Tag: automate

Webscraping using iMacros

The noted Diamonds dataset in the ggplot2 package of R is actually culled from the website http://www.diamondse.info/diamond-prices.asp

However it has ~55000 diamonds, while the whole Diamonds search engine has almost ten times that number. Using iMacros – a Google Chrome Plugin, we can scrape that data (or almost any data). The iMacros chrome plugin is available at  https://chrome.google.com/webstore/detail/cplklnmnlbnpmjogncfgfijoopmnlemp while notes on coding are at http://wiki.imacros.net

Imacros makes coding as easy as recording macro and the code is automatcially generated for whatever actions you do. You can set parameters to extract only specific parts of the website, and code can be run into a loop (of 9999 times!)

Here is the iMacros code-Note you need to navigate to the web site http://www.diamondse.info/diamond-prices.asp before running it

VERSION BUILD=5100505 RECORDER=CR
FRAME F=1
SET !EXTRACT_TEST_POPUP NO
SET !ERRORIGNORE YES
TAG POS=6 TYPE=TABLE ATTR=TXT:* EXTRACT=TXT
TAG POS=1 TYPE=DIV ATTR=CLASS:paginate_enabled_next
SAVEAS TYPE=EXTRACT FOLDER=* FILE=test+3

 

 

 

 

 

 

 

 

 

and voila- all the diamonds you need to analyze!

The returning data can be read using the standard delimiter data munging in the language of SAS or R.

More on IMacros from

https://chrome.google.com/webstore/detail/cplklnmnlbnpmjogncfgfijoopmnlemp/details

Description

Automate your web browser. Record and replay repetitious work

If you encounter any problems with iMacros for Chrome, please let us know in our Chrome user forum at http://forum.iopus.com/viewforum.php?f=21

Our forum is also the best place for new feature suggestions :-)
----

iMacros was designed to automate the most repetitious tasks on the web. If there’s an activity you have to do repeatedly, just record it in iMacros. The next time you need to do it, the entire macro will run at the click of a button! With iMacros, you can quickly and easily fill out web forms, remember passwords, create a webmail notifier, and more. You can keep the macros on your computer for your own use, use them within bookmark sync / Xmarks or share them with others by embedding them on your homepage, blog, company Intranet or any social bookmarking service as bookmarklet. The uses are limited only by your imagination!

Popular uses are as web macro recorder, form filler on steroids and highly-secure password manager (256-bit AES encryption).


Interview Michal Kosinski , Concerto Web Based App using #Rstats

Here is an interview with Michal Kosinski , leader of the team that has created Concerto – a web based application using R. What is Concerto? As per http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm

Concerto is a web based, adaptive testing platform for creating and running rich, dynamic tests. It combines the flexibility of HTML presentation with the computing power of the R language, and the safety and performance of the MySQL database. It’s totally free for commercial and academic use, and it’s open source

Ajay-  Describe your career in science from high school to this point. What are the various stats platforms you have trained on- and what do you think about their comparative advantages and disadvantages?  

Michal- I started with maths, but quickly realized that I prefer social sciences – thus after one year, I switched to a psychology major and obtained my MSc in Social Psychology with a specialization in Consumer Behaviour. At that time I was mostly using SPSS – as it was the only statistical package that was taught to students in my department. Also, it was not too bad for small samples and the rather basic analyses I was performing at that time.

 

My more recent research performed during my Mphil course in Psychometrics at Cambridge University followed by my current PhD project in social networks and research work at Microsoft Research, requires significantly more powerful tools. Initially, I tried to squeeze as much as possible from SPSS/PASW by mastering the syntax language. SPSS was all I knew, though I reached its limits pretty quickly and was forced to switch to R. It was a pretty dreary experience at the start, switching from an unwieldy but familiar environment into an unwelcoming command line interface, but I’ve quickly realized how empowering and convenient this tool was.

 

I believe that a course in R should be obligatory for all students that are likely to come close to any data analysis in their careers. It is really empowering – once you got the basics you have the potential to use virtually any method there is, and automate most tasks related to analysing and processing data. It is also free and open-source – so you can use it wherever you work. Finally, it enables you to quickly and seamlessly migrate to other powerful environments such as Matlab, C, or Python.

Ajay- What was the motivation behind building Concerto?

