RCOMM 2012 goes live in August

An awesome conference by an awesome software Rapid Miner remains one of the leading enterprise grade open source software , that can help you do a lot of things including flow driven data modeling ,web mining ,web crawling etc which even other software cant.

Presentations include:

  • Mining Machine 2 Machine Data (Katharina Morik, TU Dortmund University)
  • Handling Big Data (Andras Benczur, MTA SZTAKI)
  • Introduction of RapidAnalytics at Telenor (Telenor and United Consult)
  • and more

Here is a list of complete program

 

Program

 

Time
Slot
Tuesday
Training / Workshop 1
Wednesday
Conference 1
Thursday
Conference 2
Friday
Training / Workshop 2
09:00 – 10:30
Introductory Speech
Ingo Mierswa (Rapid-I)Resource-aware Data Mining or M2M Mining (Invited Talk)

Katharina Morik (TU Dortmund University)

More information

 

Data Analysis

 

NeurophRM: Integration of the Neuroph framework into RapidMiner
Miloš Jovanović, Jelena Stojanović, Milan Vukićević, Vera Stojanović, Boris Delibašić (University of Belgrade)

To be announced (Invited Talk)
Andras Benczur 

Recommender Systems

 

Extending RapidMiner with Recommender Systems Algorithms
Matej Mihelčić, Nino Antulov-Fantulin, Matko Bošnjak, Tomislav Šmuc (Ruđer Bošković Institute)

Implementation of User Based Collaborative Filtering in RapidMiner
Sérgio Morais, Carlos Soares (Universidade do Porto)

Parallel Training / Workshop Session

Advanced Data Mining and Data Transformations

or

Development Workshop Part 2

10:30 – 11:00
Coffee Break
Coffee Break
Coffee Break
11:00 – 12:30
Data Analysis

Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner
Mennatallah Amer, Markus Goldstein (DFKI)

Customers’ LifeStyle Targeting on Big Data using Rapid Miner
Maksim Drobyshev (LifeStyle Marketing Ltd)

Robust GPGPU Plugin Development for RapidMiner
Andor Kovács, Zoltán Prekopcsák (Budapest University of Technology and Economics)

Extensions

 

Optimization Plugin For RapidMiner
Venkatesh Umaashankar, Sangkyun Lee (TU Dortmund University; presented by Hendrik Blom)

 

Image Mining Extension – Year After
Radim Burget, Václav Uher, Jan Mašek (Brno University of Technology)

Incorporating R Plots into RapidMiner Reports
Peter Jeszenszky (University of Debrecen)

12:30 – 13:30
Lunch
Lunch
Lunch
13:30 – 15:30
Parallel Training / Workshop Session

Basic Data Mining and Data Transformations

or

Development Workshop Part 1

Applications

 

Introduction of RapidAnalyticy Enterprise Edition at Telenor Hungary
t.b.a. (Telenor Hungary and United Consult)

 

Application of RapidMiner in Steel Industry Research and Development
Bengt-Henning Maas, Hakan Koc, Martin Bretschneider (Salzgitter Mannesmann Forschung)

A Comparison of Data-driven Models for Forecast River Flow
Milan Cisty, Juraj Bezak (Slovak University of Technology)

Portfolio Optimization Using Local Linear Regression Ensembles in Rapid Miner
Gábor Nagy, Tamás Henk, Gergő Barta (Budapest University of Technology and Economics)

Extensions

 

An Octave Extension for RapidMiner
Sylvain Marié (Schneider Electric)

 

Unstructured Data

 

Processing Data Streams with the RapidMiner Streams-Plugin
Christian Bockermann, Hendrik Blom (TU Dortmund)

Automated Creation of Corpuses for the Needs of Sentiment Analysis
Peter Koncz, Jan Paralic (Technical University of Kosice)

 

Demonstration: News from the Rapid-I Labs
Simon Fischer; Rapid-I

This short session demonstrates the latest developments from the Rapid-I lab and will let you how you can build powerful analysis processes and routines by using those RapidMiner tools.

Certification Exam
15:30 – 16:00
Coffee Break
Coffee Break
Coffee Break
16:00 – 18:00
Book Presentation and Game Show

Data Mining for the Masses: A New Textbook on Data Mining for Everyone
Matthew North (Washington & Jefferson College)

Matthew North presents his new book “Data Mining for the Masses” introducing data mining to a broader audience and making use of RapidMiner for practical data mining problems.

