Here is a great website for sharing workflows – it is called MyExperiment.org and it can also include Work flows from many software.
myExperiment currently has 4742 members, 270 groups, 1842 workflows, 423 files and 173 packs
Could it also include workflow from Red-R from #rstats or Enterprise Miner
For some time now, I had been hoping for a place where new package or algorithm developers get at least a fraction of the money that iPad or iPhone application developers get. Rapid Miner has taken the lead in establishing a marketplace for extensions. Is there going to be paid extensions as well- I hope so!!
This probably makes it the first “app” marketplace in open source and the second app marketplace in analytics after salesforce.com
It is hard work to think of new algols, and some of them can really be usefull.
Can we hope for #rstats marketplace where people downloading say ggplot3.0 atleast get a prompt to donate 99 cents per download to Hadley Wickham’s Amazon wishlist. http://www.amazon.com/gp/registry/1Y65N3VFA613B
Do you think it is okay to pay 99 cents per iTunes song, but not pay a cent for open source software.
I dont know- but I am just a capitalist born in a country that was socialist for the first 13 years of my life. Congratulations once again to Rapid Miner for innovating and leading the way.
http://rapid-i.com/component/option,com_myblog/show,Rapid-I-Marketplace-Launched.html/Itemid,172
| RapidMiner, Marketplace, Extensions | 30 May 2011 |
| Rapid-I Marketplace Launched by Simon Fischer |
Over the years, many of you have been developing new RapidMiner Extensions dedicated to a broad set of topics. Whereas these extensions are easy to install in RapidMiner – just download and place them in the plugins folder – the hard part is to find them in the vastness that is the Internet. Extensions made by ourselves at Rapid-I, on the other hand, are distributed by the update server making them searchable and installable directly inside RapidMiner.
We thought that this was a bit unfair, so we decieded to open up the update server to the public, and not only this, we even gave it a new look and name. The Rapid-I Marketplace is available in beta mode at http://rapidupdate.de:8180/ . You can use the Web interface to browse, comment, and rate the extensions, and you can use the update functionality in RapidMiner by going to the preferences and entering http://rapidupdate.de:8180/UpdateServer/ as the update server URL. (Once the beta test is complete, we will change the port back to 80 so we won’t have any firewall problems.)
As an Extension developer, just register with the Marketplace and drop me an email (fischer at rapid-i dot com) so I can give you permissions to upload your own extension. Upload is simple provided you use the standard RapidMiner Extension build process and will boost visibility of your extension.
Looking forward to see many new extensions there soon!
Disclaimer- Decisionstats is a partner of Rapid Miner. I have been liking the software for a long long time, and recently agreed to partner with them just like I did with KXEN some years back, and with Predictive AnalyticsConference, and Aster Data until last year.
I still think Rapid Miner is a very very good software,and a globally created software after SAP.
Here is the actual marketplace
http://rapidupdate.de:8180/UpdateServer/faces/index.xhtml
The Rapid-I Marketplace will soon replace the RapidMiner update server. Using this marketplace, you can share your RapidMiner extensions and make them available for download by the community of RapidMiner users. Currently, we are beta testing this server. If you want to use this server in RapidMiner, you must go to the preferences and enter http://rapidupdate.de:8180/UpdateServer for the update url. After the beta test, we will change the port back to 80, which is currently occupied by the old update server. You can test the marketplace as a user (downloading extensions) and as an Extension developer. If you want to publish your extension here, please let us know via the contact form.
| 5/30/11 12:39 PM | User burgetrm has uploaded version 1.1.0 of Imageprocessing. |
| 5/30/11 12:34 PM | User burgetrm has uploaded version 1.0.0 of Imageprocessing. |
| 5/30/11 11:55 AM | User burgetrm has created the new product Imageprocessing. |
| 5/30/11 11:12 AM | User Rapid-I has uploaded version 5.0.7 of RapidMiner. |
| 5/30/11 11:12 AM | User Rapid-I has uploaded version 5.0.2 of RapidMiner. |
This is a short list of several known as well as lesser known R ( #rstats) language codes, packages and tricks to build a business intelligence application. It will be slightly Messy (and not Messi) but I hope to refine it someday when the cows come home.
It assumes that BI is basically-
a Database, a Document Database, a Report creation/Dashboard pulling software as well unique R packages for business intelligence.
What is business intelligence?
Seamless dissemination of data in the organization. In short let it flow- from raw transactional data to aggregate dashboards, to control and test experiments, to new and legacy data mining models- a business intelligence enabled organization allows information to flow easily AND capture insights and feedback for further action.