Michal- We deal with a lot of online projects at the Psychometrics Centre – one of them attracted more than 7 million unique participants. We needed a powerful tool that would allow researchers and practitioners to conveniently build and deliver online tests.

Also, our relationships with the website designers and software engineers that worked on developing our tests were rather difficult. We had trouble successfully explaining our needs, each little change was implemented with a delay and at significant cost. Not to mention the difficulties with embedding some more advanced methods (such as adaptive testing) in our tests.

So we created a tool allowing us, psychometricians, to easily develop psychometric tests from scratch an publish them online. And all this without having to hire software developers.

Ajay -Why did you choose R as the background for Concerto? What other languages and platforms did you consider. Apart from Concerto, how else do you utilize R in your center, department and University?

Michal- R was a natural choice as it is open-source, free, and nicely integrates with a server environment. Also, we believe that it is becoming a universal statistical and data processing language in science. We put increasing emphasis on teaching R to our students and we hope that it will replace SPSS/PASW as a default statistical tool for social scientists.

Ajay -What all can Concerto do besides a computer adaptive test?

Michal- We did not plan it initially, but Concerto turned out to be extremely flexible. In a nutshell, it is a web interface to R engine with a built-in MySQL database and easy-to-use developer panel. It can be installed on both Windows and Unix systems and used over the network or locally.

Effectively, it can be used to build any kind of web application that requires a powerful and quickly deployable statistical engine. For instance, I envision an easy to use website (that could look a bit like SPSS) allowing students to analyse their data using a web browser alone (learning the underlying R code simultaneously). Also, the authors of R libraries (or anyone else) could use Concerto to build user-friendly web interfaces to their methods.

Finally, Concerto can be conveniently used to build simple non-adaptive tests and questionnaires. It might seem to be slightly less intuitive at first than popular questionnaire services (such us my favourite Survey Monkey), but has virtually unlimited flexibility when it comes to item format, test flow, feedback options, etc. Also, it’s free.

Ajay- How do you see the cloud computing paradigm growing? Do you think browser based computation is here to stay?

Michal – I believe that cloud infrastructure is the future. Dynamically sharing computational and network resources between online service providers has a great competitive advantage over traditional strategies to deal with network infrastructure. I am sure the security concerns will be resolved soon, finishing the transformation of the network infrastructure as we know it. On the other hand, however, I do not see a reason why client-side (or browser) processing of the information should cease to exist – I rather think that the border between the cloud and personal or local computer will continually dissolve.

About

Michal Kosinski is Director of Operations for The Psychometrics Centre and Leader of the e-Psychometrics Unit. He is also a research advisor to the Online Services and Advertising group at the Microsoft Research Cambridge, and a visiting lecturer at the Department of Mathematics in the University of Namur, Belgium. You can read more about him at http://www.michalkosinski.com/

You can read more about Concerto at http://code.google.com/p/concerto-platform/ and http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm

Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

Interview Mike Boyarski Jaspersoft

Here is an interview with Mike Boyarski , Director Product Marketing at Jaspersoft

.

 

the largest BI community with over 14 million downloads, nearly 230,000 registered members, representing over 175,000 production deployments, 14,000 customers, across 100 countries.

