda overview ]


3:20pm-3:50pm

Breaks /Exhibits
Room: Albert Suites


3:50pm-4:40pm
Human Resources; e-Commerce
Case Study: Naukri.com, Jeevansathi.com
Increasing Marketing ROI and Efficiency of Candidate-Search with Predictive Analytics

InfoEdge, India’s largest and most profitable online firm with a bouquet of internet properties has been Google’s biggest customer in India. Our team used predictive modeling to double our profits across multiple fronts. For Naukri.com, India’s number 1 job portal, predictive models target jobseekers most relevant to the recruiter. Analytical insights provided a deeper understanding of recruiter behaviour and informed a redesign of this product’s recruiter search functionality. This session will describe how we did it, and also reveal how Jeevansathi.com, India’s 2nd-largest matrimony portal, targets the acquisition of consumers in the market for marriage.

 

Speaker: Suvomoy Sarkar, Chief Analytics Officer, HT Media & Info Edge India (parent company of the two companies above)

 

Top of this page ] [ Agenda overview ]


4:40pm-5:00pm
Closing Remarks

Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World

Top of this page ] [ Agenda overview ]


Wednesday November 17, 2010

Full-day Workshop
The Best and the Worst of Predictive Analytics:
Predictive Modeling Methods and Common Data Mining Mistakes

Click here for the detailed workshop description

  • Workshop starts at 9:00am
  • First AM Break from 10:00 – 10:15
  • Second AM Break from 11:15 – 11:30
  • Lunch from 12:30 – 1:15pm
  • First PM Break: 2:00 – 2:15
  • Second PM Break: 3:15 – 3:30
  • Workshop ends at 4:30pm

Speaker: John Elder, Ph.D., CEO and Founder, Elder Research, Inc.

 

Business Intelligence and Stat Computing: The White Man's Last Stand

Unknown White Male
Image via Wikipedia

Name an industry in which top level executives are mostly white males, new recruits are mostly male (white or Indian/Chinese), women are primarily shunted into publicity relationships, social media or marketing.

Statistical Computing And Business Intelligence are the white man’s last stand to preserve an exclusive club of hail fellow well met and lets catch up after drinks culture. Newer startups are the exception in the business intelligence world , but  a whiter face helps (so do an Indian or Chinese male) to attract a mostly male white venture capital industry.

I have earlier talked about technology being totally dominated by Asian males at grad student level and ASA membership almost not representing minorities like blacks and yes women- but this is about corporate culture in the traditional BI world.

If you are connected to the BI or Stat Computing world, who would you rather hire AND who have you actually hired- with identical resumes

White Male or White Female or Brown Indian Male/Female or Yellow Male/Female or Black Male or Black Female

How many Black Grad Assistants do you see in tech corridors- (Nah- it is easier to get a  hard working Chinese /Indian- who smiles and does a great job at $12/hour)

How many non- Asian non white Authors do you see in technology and does that compare to pie chart below


racist image Pictures, Images and Photos

Note_ 2010 Census numbers arent available for STEM, and I was unable to find ethnic background for various technology companies, because though these numbers are collected for legal purposes, they are not publicly shared.

Any technology company which has more than 40% women , or more than 10% blacks would be fairly representative to the US population. Anecdotal evidence suggests European employment for minorities is worse (especially for Asians) but better for women.

Any data sources to support/ refute these hypothesis are welcome for purposes of scientific inquiry.

racist math image Pictures, Images and Photos

Cloud Computing with R

Illusion of Depth and Space (4/22) - Rotating ...
Image by Dominic's pics via Flickr

Here is a short list of resources and material I put together as starting points for R and Cloud Computing It’s a bit messy but overall should serve quite comprehensively.

Cloud computing is a commonly used expression to imply a generational change in computing from desktop-servers to remote and massive computing connections,shared computers, enabled by high bandwidth across the internet.

As per the National Institute of Standards and Technology Definition,
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.

(Citation: The NIST Definition of Cloud Computing

Authors: Peter Mell and Tim Grance
Version 15, 10-7-09
National Institute of Standards and Technology, Information Technology Laboratory
http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc)

R is an integrated suite of software facilities for data manipulation, calculation and graphical display.

From http://cran.r-project.org/doc/FAQ/R-FAQ.html#R-Web-Interfaces

R Web Interfaces

Rweb is developed and maintained by Jeff Banfield. The Rweb Home Page provides access to all three versions of Rweb—a simple text entry form that returns output and graphs, a more sophisticated JavaScript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language. All of the Rweb versions can analyze Web accessible datasets if a URL is provided.
The paper “Rweb: Web-based Statistical Analysis”, providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.jstatsoft.org/v04/i01/).

