Tag: model

KXEN Update

Update from a very good data mining software company, KXEN –

  1. Longtime Chairman and founder Roger Haddad is retiring but would be a Board Member. See his interview with Decisionstats here https://decisionstats.wordpress.com/2009/01/05/interview-roger-haddad-founder-of-kxen-automated-modeling-software/ (note images were hidden due to migration from .com to .wordpress.com )
  2. New Members of Leadership are as-
John Ball, CEOJohn Ball
Chief Executive Officer

John Ball brings 20 years of experience in enterprise software, deep expertise in business intelligence and CRM applications, and a proven track record of success driving rapid growth at highly innovative companies.

Prior to joining KXEN, Mr. Ball served in several executive roles at salesforce.com, the leading provider of SaaS applications. Most recently, John served as VP & General Manager, Analytics and Reporting Products, where he spearheaded salesforce.com’s foray into CRM analytics and business intelligence. John also served as VP & General Manager, Service and Support Applications at salesforce.com, where he successfully grew the business to become the second largest and fastest growing product line at salesforce.com. Before salesforce.com, Ball was founder and CEO of Netonomy, the leading provider of customer self-service solutions for the telecommunications industry. Ball also held a number of executive roles at Business Objects, including General Manager, Web Products, where delivered to market the first 3 versions of WebIntelligence. Ball has a master’s degree in electrical engineering from Georgia Tech and a master’s degree in electric

I hope John atleast helps build a KXEN Force.com application- there are only 2 data mining apps there on App Exchange. Also on the wish list  more social media presence, a Web SaaS/Amazon API for KXEN, greater presence in American/Asian conferences, and a solution for SME’s (which cannot afford the premium pricing of the flagship solution. An alliance with bigger BI vendors like Oracle, SAP or IBM  for selling the great social network analysis.

Bill Russell as Non Executive Chairman-

Bill Russell as Non-executive Chairman of the Board, effective July 16 2010. Russell has 30 years of operational experience in enterprise software, with a special focus on business intelligence, analytics, and databases.Russell held a number of senior-level positions in his more than 20 years at Hewlett-Packard, including Vice President and General Manager of the multi-billion dollar Enterprise Systems Group. He has served as Non-executive Chairman of the Board for Sylantro Systems Corporation, webMethods Inc., and Network Physics, Inc. and has served as a board director for Cognos Inc. In addition to KXEN, Russell currently serves on the boards of Saba, PROS Holdings Inc., Global 360, ParAccel Inc., and B.T. Mancini Company.

Xavier Haffreingue as senior vice president, worldwide professional services and solutions.
He has almost 20 years of international enterprise software experience gained in the CRM, BI, Web and database sectors. Haffreingue joins KXEN from software provider Axway where he was VP global support operations. Prior to Axway, he held various leadership roles in the software industry, including VP self service solutions at Comverse Technologies and VP professional services and support at Netonomy, where he successfully delivered multi-million dollar projects across Europe, Asia-Pacific and Africa. Before that he was with Business Objects and Sybase, where he ran support and services in southern Europe managing over 2,500 customers in more than 20 countries.

David Guercio  as senior vice president, Americas field operations. Guercio brings to the role more than 25 years experience of building and managing high-achieving sales teams in the data mining, business intelligence and CRM markets. Guercio comes to KXEN from product lifecycle management vendor Centric Software, where he was EVP sales and client services. Prior to Centric, he was SVP worldwide sales and client services at Inxight Software, where he was also Chairman and CEO of the company’s Federal Systems Group, a subsidiary of Inxight that saw success in the US Federal Government intelligence market. The success in sales growth and penetration into the federal government led to the acquisition of Inxight by Business Objects in 2007, where Guercio then led the Inxight sales organization until Business Objects was acquired by SAP. Guercio was also a key member of the management team and a co-founder at Neovista, an early pioneer in data mining and predictive analytics. Additionally, he held the positions of director of sales and VP of professional services at Metaphor Computer Systems, one of the first data extraction solutions companies, which was acquired by IBM. During his career, Guercio also held executive positions at Resonate and SiGen.

3) Venture Capital funding to fund expansion-

It has closed $8 million in series D funding to further accelerate its growth and international expansion. The round was led by NextStage and included participation from existing investors XAnge Capital, Sofinnova Ventures, Saints Capital and Motorola Ventures.