Ajay- Describe your career in science from Biology to marketing great software.
Mike- I studied Biology with the assumption I’d pursue a career in medicine. It took about 2 weeks during an internship at a Los Angeles hospital to determine I should do something else.  I enjoyed learning about life science, but the whole health care environment was not for me.  I was initially introduced to enterprise-level software while at Applied Materials within their Microcontamination group.  I was able to assist with an internal application used to collect contamination data.  I later joined Oracle to work on an Oracle Forms application used to automate the production of software kits (back when documentation and CDs had to be physically shipped to recognize revenue). This gave me hands on experience with Oracle 7, web application servers, and the software development process.
I then transitioned to product management for various products including application servers, software appliances, and Oracle’s first generation SaaS based software infrastructure. In 2006, with the Siebel and PeopleSoft acquisitions underway, I moved on to Ingres to help re-invigorate their solid yet antiquated technology. This introduced me to commercial open source software and the broader Business Intelligence market.  From Ingres I joined Jaspersoft, one of the first and most popular open source Business Intelligence vendors, serving as head of product marketing since mid 2009.
Ajay- Describe some of the new features in Jaspersoft 4.1 that help differentiate it from the rest of the crowd. What are the exciting product features we can expect from Jaspersoft down the next couple of years.
Mike- Jaspersoft 4.1 was an exciting release for our customers because we were able to extend the latest UI advancements in our ad hoc report designer to the data analysis environment. Now customers can use a unified intuitive web-based interface to perform several powerful and interactive analytic functions across any data source, whether its relational, non-relational, or a Big Data source.
 The reality is that most (roughly 70%) of todays BI adoption is in the form of reports and dashboards. These tools are used to drive and measure an organizations business, however, data analysis presents the most strategic opportunity for companies because it can identify new opportunities, efficiencies, and competitive differentiation.  As more data comes online, the difference between those companies that are successful and those that are not will likely be attributed to their ability to harness data analysis techniques to drive and improve business performance. Thus, with Jaspersoft 4.1, and our improved ad hoc reporting and analysis UI we can effectively address a broader set of BI requirements for organizations of all sizes.
Ajay-  What do you think is a good metric to measure influence of an open source software product – is it revenue or is it number of downloads or number of users. How does Jaspersoft do by these counts.
Mike- History has shown that open source software is successful as a “bottoms up” disrupter within IT or the developer market.  Today, many new software projects and startup ventures are birthed on open source software, often initiated with little to no budget. As the organization achieves success with a particular project, the next initiative tends to be larger and more strategic, often displacing what was historically solved with a proprietary solution. These larger deployments strengthen the technology over time.
Thus, the more proven and battle tested an open source solution is, often measured via downloads, deployments, community size, and community activity, usually equates to its long term success. Linux, Tomcat, and MySQL have plenty of statistics to model this lifecycle. This model is no different for open source BI.
The success to date of Jaspersoft is directly tied to its solid proven technology and the vibrancy of the community.  We proudly and openly claim to have the largest BI community with over 14 million downloads, nearly 230,000 registered members, representing over 175,000 production deployments, 14,000 customers, across 100 countries.  Every day, 30,000 developers are using Jaspersoft to build BI applications.  Behind Excel, its hard to imagine a more widely used BI tool in the market.  Jaspersoft could not reach these kind of numbers with crippled or poorly architected software.
Ajay- What are your plans for leveraging cloud computing, mobile and tablet platforms and for making Jaspersoft more easy and global  to use.

SAS/Blades/Servers/ GPU Benchmarks

Just checked out cool new series from NVidia servers.

Now though SAS Inc/ Jim Goodnight thinks HP Blade Servers are the cool thing- the GPU takes hardware high performance computing to another level. It would be interesting to see GPU based cloud computers as well – say for the on Demand SAS (free for academics and students) but which has had some complaints of being slow.

See this for SAS and Blade Servers-

http://www.sas.com/success/ncsu_analytics.html

To give users hands-on experience, the program is underpinned by a virtual computing lab (VCL), a remote access service that allows users to reserve a computer configured with a desired set of applications and operating system and then access that computer over the Internet. The lab is powered by an IBM BladeCenter infrastructure, which includes more than 500 blade servers, distributed between two locations. The assignment of the blade servers can be changed to meet shifts in the balance of demand among the various groups of users. Laura Ladrie, MSA Classroom Coordinator and Technical Support Specialist, says, “The virtual computing lab chose IBM hardware because of its quality, reliability and performance. IBM hardware is also energy efficient and lends itself well to high performance/low overhead computing.

Thats interesting since IBM now competes (as owner of SPSS) and also cooperates with SAS Institute

And

http://www.theaustralian.com.au/australian-it/the-world-according-to-jim-goodnight-blade-switch-slashes-job-times/story-e6frgakx-1225888236107

You’re effectively turbo-charging through deployment of many processors within the blade servers?

Yes. We’ve got machines with 192 blades on them. One of them has 202 or 203 blades. We’re using Hewlett-Packard blades with 12 CP cores on each, so it’s a total 2300 CPU cores doing the computation.

Our idea was to give every one of those cores a little piece of work to do, and we came up with a solution. It involved a very small change to the algorithm we were using, and it’s just incredible how fast we can do things now.

I don’t think of it as a grid, I think of it as essentially one computer. Most people will take a blade and make a grid out of it, where everything’s a separate computer running separate jobs.

We just look at it as one big machine that has memory and processors all over the place, so it’s a totally different concept.