BI software has lately meant to be just reporting software- and Business Analytics has meant to be primarily predictive analytics. the terms are interchangeable in my opinion -as BI reports can also be called descriptive aggregated statistics or descriptive analytics, and predictive analytics is useless and incomplete unless you measure the effect in dashboards and summary reports.
Data Mining- is a bit more than predictive analytics- it includes pattern recognizability as well as black box machine learning algorithms. To further aggravate these divides, students mostly learn data mining in computer science, predictive analytics (if at all) in business departments and statistics, and no one teaches metrics , dashboards, reporting in mainstream academia even though a large number of graduates will end up fiddling with spreadsheets or dashboards in real careers.
Using R with
1) Databases-
I created a short list of database connectivity with R here at https://rforanalytics.wordpress.com/odbc-databases-for-r/ but R has released 3 new versions since then.
The RODBC package remains the package of choice for connecting to SQL Databases.
http://cran.r-project.org/web/packages/RODBC/RODBC.pdf
Details on creating DSN and connecting to Databases are given at https://rforanalytics.wordpress.com/odbc-databases-for-r/
For document databases like MongoDB and CouchDB
( what is the difference between traditional RDBMS and NoSQL if you ever need to explain it in a cocktail conversation http://dba.stackexchange.com/questions/5/what-are-the-differences-between-nosql-and-a-traditional-rdbms
Basically dispensing with the relational setup, with primary and foreign keys, and with the additional overhead involved in keeping transactional safety, often gives you extreme increases in performance
NoSQL is a kind of database that doesn’t have a fixed schema like a traditional RDBMS does. With the NoSQL databases the schema is defined by the developer at run time. They don’t write normal SQL statements against the database, but instead use an API to get the data that they need.
instead relating data in one table to another you store things as key value pairs and there is no database schema, it is handled instead in code.)
I believe any corporation with data driven decision making would need to both have atleast one RDBMS and one NoSQL for unstructured data-Ajay. This is a sweeping generic statement 😉 , and is an opinion on future technologies.
From- http://tommy.chheng.com/2010/11/03/rmongo-accessing-mongodb-in-r/
http://plindenbaum.blogspot.com/2010/09/connecting-to-mongodb-database-from-r.html
Connecting to a MongoDB database from R using Java
http://nsaunders.wordpress.com/2010/09/24/connecting-to-a-mongodb-database-from-r-using-java/
Also see a nice basic analysis using R Mongo from
http://pseudofish.com/blog/2011/05/25/analysis-of-data-with-mongodb-and-r/
For CouchDB
please see https://github.com/wactbprot/R4CouchDB and
http://digitheadslabnotebook.blogspot.com/2010/10/couchdb-and-r.html
2) External Report Creating Software-
Jaspersoft- It has good integration with R and is a certified Revolution Analytics partner (who seem to be the only ones with a coherent #Rstats go to market strategy- which begs the question – why is the freest and finest stats software having only ONE vendor- if it was so great lots of companies would make exclusive products for it – (and some do -see https://rforanalytics.wordpress.com/r-business-solutions/ and https://rforanalytics.wordpress.com/using-r-from-other-software/)
From
http://www.jaspersoft.com/sites/default/files/downloads/events/Analytics%20-Jaspersoft-SEP2010.pdf
we see
http://jasperforge.org/projects/rrevodeployrbyrevolutionanalytics
RevoConnectR for JasperReports Server
RevoConnectR for JasperReports Server RevoConnectR for JasperReports Server is a Java library interface between JasperReports Server and Revolution R Enterprise’s RevoDeployR, a standardized collection of web services that integrates security, APIs, scripts and libraries for R into a single server. JasperReports Server dashboards can retrieve R charts and result sets from RevoDeployR.
http://jasperforge.org/plugins/esp_frs/optional_download.php?group_id=409
R and BI – Integrating R with Open Source Business Intelligence Platforms Pentaho and Jaspersoft David Reinke, Steve Miller Keywords: business intelligence Increasingly, R is becoming the tool of choice for statistical analysis, optimization, machine learning and visualization in the business world. This trend will only escalate as more R analysts transition to business from academia. But whereas in academia R is often the central tool for analytics, in business R must coexist with and enhance mainstream business intelligence (BI) technologies. A modern BI portfolio already includes relational databeses, data integration (extract, transform, load – ETL), query and reporting, online analytical processing (OLAP), dashboards, and advanced visualization. The opportunity to extend traditional BI with R analytics revolves on the introduction of advanced statistical modeling and visualizations native to R. The challenge is to seamlessly integrate R capabilities within the existing BI space. This presentation will explain and demo an initial approach to integrating R with two comprehensive open source BI (OSBI) platforms – Pentaho and Jaspersoft. Our efforts will be successful if we stimulate additional progress, transparency and innovation by combining the R and BI worlds. The demonstration will show how we integrated the OSBI platforms with R through use of RServe and its Java API. The BI platforms provide an end user web application which include application security, data provisioning and BI functionality. Our integration will demonstrate a process by which BI components can be created that prompt the user for parameters, acquire data from a relational database and pass into RServer, invoke R commands for processing, and display the resulting R generated statistics and/or graphs within the BI platform. Discussion will include concepts related to creating a reusable java class library of commonly used processes to speed additional development.