This was done after John Ball had joined as CEO.

4) Continued kudos from analysts and customers for it’s technical excellence.

KXEN was named a leader in predictive analytics and data mining by Forrester Research (1) and was rated highest for commercial deployments of social network analytics by Frost & Sullivan (2)

Also it became an alliance partner of Accenture- which is also a prominent SAS partner as well.

In Database Optimization-

In KXEN V5.1, a new data manipulation module (ADM) is provided in conjunction with scoring to optimize database workloads and provide full in-database model deployment. Some leading data mining vendors are only now beginning to offer this kind of functionality, and then with only one or two selected databases, giving KXEN a more than five-year head start. Some other vendors are only offering generic SQL generation, not optimized for each database, and do not provide the wealth of possible outputs for their scoring equations: For example, real operational applications require not only to generate scores, but decision probabilities, error bars, individual input contributions – used to derive reasons of decision and more, which are available in KXEN in-database scoring modules.

Since 2005, KXEN has leveraged databases as the data manipulation engine for analytical dataset generation. In 2008, the ADM (Analytical Data Management) module delivered a major enhancement by providing a very easy to use data manipulation environment with unmatched productivity and efficiency. ADM works as a generator of optimized database-specific SQL code and comes with an integrated layer for the management of meta-data for analytics.

KXEN Modeling Factory- (similar to SAS’s recent product Rapid Predictive Modeler http://www.sas.com/resources/product-brief/rapid-predictive-modeler-brief.pdf and http://jtonedm.com/2010/09/02/first-look-rapid-predictive-modeler/)

KXEN Modeling Factory (KMF) has been designed to automate the development and maintenance of predictive analytics-intensive systems, especially systems that include large numbers of models, vast amounts of data or require frequent model refreshes. Information about each project and model is monitored and disseminated to ensure complete management and oversight and to facilitate continual improvement in business performance.

Main Functions

Schedule: creation of the Analytic Data Set (ADS), setup of how and when to score, setup of when and how to perform model retraining and refreshes …

Report: Monitormodel execution over time, Track changes in model quality over time, see how useful one variable is by considering its multiple instance in models …

Notification: Rather than having to wade through pages of event logs, KMF Department allows users to manage by exception through notifications.

Other products from KXEN have been covered here before https://decisionstats.wordpress.com/tag/kxen/ , including Structural Risk Minimization- https://decisionstats.wordpress.com/2009/04/27/kxen-automated-regression-modeling/

Thats all for the KXEN update- all the best to the new management team and a splendid job done by Roger Haddad in creating what is France and Europe’s best known data mining company.

Note- Source – http://www.kxen.com


Google moving on from MapReduce: rest of world still catching up

Apparently it is true as per the Register, but details in a paper next month- It is called Google Caffeine.

http://www.theregister.co.uk/2010/09/09/google_caffeine_explained/

Caffeine expands on BigTable to create a kind of database programming model that lets the company make changes to its web index without rebuilding the entire index from scratch. “[Caffeine] is a database-driven, Big Table–variety indexing system,” Lipkovitz tells The Reg, saying that Google will soon publish a paper discussing the system. The paper, he says, will be delivered next month at the USENIX Symposium on Operating Systems Design and Implementation (OSDI).

and interestingly

MapReduce, he says, isn’t suited to calculations that need to occur in near real-time.

MapReduce is a sequence of batch operations, and generally, Lipkovits explains, you can’t start your next phase of operations until you finish the first. It suffers from “stragglers,” he says. If you want to build a system that’s based on series of map-reduces, there’s a certain probability that something will go wrong, and this gets larger as you increase the number of operations. “You can’t do anything that takes a relatively short amount of time,” Lipkovitz says, “so we got rid of it.”

With Caffeine, Google can update its index by making direct changes to the web map already stored in BigTable. This includes a kind of framework that sits atop BigTable, and Lipkovitz compares it to old-school database programming and the use of “database triggers.”

but most importantly

In 2004, Google published research papers on GFS and MapReduce that became the basis for the open source Hadoop platform now used by Yahoo!, Facebook, and — yes — Microsoft. But as Google moves beyond GFS and MapReduce, Lipokovitz stresses that he is “not claiming that the rest of the world is behind us.”