GPU servers can be faster than CPU servers, though , Professor G.




Source-

http://www.nvidia.com/object/preconfigured_clusters.html

TESLA GPU COMPUTING SOLUTIONS FOR DATA CENTERS
Supercharge your cluster with the Tesla family of GPU computing solutions. Deploy 1U systems from NVIDIA or hybrid CPU-GPU servers from OEMs that integrate NVIDIA® Tesla™ GPU computing processors.

When compared to the latest quad-core CPU, Tesla 20-series GPU computing processors deliver equivalent performance at 1/20th the power consumption and 1/10th the cost. Each Tesla GPU features hundreds of parallel CUDA cores and is based on the revolutionary NVIDIA® CUDA™ parallel computing architecture with a rich set of developer tools (compilers, profilers, debuggers) for popular programming languages APIs like C, C++, Fortran, and driver APIs like OpenCL and DirectCompute.

NVIDIA’s partners provide turnkey easy-to-deploy Preconfigured Tesla GPU clusters that are customizable to your needs. For 3D cloud computing applications, our partners offer the Tesla RS clusters that are optimized for running RealityServer with iray.

Available Tesla Products for Data Centers:
– Tesla S2050
– Tesla M2050/M2070
– Tesla S1070
– Tesla M1060

Also I liked the hybrid GPU and CPU

And from a paper on comparing GPU and CPU using Benchmark tests on BLAS from a Debian- Dirk E’s excellent blog

http://dirk.eddelbuettel.com/blog/

Usage of accelerated BLAS libraries seems to shrouded in some mystery, judging from somewhat regularly recurring requests for help on lists such as r-sig-hpc(gmane version), the R list dedicated to High-Performance Computing. Yet it doesn’t have to be; installation can be really simple (on appropriate systems).

Another issue that I felt needed addressing was a comparison between the different alternatives available, quite possibly including GPU computing. So a few weeks ago I sat down and wrote a small package to run, collect, analyse and visualize some benchmarks. That package, called gcbd (more about the name below) is now onCRAN as of this morning. The package both facilitates the data collection for the paper it also contains (in the vignette form common among R packages) and provides code to analyse the data—which is also included as a SQLite database. All this is done in the Debian and Ubuntu context by transparently installing and removing suitable packages providing BLAS implementations: that we can fully automate data collection over several competing implementations via a single script (which is also included). Contributions of benchmark results is encouraged—that is the idea of the package.

And from his paper on the same-

Analysts are often eager to reap the maximum performance from their computing platforms.

A popular suggestion in recent years has been to consider optimised basic linear algebra subprograms (BLAS). Optimised BLAS libraries have been included with some (commercial) analysis platforms for a decade (Moler 2000), and have also been available for (at least some) Linux distributions for an equally long time (Maguire 1999). Setting BLAS up can be daunting: the R language and environment devotes a detailed discussion to the topic in its Installation and Administration manual (R Development Core Team 2010b, appendix A.3.1). Among the available BLAS implementations, several popular choices have emerged. Atlas (an acronym for Automatically Tuned Linear Algebra System) is popular as it has shown very good performance due to its automated and CPU-speci c tuning (Whaley and Dongarra 1999; Whaley and Petitet 2005). It is also licensed in such a way that it permits redistribution leading to fairly wide availability of Atlas.1 We deploy Atlas in both a single-threaded and a multi-threaded con guration. Another popular BLAS implementation is Goto BLAS which is named after its main developer, Kazushige Goto (Goto and Van De Geijn 2008). While `free to use’, its license does not permit redistribution putting the onus of con guration, compilation and installation on the end-user. Lastly, the Intel Math Kernel Library (MKL), a commercial product, also includes an optimised BLAS library. A recent addition to the tool chain of high-performance computing are graphical processing units (GPUs). Originally designed for optimised single-precision arithmetic to accelerate computing as performed by graphics cards, these devices are increasingly used in numerical analysis. Earlier criticism of insucient floating-point precision or severe performance penalties for double-precision calculation are being addressed by the newest models. Dependence on particular vendors remains a concern with NVidia’s CUDA toolkit (NVidia 2010) currently still the preferred development choice whereas the newer OpenCL standard (Khronos Group 2008) may become a more generic alternative that is independent of hardware vendors. Brodtkorb et al. (2010) provide an excellent recent survey. But what has been lacking is a comparison of the e ective performance of these alternatives. This paper works towards answering this question. By analysing performance across ve di erent BLAS implementations|as well as a GPU-based solution|we are able to provide a reasonably broad comparison.