If you know Java- try http://ramanareddyg.blog.com/2010/07/03/integrating-r-and-pentaho-data-integration/
and I like this list by two venerable powerhouses of the BI Open Source Movement
http://www.openbi.com/demosarticles.html
Open Source BI as disruptive technology
http://www.openbi.biz/articles/osbi_disruption_openbi.pdf
Open Source Punditry
| TITLE | AUTHOR | COMMENTS |
|---|---|---|
| Commercial Open Source BI Redux | Dave Reinke & Steve Miller | An review and update on the predictions made in our 2007 article focused on the current state of the commercial open source BI market. Also included is a brief analysis of potential options for commercial open source business models and our take on their applicability. |
| Open Source BI as Disruptive Technology | Dave Reinke & Steve Miller | Reprint of May 2007 DM Review article explaining how and why Commercial Open Source BI (COSBI) will disrupt the traditional proprietary market. |
| TITLE | AUTHOR | COMMENTS |
|---|---|---|
| R You Ready for Open Source Statistics? | Steve Miller | R has become the “lingua franca” for academic statistical analysis and modeling, and is now rapidly gaining exposure in the commercial world. Steve examines the R technology and community and its relevancy to mainstream BI. |
| R and BI (Part 1): Data Analysis with R | Steve Miller | An introduction to R and its myriad statistical graphing techniques. |
| R and BI (Part 2): A Statistical Look at Detail Data | Steve Miller | The usage of R’s graphical building blocks – dotplots, stripplots and xyplots – to create dashboards which require little ink yet tell a big story. |
| R and BI (Part 3): The Grooming of Box and Whiskers | Steve Miller | Boxplots and variants (e.g. Violin Plot) are explored as an essential graphical technique to summarize data distributions by categories and dimensions of other attributes. |
| R and BI (Part 4): Embellishing Graphs | Steve Miller | Lattices and logarithmic data transformations are used to illuminate data density and distribution and find patterns otherwise missed using classic charting techniques. |
| R and BI (Part 5): Predictive Modelling | Steve Miller | An introduction to basic predictive modelling terminology and techniques with graphical examples created using R. |
| R and BI (Part 6) : Re-expressing Data |
Steve Miller | How do you deal with highly skewed data distributions? Standard charting techniques on this “deviant” data often fail to illuminate relationships. This article explains techniques to re-express skewed data so that it is more understandable. |
| The Stock Market, 2007 | Steve Miller | R-based dashboards are presented to demonstrate the return performance of various asset classes during 2007. |
| Bootstrapping for Portfolio Returns: The Practice of Statistical Analysis | Steve Miller | Steve uses the R open source stats package and Monte Carlo simulations to examine alternative investment portfolio returns…a good example of applied statistics using R. |
| Statistical Graphs for Portfolio Returns | Steve Miller | Steve uses the R open source stats package to analyze market returns by asset class with some very provocative embedded trellis charts. |
| Frank Harrell, Iowa State and useR!2007 | Steve Miller | In August, Steve attended the 2007 Internation R User conference (useR!2007). This article details his experiences, including his meeting with long-time R community expert, Frank Harrell. |
| An Open Source Statistical “Dashboard” for Investment Performance | Steve Miller | The newly launched Dashboard Insight web site is focused on the most useful of BI tools: dashboards. With this article discussing the use of R and trellis graphics, OpenBI brings the realm of open source to this forum. |
| Unsexy Graphics for Business Intelligence | Steve Miller | Utilizing Tufte’s philosophy of maximizing the data to ink ratio of graphics, Steve demonstrates the value in dot plot diagramming. The R open source statistical/analytics software is showcased. |
brew: Templating Framework for Report Generation brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module. http://bit.ly/jINmaI
http://dirk.eddelbuettel.com/blog/2011/01/16/#overbought_oversold_plot
I always liked Citrix products when I was a member of the Technical Advisory Board at the University of Tennessee. I especially liked enabling SAS software , R software, Matlab software , ONLY from a browser.