But oh no!

“We’re in business of making searches useful,” he says. “We’re not in the business of selling infrastructure

But I say why not- Search is good and advertising is okay

There is more (not evil) money in infrastructure (of big data) as there is in advertising. But the advertising guys disagree

Open Source Business Intelligence: Pentaho and Jaspersoft

Here are two products that are used widely for Business Intelligence_ They are open source and both have free preview.

Jaspersoft-For the Enterprise version click on the screenshot while for the free community version you can go to

http://jasperforge.org/projects/jasperserver

Interestingly (and not surprisingly) Revolution Analytics is teaming up with Jaspersoft to use R for reporting along with the Jaspersoft BI stack.

ADVANCED ANALYTICS ON DEMAND IN APPLICATIONS, IN DASHBOARDS, AND ON THE WEB

FREE WEBINAR WEDNESDAY, SEPTEMBER 22ND @9AM PACIFIC

DEPLOYING R: ADVANCED ANALYTICS ON DEMAND IN APPLICATIONS, IN DASHBOARDS, AND ON THE WEB

A JOINT WEBINAR FROM REVOLUTION ANALYTICS AND JASPERSOFT

Date: Wednesday, September 22, 2010
Time: 9:00am PDT (12:00pm EDT; 4:00pm GMT)
Presenters: David Smith, Vice President of Marketing, Revolution Analytics
Andrew Lampitt, Senior Director of Technology Alliances, Jaspersoft
Matthew Dahlman, Business Development Engineer, Jaspersoft
Registration: Click here to register now!

R is a popular and powerful system for creating custom data analysis, statistical models, and data visualizations. But how can you make the results of these R-based computations easily accessible to others? A PhD statistician could use R directly to run the forecasting model on the latest sales data, and email a report on request, but then the process is just going to have to be repeated again next month, even if the model hasn’t changed. Wouldn’t it be better to empower the Sales manager to run the model on demand from within the BI application she already uses—daily, even!—and free up the statistician to build newer, better models for others?

In this webinar, David Smith (VP of Marketing, Revolution Analytics) will introduce the new “RevoDeployR” Web Services framework for Revolution R Enterprise, which is designed to make it easy to integrate dynamic R-based computations into applications for business users. RevoDeployR empowers data analysts working in R to publish R scripts to a server-based installation of Revolution R Enterprise. Application developers can then use the RevoDeployR Web Services API to securely and scalably integrate the results of these scripts into any application, without needing to learn the R language. With RevoDeployR, authorized users of hosted or cloud-based interactive Web applications, desktop applications such as Microsoft Excel, and BI applications like Jaspersoft can all benefit from on-demand analytics and visualizations developed by expert R users.

To demonstrate the power of deploying R-based computations to business users, Andrew Lampitt will introduce Jaspersoft commercial open source business intelligence, the world’s most widely used BI software. In a live demonstration, Matt Dahlman will show how to supercharge the BI process by combining Jaspersoft and Revolution R Enterprise, giving business users on-demand access to advanced forecasts and visualizations developed by expert analysts.

Click here to register for the webinar.

Speaker Biographies:

David Smith is the Vice President of Marketing at Revolution Analytics, the leading commercial provider of software and support for the open source “R” statistical computing language. David is the co-author (with Bill Venables) of the official R manual An Introduction to R. He is also the editor of Revolutions (http://blog.revolutionanalytics.com), the leading blog focused on “R” language, and one of the originating developers of ESS: Emacs Speaks Statistics. You can follow David on Twitter as @revodavid.

Andrew Lampitt is Senior Director of Technology Alliances at Jaspersoft. Andrew is responsible for strategic initiatives and partnerships including cloud business intelligence, advanced analytics, and analytic databases. Prior to Jaspersoft, Andrew held other business positions with Sunopsis (Oracle), Business Objects (SAP), and Sybase (SAP). Andrew earned a BS in engineering from the University of Illinois at Urbana Champaign.

Matthew Dahlman is Jaspersoft’s Business Development Engineer, responsible for technical aspects of technology alliances and regional business development. Matt has held a wide range of technical positions including quality assurance, pre-sales, and technical evangelism with enterprise software companies including Sybase, Netonomy (Comverse), and Sunopsis (Oracle). Matt earned a BA in mathematics from Carleton College in Northfield, Minnesota.


