Tag: Time

Trrrouble in land of R…and Open Source Suggestions

Recently some comments by Ross Ihake , founder of R Statistical Software on Revolution Analytics, leading commercial vendor of R….. came to my attention-

http://www.stat.auckland.ac.nz/mail/archive/r-downunder/2010-May/000529.html

[R-downunder] Article on Revolution Analytics

Ross Ihaka ihaka at stat.auckland.ac.nz
Mon May 10 14:27:42 NZST 2010

On 09/05/10 09:52, Murray Jorgensen wrote:
> Perhaps of interest:
>
> http://www.theregister.co.uk/2010/05/06/revolution_commercial_r/

Please note that R is "free software" not "open source".  These guys
are selling a GPLed work without disclosing the source to their part
of the work. I have complained to them and so far they have given me
the brush off. I am now considering my options.

Don't support these guys by buying their product. The are not feeding
back to the rights holders (the University of Auckland and I are rights
holders and they didn't even have the courtesy to contact us).

--
Ross Ihaka                         Email:  ihaka at stat.auckland.ac.nz
Department of Statistics           Phone:  (64-9) 373-7599 x 85054
University of Auckland             Fax:    (64-9) 373-7018
Private Bag 92019, Auckland
New Zealand
and from http://www.theregister.co.uk/2010/05/06/revolution_commercial_r/
Open source purists probably won't be all too happy to learn that Revolution is going to be employing an "open core" strategy, which means the core R programs will remain open source and be given tech support under a license model, but the key add-ons that make R more scalable will be closed source and sold under a separate license fee. Because most of those 2,500 add-ons for R were built by academics and Revolution wants to supplant SPSS and SAS as the tools used by students, Revolution will be giving the full single-user version of the R Enterprise stack away for free to academics. 
Conclusion-
So one co-founder of R is advocating not to buy from Revolution Analytics , which has the other co-founder of R, Gentleman on its board. 
Source- http://www.revolutionanalytics.com/aboutus/leadership.php

2) If Revolution Analytics is using 2500 packages for free but insisting on getting paid AND closing source of it’s packages (which is a technical point- how exactly can you prevent source code of a R package from being seen)

Maybe there can be a PACKAGE marketplace just like Android Apps, Facebook Apps, and Salesforce.com Apps – so atleast some of the thousands of R package developers can earn – sorry but email lists do not pay mortgages and no one is disputing the NEED for commercializing R or rewarding developers.

Though Barr created SAS, he gave up control to Goodnight and Sall https://decisionstats.wordpress.com/2010/06/02/sas-early-days/

and Goodnight and Sall do pay their developers well- to the envy of not so well paid counterparts.

3) I really liked the innovation of Revolution Analytics RevoScalar, and I wish that the default R dataset be converted to XDF dataset so that it basically kills

off the R criticism of being slow on bigger datasets. But I also realize the need for creating an analytics marketplace for R developers and R students- so academic version of R being free and Revolution R being paid seems like a trade off.

Note- You can still get a job faster as a stats student if you mention SAS and not R as a statistical skill- not all stats students go into academics.

4) There can be more elegant ways of handling this than calling for ignoring each other as REVOLUTION and Ihake seem to be doing to each other.

I can almost hear people in Cary, NC chuckling at Norman Nie, long time SPSS opponent and now REVOLUTION CEO, and his antagonizing R’s academicians within 1 year of taking over- so I hope this ends well for all. The road to hell is paved with good intentions- so if REVOLUTION can share some source code with say R Core members (even Microsoft shares source code with partners)- and R Core and Revolution agree on a licensing royalty from each other, they can actually speed up R package creation rather than allow this 2 decade effort to end up like S and S plus and TIBCO did.

Maybe Richard Stallman can help-or maybe Ihaka has a better sense of where things will go down in a couple of years-he must know something-he invented it, didnt he

On 09/05/10 09:52, Murray Jorgensen wrote:
> Perhaps of interest:
>
> http://www.theregister.co.uk/2010/05/06/revolution_commercial_r/

Please note that R is "free software" not "open source".  These guys
are selling a GPLed work without disclosing the source to their part
of the work. I have complained to them and so far they have given me
the brush off. I am now considering my options.

Don't support these guys by buying their product. The are not feeding
back to the rights holders (the University of Auckland and I are rights
holders and they didn't even have the courtesy to contact us).

--
Ross Ihaka                         Email:  ihaka at stat.auckland.ac.nz
Department of Statistics           Phone:  (64-9) 373-7599 x 85054
University of Auckland             Fax:    (64-9) 373-7018
Private Bag 92019, Auckland
New Zealand

Google AppInventor -Android and Business Intelligence

Here is a great new tool for techies to start creating Android Apps right away- even if you have no knowledge of the platform. Of course there are existing great number of apps- including my favorite Android Data Mining App in R – called AnalyticDroid http://analyticdroid.togaware.com/

Basically it calls the Rattle (R Analytical Tool To Learn Easily) Data Mining GUI -enabling data mining from an Android Mobile using remote computing.