Data Mining through cloud computing, yes University of Tennessee’s analytics server http://analytics.utk.edu was way ahead in 2009- all these softwares at one portal no software needed on your own PC, simply upload data and work on any analytics software.
Here is a nice citrix webinar on managing Time (so you can read more webinars! nah. I think Youtube live streaming events with interactive question and answers is the way of the future while webinars are for Baby Boomers- you can do a test and control experiment yourself if you are in the webinar business. its a web2.oinar)
Register here
http://learn.gotomeeting.com/forms/26May11-APAC-ANZ-G2MC-WBR-L1?url=decisionstats
Standard Disclosure- I have not received any monetary or indirect compensation for promoting this webinar.
————————————————————————————————————————————————————-
Interruptions are productivity killers – between email, phone calls and back-to-back meetings, how do you find time to work on your top priorities?
Join top time-management guru Kent Curtis and learn how to stop “living in your inbox” and start prioritising tasks, messages and appointments according to what is most important.
This webinar takes the best principles from FranklinCovey’s world-class productivity training and teaches you how to apply them while using Microsoft Outlook as your scheduling tool.
Attend this interactive, one-hour webinar to:
Stay focused every day with a reliable planning system utilising Microsoft Outlook.
Control competing demands such as email, voice mail, meetings and interruptions.
Apply a planning process that gets better business results.
Reduce stress by eliminating low priority activities and distractions.
Register for the Webinar
Please forward this to colleagues who might be interested in learning more.
Kind regards,
H.R. Shiever | Managing Director – Asia Pacific
Citrix Online
A division of Citrix Systems, Inc.
http://www.citrixonline.com
Online Meetings Made Easy
GoToMeeting Corporate
Live Webinar
Title:
The New Time Management: Stay Focused Every Day with Reliable Planning
Date:
Thursday, 26 May
Time:
12 Noon Australian EST
10 AM Singapore SGT
7.30 AM India ST
Speakers:
Kent Curtis, Senior Consultant, FranklinCovey
Register here
http://learn.gotomeeting.com/forms/26May11-APAC-ANZ-G2MC-WBR-L1?url=decisionstats
If you do a Google search for Data Mining Blog- for the past several years one Blog will come on top. data mining blog – Google Search http://bit.ly/kEdPlE
To honor 5 years of Sandro Saitta’s blog (yes thats 5 years!) , we cover an exclusive interview with him where he reveals his unique sauce for cool techie blogging.
Sandro- My first experience with data mining was my master project. I used decision tree to predict pollen concentration for the following week using input data such as wind, temperature and rain. The fact that an algorithm can make a computer learn from experience was really amazing to me. I found it so interesting that I started a PhD in data mining. This time, the field of application was civil engineering. Civil engineers put a lot of sensors on their structure in order to understand how they behave. With all these sensors they generate a lot of data. To interpret these data, I used data mining techniques such as feature selection and clustering. I started my blog, Data Mining Research, during my PhD, to share with other researchers.
I then started applying data mining in the stock market as my first job in industry. I realized the difference between image recognition, where 99% correct classification rate is state of the art, and stock market, where you’re happy with 55%. However, the company ambiance was not as good as I thought, so I moved to consulting. There, I applied data mining in behavioral targeting to increase click-through rates. When you compare the number of customers who click with the ones who don’t, then you really understand what class imbalance mean. A few months ago, I accepted a very good opportunity at SICPA. I’m looking forward to resolving new challenges there.
Ajay- Your blog is the top ranked blog for “data mining blog”. Could you share some tips on better blogging for analytics and technical people
Sandro- It’s always difficult to start a blog, since at the beginning you have no reader. Writing for nobody may seem stupid, but it is not. By writing my first posts during my PhD I was reorganizing my ideas. I was expressing concepts which were not always clear to me. I thus learned a lot and also improved my English level. Of course, it’s still not perfect, but I hope most people can understand me.
Next come the readers. A few dozen each week first. To increase this number, I then started to learn SEO (Search Engine Optimization) by reading books and blogs. I tested many techniques that increased Data Mining Research visibility in the blogosphere. I think SEO is interesting when you already have some content published (which means not at the very beginning of your blog). After a while, once your blog is nicely ranked, the main task is to work on the content of the blog. To be of interest, your content must be particular: original, informative or provocative for example. I also had the chance to have a good visibility thanks to well-known people in the field like Kevin Hillstrom, Gregory Piatetsky-Shapiro, Will Dwinnell / Dean Abbott, Vincent Granville, Matthew Hurst and many others.