Ulf Bartel has developed R-Online, a simple on-line programming environment for R which intends to make the first steps in statistical programming with R (especially with time series) as easy as possible. There is no need for a local installation since the only requirement for the user is a JavaScript capable browser. See http://osvisions.com/r-online/ for more information.

Rcgi is a CGI WWW interface to R by MJ Ray. It had the ability to use “embedded code”: you could mix user input and code, allowing the HTMLauthor to do anything from load in data sets to enter most of the commands for users without writing CGI scripts. Graphical output was possible in PostScript or GIF formats and the executed code was presented to the user for revision. However, it is not clear if the project is still active.

Currently, a modified version of Rcgi by Mai Zhou (actually, two versions: one with (bitmap) graphics and one without) as well as the original code are available from http://www.ms.uky.edu/~statweb/.

CGI-based web access to R is also provided at http://hermes.sdu.dk/cgi-bin/go/. There are many additional examples of web interfaces to R which basically allow to submit R code to a remote server, see for example the collection of links available from http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse.

David Firth has written CGIwithR, an R add-on package available from CRAN. It provides some simple extensions to R to facilitate running R scripts through the CGI interface to a web server, and allows submission of data using both GET and POST methods. It is easily installed using Apache under Linux and in principle should run on any platform that supports R and a web server provided that the installer has the necessary security permissions. David’s paper “CGIwithR: Facilities for Processing Web Forms Using R” was published in the Journal of Statistical Software (http://www.jstatsoft.org/v08/i10/). The package is now maintained by Duncan Temple Lang and has a web page athttp://www.omegahat.org/CGIwithR/.

Rpad, developed and actively maintained by Tom Short, provides a sophisticated environment which combines some of the features of the previous approaches with quite a bit of JavaScript, allowing for a GUI-like behavior (with sortable tables, clickable graphics, editable output), etc.
Jeff Horner is working on the R/Apache Integration Project which embeds the R interpreter inside Apache 2 (and beyond). A tutorial and presentation are available from the project web page at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject.

Rserve is a project actively developed by Simon Urbanek. It implements a TCP/IP server which allows other programs to use facilities of R. Clients are available from the web site for Java and C++ (and could be written for other languages that support TCP/IP sockets).

OpenStatServer is being developed by a team lead by Greg Warnes; it aims “to provide clean access to computational modules defined in a variety of computational environments (R, SAS, Matlab, etc) via a single well-defined client interface” and to turn computational services into web services.

Two projects use PHP to provide a web interface to R. R_PHP_Online by Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb. R-php is actively developed by Alfredo Pontillo and Angelo Mineo and provides both a web interface to R and a set of pre-specified analyses that need no R code input.

webbioc is “an integrated web interface for doing microarray analysis using several of the Bioconductor packages” and is designed to be installed at local sites as a shared computing resource.

Rwui is a web application to create user-friendly web interfaces for R scripts. All code for the web interface is created automatically. There is no need for the user to do any extra scripting or learn any new scripting techniques. Rwui can also be found at http://rwui.cryst.bbk.ac.uk.

Finally, the R.rsp package by Henrik Bengtsson introduces “R Server Pages”. Analogous to Java Server Pages, an R server page is typically HTMLwith embedded R code that gets evaluated when the page is requested. The package includes an internal cross-platform HTTP server implemented in Tcl, so provides a good framework for including web-based user interfaces in packages. The approach is similar to the use of the brew package withRapache with the advantage of cross-platform support and easy installation.

Also additional R Cloud Computing Use Cases
http://wwwdev.ebi.ac.uk/Tools/rcloud/

ArrayExpress R/Bioconductor Workbench

Remote access to R/Bioconductor on EBI’s 64-bit Linux Cluster

Start the workbench by downloading the package for your operating system (Macintosh or Windows), or via Java Web Start, and you will get access to an instance of R running on one of EBI’s powerful machines. You can install additional packages, upload your own data, work with graphics and collaborate with colleagues, all as if you are running R locally, but unlimited by your machine’s memory, processor or data storage capacity.

  • Most up-to-date R version built for multicore CPUs
  • Access to all Bioconductor packages
  • Access to our computing infrastructure
  • Fast access to data stored in EBI’s repositories (e.g., public microarray data in ArrayExpress)

Using R Google Docs
http://www.omegahat.org/RGoogleDocs/run.pdf
It uses the XML and RCurl packages and illustrates that it is relatively quick and easy
to use their primitives to interact with Web services.