The second widely used BI stack in open source is Pentaho.

You can download it here to evaluate it or click on screenshot to read more at

http://community.pentaho.com/

http://sourceforge.net/projects/pentaho/files/Business%20Intelligence%20Server/

Event: Predictive analytics with R, PMML and ADAPA

From http://www.meetup.com/R-Users/calendar/14405407/

The September meeting is at the Oracle campus. (This is next door to the Oracle towers, so there is plenty of free parking.) The featured talk is from Alex Guazzelli (Vice President – Analytics, Zementis Inc.) who will talk about “Predictive analytics with R, PMML and ADAPA”.

Agenda:
* 6:15 – 7:00 Networking and Pizza (with thanks to Revolution Analytics)
* 7:00 – 8:00 Talk: Predictive analytics with R, PMML and ADAPA
* 8:00 – 8:30 General discussion

Talk overview:

The rule in the past was that whenever a model was built in a particular development environment, it remained in that environment forever, unless it was manually recoded to work somewhere else. This rule has been shattered with the advent of PMML (Predictive Modeling Markup Language). By providing a uniform standard to represent predictive models, PMML allows for the exchange of predictive solutions between different applications and various vendors.

Once exported as PMML files, models are readily available for deployment into an execution engine for scoring or classification. ADAPA is one example of such an engine. It takes in models expressed in PMML and transforms them into web-services. Models can be executed either remotely by using web-services calls, or via a web console. Users can also use an Excel add-in to score data from inside Excel using models built in R.

R models have been exported into PMML and uploaded in ADAPA for many different purposes. Use cases where clients have used the flexibility of R to develop and the PMML standard combined with ADAPA to deploy range from financial applications (e.g., risk, compliance, fraud) to energy applications for the smart grid. The ability to easily transition solutions developed in R to the operational IT production environment helps eliminate the traditional limitations of R, e.g. performance for high volume or real-time transactional systems and memory constraints associated with large data sets.

Speaker Bio:

Dr. Alex Guazzelli has co-authored the first book on PMML, the Predictive Model Markup Language which is the de facto standard used to represent predictive models. The book, entitled PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics, is available on Amazon.com. As the Vice President of Analytics at Zementis, Inc., Dr. Guazzelli is responsible for developing core technology and analytical solutions under ADAPA, a PMML-based predictive decisioning platform that combines predictive analytics and business rules. ADAPA is the first system of its kind to be offered as a service on the cloud.
Prior to joining Zementis, Dr. Guazzelli was involved in not only building but also deploying predictive solutions for large financial and telecommunication institutions around the globe. In academia, Dr. Guazzelli worked with data mining, neural networks, expert systems and brain theory. His work in brain theory and computational neuroscience has appeared in many peer reviewed publications. At Zementis, Dr. Guazzelli and his team have been involved in a myriad of modeling projects for financial, health-care, gaming, chemical, and manufacturing industries.

Dr. Guazzelli holds a Ph.D. in Computer Science from the University of Southern California and a M.S and B.S. in Computer Science from the Federal University of Rio Grande do Sul, Brazil.

Dryad- Microsoft's answer to MR

While reading across the internet I came across Microsoft’s version to MapReduce called Dryad- which has been around for some time, but has not generated quite the buzz that Hadoop or MapReduce are doing.

http://research.microsoft.com/en-us/projects/dryadlinq/

DryadLINQ

DryadLINQ is a simple, powerful, and elegant programming environment for writing large-scale data parallel applications running on large PC clusters.

Overview

New! An academic release of Dryad/DryadLINQ is now available for public download.

The goal of DryadLINQ is to make distributed computing on large compute cluster simple enough for every programmers. DryadLINQ combines two important pieces of Microsoft technology: the Dryad distributed execution engine and the .NET Language Integrated Query (LINQ).

Dryad provides reliable, distributed computing on thousands of servers for large-scale data parallel applications. LINQ enables developers to write and debug their applications in a SQL-like query language, relying on the entire .NET library and using Visual Studio.