Ajay- Whats your favorite statistical software and what are the various softwares that you have worked with.
Could you compare and contrast these software as well.
Sandro- My favorite software at this point is SAS. I worked with it for two years. Once you know the language, you can perform ETL and data mining so easily. It’s also very fast compared to others. There are a lot of tools for data mining, but I cannot think of a tool that is as powerful as SAS and, in the same time, has a high-level programming language behind it.
I also worked with R and Matlab. R is very nice since you have all the up-to-date data mining algorithms implemented. However, working in the memory is not always a good choice, especially for ETL. Matlab is an excellent tool for prototyping. It’s not so fast and certainly not done for ETL, but the price is low regarding all the possibilities for data mining. According to me, SAS is the best choice for ETL and a good choice for data mining. Of course, there is the price.
Ajay- What are your favorite techniques and training resources for learning basics of data mining to say statisticians or business management graduates.
Sandro- I’m the kind of guy who likes to read books. I read data mining books one after the other. The fact that the same concepts are explained differently (and by different people) helps a lot in learning a topic like data mining. Of course, nothing replaces experience in the field. You can read hundreds of books, you will still not be a good practitioner until you really apply data mining in specific fields. My second choice after books is blogs. By reading data mining blogs, you will really see the issues and challenges in the field. It’s still not experience, but we are closer. Finally, web resources and networks such as KDnuggets of course, but also AnalyticBridge and LinkedIn.
Ajay- Describe your hobbies and how they help you ,if at all in your professional life.
Sandro- One of my hobbies is reading. I read a lot of books about data mining, SEO, Google as well as Sci-Fi and Fantasy. I’m a big fan of Asimov by the way. My other hobby is playing tennis. I think I simply use my hobbies as a way to find equilibrium in my life. I always try to find the best balance between work, family, friends and sport.
Ajay- What are your plans for your website for 2011-2012.
Sandro- I will continue to publish guest posts and interviews. I think it is important to let other people express themselves about data mining topics. I will not write about my current applications due to the policies of my current employer. But don’t worry, I still have a lot to write, whether it is technical or not. I will also emphasis more on my experience with data mining, advices for data miners, tips and tricks, and of course book reviews!
Standard Disclosure of Blogging- Sandro awarded me the Peoples Choice award for his blog for 2010 and carried out my interview. There is a lot of love between our respective wordpress blogs, but to reassure our puritan American readers- it is platonic and intellectual.
About Sandro S-
Sandro Saitta is a Data Mining Research Engineer at SICPA Security Solutions. He is also a blogger at Data Mining Research (www.dataminingblog.com). His interests include data mining, machine learning, search engine optimization and website marketing.
You can contact Mr Saitta at his Twitter address-
https://twitter.com/#!/dataminingblog
Here is an excellent example of how websites should help rather than hinder new customers take a demo of the software without being overwhelmed by sweet talking marketing guys who dont know the difference between heteroskedasticity, probability, odds and likelihood.
It is made by Zementis (Dr Michael Zeller has been a frequent guest here) and Revolution Analytics is still the best shot in Enterprise software for #Rstats
Now if only Revo could get into the lucrative Department of Energy or Department of Defense business- they could change the world AND earn some more revenue than they have been doing. But seriously.
Check out http://deployr.revolutionanalytics.com/zementis/ and play with it. or better still mash it with some data viz and ROC curves.- or extend it with some APIS 😉
message from Predictive Analytics World. If you are NY based you may want to drop in and listen.———————————————————————————-Tom Davenport to Keynote at
Predictive Analytics World New York
Take advantage of Super Early Bird Pricing by May 20th and recognize savings of $400. Additional savings when you bring the team*
Join your peers October 17-21, 2011 at the Hilton New York for Predictive Analytics World, the business event for predictive analytics professionals, managers and commercial practitioners, covering today’s commercial deployment of predictive analytics, across industries and across software vendors. |
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| RAVE REVIEWS:“I came to PAW because it provides case studies relevant to my industry. It has lived up to the expectation and I think it’s the best analytics conference I’ve ever attended!“
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T-Mobile PAW NYC’s agenda covers black box trading, churn modeling, crowdsourcing, demand forecasting, ensemble models, fraud detection, healthcare, insurance applications, law enforcement, litigation, market mix modeling, mobile analytics, online marketing, risk management, social data, supply chain management, targeting direct marketing, uplift modeling (net lift), and other innovative applications that benefit organizations in new and creative ways. Take advantage of Super Early Bird Pricing and realize Note: Each additional attendee from the same company registered at the same time receives an extra $200 off the Conference Pass.
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DECISION STATS 