Using R with Amazon
Citation
http://rgrossman.com/2009/05/17/running-r-on-amazons-ec2/

Amazon’s EC2 is a type of cloud that provides on demand computing infrastructures called an Amazon Machine Images or AMIs. In general, these types of cloud provide several benefits:

  • Simple and convenient to use. An AMI contains your applications, libraries, data and all associated configuration settings. You simply access it. You don’t need to configure it. This applies not only to applications like R, but also can include any third-party data that you require.
  • On-demand availability. AMIs are available over the Internet whenever you need them. You can configure the AMIs yourself without involving the service provider. You don’t need to order any hardware and set it up.
  • Elastic access. With elastic access, you can rapidly provision and access the additional resources you need. Again, no human intervention from the service provider is required. This type of elastic capacity can be used to handle surge requirements when you might need many machines for a short time in order to complete a computation.
  • Pay per use. The cost of 1 AMI for 100 hours and 100 AMI for 1 hour is the same. With pay per use pricing, which is sometimes called utility pricing, you simply pay for the resources that you use.

Connecting to R on Amazon EC2- Detailed tutorials
Ubuntu Linux version
https://decisionstats.com/2010/09/25/running-r-on-amazon-ec2/
and Windows R version
https://decisionstats.com/2010/10/02/running-r-on-amazon-ec2-windows/

Connecting R to Data on Google Storage and Computing on Google Prediction API
https://github.com/onertipaday/predictionapirwrapper
R wrapper for working with Google Prediction API

This package consists in a bunch of functions allowing the user to test Google Prediction API from R.
It requires the user to have access to both Google Storage for Developers and Google Prediction API:
see
http://code.google.com/apis/storage/ and http://code.google.com/apis/predict/ for details.

Example usage:

#This example requires you had previously created a bucket named data_language on your Google Storage and you had uploaded a CSV file named language_id.txt (your data) into this bucket – see for details
library(predictionapirwrapper)

and Elastic R for Cloud Computing
http://user2010.org/tutorials/Chine.html

Abstract

Elastic-R is a new portal built using the Biocep-R platform. It enables statisticians, computational scientists, financial analysts, educators and students to use cloud resources seamlessly; to work with R engines and use their full capabilities from within simple browsers; to collaborate, share and reuse functions, algorithms, user interfaces, R sessions, servers; and to perform elastic distributed computing with any number of virtual machines to solve computationally intensive problems.
Also see Karim Chine’s http://biocep-distrib.r-forge.r-project.org/

R for Salesforce.com

At the point of writing this, there seem to be zero R based apps on Salesforce.com This could be a big opportunity for developers as both Apex and R have similar structures Developers could write free code in R and charge for their translated version in Apex on Salesforce.com

Force.com and Salesforce have many (1009) apps at
http://sites.force.com/appexchange/home for cloud computing for
businesses, but very few forecasting and statistical simulation apps.

Example of Monte Carlo based app is here
http://sites.force.com/appexchange/listingDetail?listingId=a0N300000016cT9EAI#

These are like iPhone apps except meant for business purposes (I am
unaware if any university is offering salesforce.com integration
though google apps and amazon related research seems to be on)

Force.com uses a language called Apex  and you can see
http://wiki.developerforce.com/index.php/App_Logic and
http://wiki.developerforce.com/index.php/An_Introduction_to_Formulas
Apex is similar to R in that is OOPs

SAS Institute has an existing product for taking in Salesforce.com data.

A new SAS data surveyor is
available to access data from the Customer Relationship Management
(CRM) software vendor Salesforce.com. at
http://support.sas.com/documentation/cdl/en/whatsnew/62580/HTML/default/viewer.htm#datasurveyorwhatsnew902.htm)

Personal Note-Mentioning SAS in an email to a R list is a big no-no in terms of getting a response and love. Same for being careless about which R help list to email (like R devel or R packages or R help)

For python based cloud see http://pi-cloud.com

Cisco SocialMiner

A highly simplified version of the RSS feed ic...
Image via Wikipedia

A new product from Cisco to mine social media for analytics on sentiment-

http://www.cisco.com/en/US/products/ps11349/index.html

Cisco SocialMiner is a social media customer care solution that can help you proactively respond to customers and prospects communicating through public social media networks like Twitter, Facebook, or other public forums or blogging sites. By providing social media monitoring, queuing, and workflow to organize customer posts on social media networks and deliver them to your social media customer care team, your company can respond to customers in real time using the same social network they are using.