Performance is measured as an end-user would experience it: we record computing times from launching commands in the interactive R environment (R Development Core Team 2010a) to their completion.

And

Basic Linear Algebra Subprograms (BLAS) provide an Application Programming Interface
(API) for linear algebra. For a given task such as, say, a multiplication of two conformant
matrices, an interface is described via a function declaration, in this case sgemm for single
precision and dgemm for double precision. The actual implementation becomes interchangeable
thanks to the API de nition and can be supplied by di erent approaches or algorithms. This
is one of the fundamental code design features we are using here to benchmark the di erence
in performance from di erent implementations.
A second key aspect is the di erence between static and shared linking. In static linking,
object code is taken from the underlying library and copied into the resulting executable.
This has several key implications. First, the executable becomes larger due to the copy of
the binary code. Second, it makes it marginally faster as the library code is present and
no additional look-up and subsequent redirection has to be performed. The actual amount
of this performance penalty is the subject of near-endless debate. We should also note that
this usually amounts to only a small load-time penalty combined with a function pointer
redirection|the actual computation e ort is unchanged as the actual object code is identi-
cal. Third, it makes the program more robust as fewer external dependencies are required.
However, this last point also has a downside: no changes in the underlying library will be
reected in the binary unless a new build is executed. Shared library builds, on the other
hand, result in smaller binaries that may run marginally slower|but which can make use of
di erent libraries without a rebuild.

Basic Linear Algebra Subprograms (BLAS) provide an Application Programming Interface(API) for linear algebra. For a given task such as, say, a multiplication of two conformantmatrices, an interface is described via a function declaration, in this case sgemm for singleprecision and dgemm for double precision. The actual implementation becomes interchangeablethanks to the API de nition and can be supplied by di erent approaches or algorithms. Thisis one of the fundamental code design features we are using here to benchmark the di erencein performance from di erent implementations.A second key aspect is the di erence between static and shared linking. In static linking,object code is taken from the underlying library and copied into the resulting executable.This has several key implications. First, the executable becomes larger due to the copy ofthe binary code. Second, it makes it marginally faster as the library code is present andno additional look-up and subsequent redirection has to be performed. The actual amountof this performance penalty is the subject of near-endless debate. We should also note thatthis usually amounts to only a small load-time penalty combined with a function pointerredirection|the actual computation e ort is unchanged as the actual object code is identi-cal. Third, it makes the program more robust as fewer external dependencies are required.However, this last point also has a downside: no changes in the underlying library will bereected in the binary unless a new build is executed. Shared library builds, on the otherhand, result in smaller binaries that may run marginally slower|but which can make use ofdi erent libraries without a rebuild.

And summing up,

reference BLAS to be dominated in all cases. Single-threaded Atlas BLAS improves on the reference BLAS but loses to multi-threaded BLAS. For multi-threaded BLAS we nd the Goto BLAS dominate the Intel MKL, with a single exception of the QR decomposition on the xeon-based system which may reveal an error. The development version of Atlas, when compiled in multi-threaded mode is competitive with both Goto BLAS and the MKL. GPU computing is found to be compelling only for very large matrix sizes. Our benchmarking framework in the gcbd package can be employed by others through the R packaging system which could lead to a wider set of benchmark results. These results could be helpful for next-generation systems which may need to make heuristic choices about when to compute on the CPU and when to compute on the GPU.

Source – DirkE’paper and blog http://dirk.eddelbuettel.com/papers/gcbd.pdf

Quite appropriately-,

Hardware solutions or atleast need to be a part of Revolution Analytic’s thinking as well. SPSS does not have any choice anymore though 😉

It would be interesting to see how the new SAS Cloud Computing/ Server Farm/ Time Sharing facility is benchmarking CPU and GPU for SAS analytics performance – if being done already it would be nice to see a SUGI paper on the same at http://sascommunity.org.

Multi threading needs to be taken care automatically by statistical software to optimize current local computing (including for New R)

Acceptable benchmarks for testing hardware as well as software need to be reinforced and published across vendors, academics  and companies.

What do you think?


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


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