I dont know if any other statistical application is available on Android Mobiles- though SAS did have a presentation on using SAS on IPhone

http://www.wuss.org/proceedings09/09WUSSProceedings/papers/dpr/DPR-Truong.pdf



SAS Mobile -Iphone App

All you need to do is go to http://appinventor.googlelabs.com/about/index.html and request access (yes there is a 2 week approval waiting line)

Because App Inventor provides access to a GPS-location sensor, you can build apps that know where you are. You can build an app to help you remember where you parked your car, an app that shows the location of your friends or colleagues at a concert or conference, or your own custom tour app of your school, workplace, or a museum.
You can write apps that use the phone features of an Android phone. You can write an app that periodically texts “missing you” to your loved ones, or an app “No Text While Driving” that responds to all texts automatically with “sorry, I’m driving and will contact you later”. You can even have the app read the incoming texts aloud to you (though this might lure you into responding).
App Inventor provides a way for you to communicate with the web. If you know how to write web apps, you can use App Inventor to write Android apps that talk to your favorite web sites, such as Amazon and Twitter.

Here is a not so statistical Android App I am trying to create called Hang-Out

using the current GPS location of your phone to find nearest Pub, Movie or Diner and catch Bus- Train based on your location city, the GPS and time of request and schedule of those cities public transport- very much WIP

KDNuggets Poll on SAS: Churn in Analytics Users

Here are the some surprising results from the Bible of all Data Miners , KDNuggets.com with some interesting comments about SAS being the Microsoft of analytics.

I believe technically advanced users will probably want to try out R before going in for a commercial license from Revolution Analytics as it is free to try out. Also WPS offers a one month free preview for its software- the latest release of it competes with SAS/Stat and SAS/Access, SAS/Graph and Base SAS- so anyone having these installations on a server would be interested to atleast test it for free. Also WPS would be interested in increasing engines (like they have for Oracle and Teradata).

One very crucial difference for SAS is it’s ability to pull in data from almost all data formats- so if you are using SAS/Connect to remote submit code- then you may not be able to switch soon.

Also the more license heavy customers are not the kind of cutomers who have lots of data in their local desktops but is usually pulled and then crunched before analysed. R has recently made some strides with the RevoScaler package from Revolution Analytics but it’s effectiveness would be tested and tried in the coming months- it seems like a great step in the right direction.

For SAS, the feedback should be a call to improve their product bundling – some of which can feel like over selling at times- but they have been fighting off challenges since past 4 decades and have the pockets and intention to sustain market share battles including discounts ( for repeat customers SAS can be much cheaper than say a first time user of WPS or R)

http://teamwpc.co.uk/home

This really should come as a surprise to some people. You can see the comments on WPS and R at the site itself. Interesting stufff and we can see after say 1 year to see how many actually DID switch.

http://www.kdnuggets.com/polls/2010/switching-from-sas-to-wps.html

Interview Stephanie McReynolds Director Product Marketing, AsterData

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Biography-

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

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 Analytics Apps- AsterData's Developer Express Plugin

      AsterData continues to wow with it’s efforts on bridging MapReduce and Analytics, with it’s new Developer Express plug-in for Eclipse. As any Eclipse user knows, that greatly improves ability to write code or develop ( similar to creating Android apps if you have tried to). I did my winter internship at AsterData last December last year in San Carlos, and its an amazing place with giga-level bright people.

      Here are some details ( Note I plan to play a bit more on the plugin on my currently downUbuntu on this and let you know)

      http://marketplace.eclipse.org/content/aster-data-developer-express-plug-eclipse

      Aster Data Developer Express provides an integrated set of tools for development of SQL and MapReduce analytics for Aster Data nCluster, a massively parallel database with an integrated analytics engine.

      The Aster Data Developer Express plug-in for Eclipse enables developers to easily create new analytic application projects with the help of an intuitive set of wizards, immediately test their applications on their desktop, and push down their applications into the nCluster database with a single click.

      Using Developer Express, analysts can significantly reduce the complexity and time needed to create advanced analytic applications so that they can more rapidly deliver deeper and richer analytic insights from their data.

      and from the Press Release

      Now, any developer or analyst that is familiar with the Java programming language can complete a rich analytic application in under an hour using the simple yet powerful Aster Data Developer Express environment in Eclipse. Aster Data Developer Express delivers both rapid development and local testing of advanced analytic applications for any project, regardless of size.