DryadLINQ translates LINQ programs into distributed Dryad computations:

  • C# and LINQ data objects become distributed partitioned files.
  • LINQ queries become distributed Dryad jobs.
  • C# methods become code running on the vertices of a Dryad job.

DryadLINQ has the following features:

  • Declarative programming: computations are expressed in a high-level language similar to SQL
  • Automatic parallelization: from sequential declarative code the DryadLINQ compiler generates highly parallel query plans spanning large computer clusters. For exploiting multi-core parallelism on each machine DryadLINQ relies on the PLINQ parallelization framework.
  • Integration with Visual Studio: programmers in DryadLINQ take advantage of the comprehensive VS set of tools: Intellisense, code refactoring, integrated debugging, build, source code management.
  • Integration with .Net: all .Net libraries, including Visual Basic, and dynamic languages are available.
  • and
  • Conciseness: the following line of code is a complete implementation of the Map-Reduce computation framework in DryadLINQ:
      • public static IQueryable<R>
      MapReduce<S,M,K,R>(this IQueryable<S> source,
      Expression<Func<S,IEnumerable<M>>> mapper,
      Expression<Func<M,K>> keySelector,
      Expression<Func<K,IEnumerable<M>,R>> reducer)
      {
      return source.SelectMany(mapper).GroupBy(keySelector, reducer);
      }

    and http://research.microsoft.com/en-us/projects/dryad/

    Dryad

    The Dryad Project is investigating programming models for writing parallel and distributed programs to scale from a small cluster to a large data-center.

    Overview

    New! An academic release of DryadLINQ is now available for public download.

    Dryad is an infrastructure which allows a programmer to use the resources of a computer cluster or a data center for running data-parallel programs. A Dryad programmer can use thousands of machines, each of them with multiple processors or cores, without knowing anything about concurrent programming.

    The Structure of Dryad Jobs

    A Dryad programmer writes several sequential programs and connects them using one-way channels. The computation is structured as a directed graph: programs are graph vertices, while the channels are graph edges. A Dryad job is a graph generator which can synthesize any directed acyclic graph. These graphs can even change during execution, in response to important events in the computation.

    Dryad is quite expressive. It completely subsumes other computation frameworks, such as Google’s map-reduce, or the relational algebra. Moreover, Dryad handles job creation and management, resource management, job monitoring and visualization, fault tolerance, re-execution, scheduling, and accounting.

    The Dryad Software Stack

    As a proof of Dryad’s versatility, a rich software ecosystem has been built on top Dryad:

    • SSIS on Dryad executes many instances of SQL server, each in a separate Dryad vertex, taking advantage of Dryad’s fault tolerance and scheduling. This system is currently deployed in a live production system as part of one of Microsoft’s AdCenter log processing pipelines.
    • DryadLINQ generates Dryad computations from the LINQ Language-Integrated Query extensions to C#.
    • The distributed shell is a generalization of the pipe concept from the Unix shell in three ways. If Unix pipes allow the construction of one-dimensional (1-D) process structures, the distributed shell allows the programmer to build 2-D structures in a scripting language. The distributed shell generalizes Unix pipes in three ways:
      1. It allows processes to easily connect multiple file descriptors of each process — hence the 2-D aspect.
      2. It allows the construction of pipes spanning multiple machines, across a cluster.
      3. It virtualizes the pipelines, allowing the execution of pipelines with many more processes than available machines, by time-multiplexing processors and buffering results.
    • Several languages are compiled to distributed shell processes. PSQL is an early version, recently replaced with Scope.

    Publications

    Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks
    Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly
    European Conference on Computer Systems (EuroSys), Lisbon, Portugal, March 21-23, 2007

    Video of a presentation on Dryad at the Google Campus, given by Michael Isard, Nov 1, 2007.

    Also interesting to read-

    Why does Dryad use a DAG?

    he basic computational model we decided to adopt for Dryad is the directed-acyclic graph (DAG). Each node in the graph is a computation, and each edge in the graph is a stream of data traveling in the direction of the edge. The amount of data on any given edge is assumed to be finite, the computations are assumed to be deterministic, and the inputs are assumed to be immutable. This isn’t by any means a new way of structuring a distributed computation (for example Condor had DAGMan long before Dryad came along), but it seemed like a sweet spot in the design space given our other constraints.