Cisco SocialMiner provides:

  • The ability to configure multiple campaigns to search for customer postings on the public social web about your company’s products, services, or area of expertise
  • Filtering of social contacts based on preconfigured campaign filters to focus campaign searches
  • Routing of social contacts to skilled customer care representatives in the contact center or to experts in the enterprise–multiple people can work together to handle responses to customer postings through shared work queues
  • Detailed metrics for social media customer care activities, campaign reports, and team reports

With Cisco SocialMiner, your company can listen and respond to customer conversations originating in the social web. Being proactive can help your company enhance its service, improve customer loyalty, garner new customers, and protect your brand.

Table 1. Features and Benefits of Cisco SocialMiner 8.5

Feature Benefits
Product Baseline Features
Social media feeds

• Feeds are configurable sources to capture public social contacts that contain specific words, terms, or phrases.

• Feeds enable you to search for information on the public social web about your company’s products, services, or area of expertise.

• Cisco SocialMiner supports the following types of feeds:

• Facebook

• Twitter
Campaign management

• Groups feeds into campaigns to organize all posting activity related to a product category or business objective

• Produces metrics on campaign activity

• Provides the ability to configure multiple campaigns to search for customer postings on specific products or services

• Groups social contacts for handling by the social media customer care team

• Enables filtering of social contacts based on preconfigured campaign filters to focus campaign searches
Route and queue social contacts

• Enables routing of social contacts to skilled customer care representatives in the contact center

• Draws on expertise in the enterprise by allowing multiple people in the enterprise to work together to handle responses to customer postings through shared work queues

• Enables automated distribution of work to improve efficiency and effectiveness of social media engagement
Tagging

• Allows work to be routed to the appropriate team by grouping each post or social contact into different categories; for example, a post can be marked with the “customer_support” tag; this post will then appear on a customer support agent’s queue for processing
Social media customer care metrics

• Provides detailed metrics on social media customer care activities, campaign reports, and team reports

• Measures work and results

• Manages to service-level goals

• Supports brand management

• Optimizes staffing

• Includes dashboarding of social media posting activity when Cisco Unified Intelligence Center is used
Reporting for social contacts

• Provides a reporting database that can be accessed using any reporting tool, including Cisco Unified Intelligence Center

• Enables customer care management to accurately report on and track social media interactions by the contact center
OpenSocial-compliant gadgets

Representational State Transfer (REST) application programming interfaces (APIs)

• Provides flexible user interface options

• Enables extensive opportunities for customization
Optional integration with full suite of Cisco Collaboration tools

• Allows you to take advantage of the full suite of Cisco Collaboration tools, including Cisco Quad, Cisco Show and Share, and Cisco Pulse technology, to help your social media customer care team quickly find answers to help customers efficiently and effectively

• Easy to maintain with existing IT personnel
Operating Environment
Cisco Unified Computing System(UCS) C-Series or B-Series Servers

• Requires a Cisco UCS C-Series or B-Series Server.

• Server consolidation means lower cost per server with Cisco UCS Servers.
Architecture
Scalability

• One server supports up to 30 simultaneous social media customer care users and 10,000 social contacts per hour.
Management
Cisco Unified Real-Time Monitoring Tool (RTMT)

• Operational management is enhanced through integration with the Cisco Unified RTMT, providing consistent application monitoring across Cisco Unified Communications Solutions.
Simple Network Management Protocol (SNMP)

• SNMP with an associated MIB is supported through the Cisco Voice Operating System (VOS).
Reporting
Cisco Unified Intelligence Center

• Create customizable reports of social media customer care events using Cisco Unified Intelligence Center (purchased separately).

 

 

Is 21 st century cloud computing same as 1960's time sharing

Diagram showing three main types of cloud comp...
Image via Wikipedia

and yes Prof Goodnight, cloud computing is not time sharing. (Dr J was on a roll there- bashing open source AND cloud computing in the SAME interview at http://www.cbronline.com/news/sas-ceo-says-cep-open-source-and-cloud-bi-have-limited-appeal)

What was time sharing? In the 1960’s when people had longer hair, listened to the Beatles and IBM actually owned ALL computers-

http://en.wikipedia.org/wiki/Time-sharing

or is it?

The Internet has brought the general concept of time-sharing back into popularity. Expensive corporate server farms costing millions can host thousands of customers all sharing the same common resources. As with the early serial terminals, websites operate primarily in bursts of activity followed by periods of idle time. This bursting nature permits the service to be used by many website customers at once, and none of them notice any delays in communications until the servers start to get very busy.

What is 21 st century cloud computing? Well… they are still writing papers to define it BUT http://en.wikipedia.org/wiki/Cloud_computing

Cloud computing is Web-based processing, whereby shared resources, software, and information are provided to computers and other devices (such as smartphones) on demand over the Internet.