      The free, downloadable Aster Data Developer Express IDE now brings the power of SQL-MapReduce to any organization that is looking to build richer analytic applications that can leverage massive data volumes. Much of the MapReduce coding, including programming concepts like parallelization and distributed data analysis, is addressed by the IDE without the developer or analyst needing to have expertise in these areas. This simplification makes it much easier for developers to be successful quickly and eliminates the need for them to have any deep knowledge of the MapReduce parallel processing framework. Google first published MapReduce in 2004 for parallel processing of big data sets. Aster Data has coupled SQL with MapReduce and brought SQL-MapReduce to market, making it significantly easier for any organization to leverage the power of MapReduce. The Aster Developer Express IDE simplifies application development even further with an intuitive point-and-click development environment that speeds development of rich analytic applications. Applications can be validated locally on the desktop or ultimately within Aster Data nCluster, a massive parallel processing (MPP) database with a fully integrated analytics engine that is powered by MapReduce—known as a data-analytics server.

      Rich analytic applications that can be easily built with Aster Data’s downloadable IDE include:

      Iterative Analytics: Uncovering critical business patterns in your data requires hypothesis-driven, iterative analysis.  This class of applications is defined by the exploratory navigation of massive volumes of data in a top-down, deductive manner.  Aster Data’s IDE makes this easy to develop and to validate the algorithms and functions required to deliver these advanced analytic applications.

      Prediction and Optimization: For this class of applications, the process is inductive. Rather than starting with a hypothesis, developers and analysts can easily build analytic applications that discover the trends, patterns, and outliers in data sets.  Examples include propensity to churn in telecommunications, proactive product and service recommendations in retail, and pricing and retention strategies in financial services.

      Ad Hoc Analysis: Examples of ad hoc analysis that can be performed includes social network analysis, advanced click stream analysis, graph analysis, cluster analysis, and a wide variety of mathematical, trigonometry, and statistical functions.

      “Aster Data’s IDE and SQL-MapReduce significantly eases development of advanced analytic applications on big data. We have now built over 350 analytic functions in SQL-MapReduce on Aster Data nCluster that are available for customers to purchase,” said Partha Sen, CEO and Founder of Fuzzy Logix. “Aster Data’s implementation of MapReduce with SQL-MapReduce goes beyond the capabilities of general analytic development APIs and provides us with the excellent control and flexibility needed to implement even the most complex analytic algorithms.”

      Richer analytics on big data volumes is the new competitive frontier. Organizations have always generated reports to guide their decision-making. Although reports are important, they are historical sets of information generally arranged around predefined metrics and generated on a periodic basis.

      Advanced analytics begins where reporting leaves off. Reporting often answers historical questions such as “what happened?” However, analytics addresses “why it happened” and, increasingly, “what will happen next?” To that end, solutions like Aster Data Developer Express ease the development of powerful ad hoc, predictive analytics and enables analysts to quickly and deeply explore terabytes to petabytes of data.
      “We are in the midst of a new age in analytics. Organizations today can harness the power of big data regardless of scale or complexity”, said Don Watters, Chief Data Architect for MySpace. “Solutions like the Aster Data Developer Express visual development environment make it even easier by enabling us to automate aspects of development that currently take days, allowing us to build rich analytic applications significantly faster. Making Developer Express openly available for download opens the power of MapReduce to a broader audience, making big data analytics much faster and easier than ever before.”

      “Our delivery of SQL coupled with MapReduce has clearly made it easier for customers to build highly advanced analytic applications that leverage the power of MapReduce. The visual IDE, Aster Data Developer Express, introduced earlier this year, made application development even easier and the great response we have had to it has driven us to make this open and freely available to any organization looking to build rich analytic applications,” said Tasso Argyros, Founder and CTO, Aster Data. “We are excited about today’s announcement as it allows companies of all sizes who need richer analytics to easily build powerful analytic applications and experience the power of MapReduce without having to learn any new skills.”

      You can have a look here at http://www.asterdata.com/download_developer_express/

      Indian Offshoring IPOs dismal performance

      Using Yahoo Finance, I plotted the past three years stock price of Indian Offshores  (Genpact, Wns, Exl) and in comparison with Indian Software companies (Infosys, Wipro, TCS, Sify) and market index.

      The following insights emerge-

      1) Indian Software companies have constantly created wealth.

      2) Indian Offshoring companies have constantly lost market value – perhaps because they were able to dump IPO prices at much higher prices by creating hype.

      3) You are much better off investing in Indian stock market or a blue chip Indian software company than take part in an Indian offshorers IPO.

      4) SIFY lost most value and its founder CEO is now in jail for fraud. The fraud was he added phantom employees, and phantom revenue to boost balance sheet. Auditors from PwC (were jailed) included a board member of Indian Chartered Accountants and Satyam (SIFY) had won awards for corporate governance. It makes sense to do rigorous cash flow due diligence this side of the pond.

      5) I won no stock in any of this companies  (not surprisingly) but do have a portfolio of mutual funds (index).

      So the next time you are promised the moon by an Indian IPO- KPO, remember to do the math 😉