    So, why is this a sweet spot? A DAG is very convenient because it induces an ordering on the nodes in the graph. That makes it easy to design scheduling policies, since you can define a node to be ready when its inputs are available, and at any time you can choose to schedule as many ready nodes as you like in whatever order you like, and as long as you always have at least one scheduled you will continue to make progress and never deadlock. It also makes fault-tolerance easy, since given our determinism and immutability assumptions you can backtrack as far as you want in the DAG and re-execute as many nodes as you like to regenerate intermediate data that has been lost or is unavailable due to cluster failures.

    from

    http://blogs.msdn.com/b/dryad/archive/2010/07/23/why-does-dryad-use-a-dag.aspx

      Mapreduce Book

      Here is a new book on learning MapReduce and it has a free downloadable version as well.

      Data-Intensive Text Processing with MapReduce

      Jimmy Lin and Chris Dyer

      ABSTRACT

      Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader “think in MapReduce”, but also discusses limitations of the programming model as well.

      You can download the book here

      This book is part of the Morgan & Claypool Synthesis Lectures on Human Language Technologies. If you’re at a university, your institution may already subscribe to the series, in which case you can access the electronic version directly without cost (see this page for a list of institutional subscribers). Otherwise, to purchase:

      Quite explicitly, this book focuses on MapReduce algorithm design, not Hadoop programming. Tom White’s Hadoop: The Definitive Guide is a great resource for learning Hadoop.

      Want to be notified of updates? Interested in MapReduce algorithm design? Follow @lintool on Twitter here!

      Q&A with David Smith, Revolution Analytics.

      Here’s a group of questions and answers that David Smith of Revolution Analytics was kind enough to answer post the launch of the new R Package which integrates Hadoop and R-                         RevoScaleR

      Ajay- How does RevoScaleR work from a technical viewpoint in terms of Hadoop integration?

      David-The point isn’t that there’s a deep technical integration between Revolution R and Hadoop, rather that we see them as complementary (not competing) technologies. Hadoop is amazing at reliably (if slowly) processing huge volumes of distributed data; the RevoScaleR package complements Hadoop by providing statistical algorithms to analyze the data processed by Hadoop. The analogy I use is to compare a freight train with a race car: use Hadoop to slog through a distributed data set and use Map/Reduce to output an aggregated, rectangular data file; then use RevoScaleR to perform statistical analysis on the processed data (and use the speed of RevolScaleR to iterate through many model options to find the best one).

      Ajay- How is it different from MapReduce and R Hipe– existing R Hadoop packages?
      David- They’re complementary. In fact, we’ll be publishing a white paper soon by Saptarshi Guha, author of the Rhipe R/Hadoop integration, showing how he uses Hadoop to process vast volumes of packet-level VOIP data to identify call time/duration from the packets, and then do a regression on the table of calls using RevoScaleR. There’s a little more detail in this blog post: http://blog.revolutionanalytics.com/2010/08/announcing-big-data-for-revolution-r.html
      Ajay- Is it going to be proprietary, free or licensable (open source)?
      David- RevoScaleR is a proprietary package, available to paid subscribers (or free to academics) with Revolution R Enterprise. (If you haven’t seen it, you might be interested in this Q&A I did with Matt Shotwell: http://biostatmatt.com/archives/533 )
      Ajay- Any existing client case studies for Terabyte level analysis using R.
      David- The VOIP example above gets close, but most of the case studies we’ve seen in beta testing have been in the 10’s to 100’s of Gb range. We’ve tested RevoScaleR on larger data sets internally, but we’re eager to hear about real-life use cases in the terabyte range.
      Ajay- How can I use RevoScaleR on my dual chip Win Intel laptop for say 5 gb of data.
      David- One of the great things about RevoScaleR is that it’s designed to work on commodity hardware like a dual-core laptop. You won’t be constrained by the limited RAM available, and the parallel processing algorithms will make use of all cores available to speed up the analysis even further. There’s an example in this white paper (http://info.revolutionanalytics.com/bigdata.html) of doing linear regression on 13Gb of data on a simple dual-core laptop in less than 5 seconds.
      AJ-Thanks to David Smith, for this fast response and wishing him, Saptarshi Guha Dr Norman Nie and the rest of guys at Revolution Analytics a congratulations for this new product launch.