 

Game Show
Did you miss last years’ game show “Who wants to be a data miner?”? Use RapidMiner for problems it was never created for and beat the time and other contestants!

User Support

Get some Coffee for free – Writing Operators with RapidMiner Beans
Christian Bockermann, Hendrik Blom (TU Dortmund)

Meta-Modeling Execution Times of RapidMiner operators
Matija Piškorec, Matko Bošnjak, Tomislav Šmuc (Ruđer Bošković Institute)

Conference day ends at ca. 17:00.

19:30
Social Event (Conference Dinner)
Social Event (Visit of Bar District)

 

and you should have a look at https://rapid-i.com/rcomm2012f/index.php?option=com_content&view=article&id=65

Conference is in Budapest, Hungary,Europe.

( Disclaimer- Rapid Miner is an advertising sponsor of Decisionstats.com in case you didnot notice the two banner sized ads.)

 

Facebook Search- The fall of the machines

Increasingly I am beginning to search more and more on Facebook. This is for the following reasons-

1) Facebook is walled off to Google (mostly). While within Facebook , I get both people results and content results (from Bing).

Bing is an okay alternative , though not as fast as Google Instant.

2) Cleaner Web Results When Facebook increases the number of results from 3 top links to say 10 top links, there should be more outbound traffic from FB search to websites.For some reason Google continues to show 14 pages of results… Why? Why not limit to just one page.

3) Better People Search than  Pipl and Google. But not much (or any) image search. This is curious and I am hoping the Instagram results would be added to search results.

4) I am hoping for any company Facebook or Microsoft to challenge Adsense . Adwords already has rivals. Adsense is a de facto monopoly and my experiences in advertising show that content creators can make much more money from a better Adsense (especially ) if Adsense and Adwords do not have a conflict of interest from same advertisers.

Adwords should have been a special case of Adsense for Google.com but it is not.

5) Machine learning can only get you from tau to delta tau. When ad click behavior is inherently dependent on humans who behave mostly on chaotic , or genetic models than linear CPC models. I find FB has an inherent advantage in the quantity and quality of data collected on people behavior rather than click behavior. They are also more aggressive and less apologetic about behavorially targeted  ads.

Additional point- Analytics for Google Analytics is not as rich as analytics from Facebook pages in terms of demographic variables. This can be tested by anyone.

 

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

New Plotters in Rapid Miner 5.2

I almost missed this because of my vacation and traveling

Rapid Miner has a tonne of new stuff (Statuary Ethics Declaration- Rapid Miner has been an advertising partner for Decisionstats – see the right margin)

see

http://rapid-i.com/component/option,com_myblog/Itemid,172/lang,en/

Great New Graphical Plotters

and some flashy work

and a great series of educational lectures

A Simple Explanation of Decision Tree Modeling based on Entropies

Link: http://www.simafore.com/blog/bid/94454/A-simple-explanation-of-how-entropy-fuels-a-decision-tree-model

Description of some of the basics of decision trees. Simple and hardly any math, I like the plots explaining the basic idea of the entropy as splitting criterion (although we actually calculate gain ratio differently than explained…)

Logistic Regression for Business Analytics using RapidMiner

Link: http://www.simafore.com/blog/bid/57924/Logistic-regression-for-business-analytics-using-RapidMiner-Part-2

Same as above, but this time for modeling with logistic regression.
Easy to read and covering all basic ideas together with some examples. If you are not familiar with the topic yet, part 1 (see below) might help.

Part 1 (Basics): http://www.simafore.com/blog/bid/57801/Logistic-regression-for-business-analytics-using-RapidMiner-Part-1

Deploy Model: http://www.simafore.com/blog/bid/82024/How-to-deploy-a-logistic-regression-model-using-RapidMiner

Advanced Information: http://www.simafore.com/blog/bid/99443/Understand-3-critical-steps-in-developing-logistic-regression-models

and lastly a new research project for collaborative data mining

http://www.e-lico.eu/

e-LICO Architecture and Components

The goal of the e-LICO project is to build a virtual laboratory for interdisciplinary collaborative research in data mining and data-intensive sciences. The proposed e-lab will comprise three layers: the e-science and data mining layers will form a generic research environment that can be adapted to different scientific domains by customizing the application layer.

  1. Drag a data set into one of the slots. It will be automatically detected as training data, test data or apply data, depending on whether it has a label or not.
  2. Select a goal. The most frequent one is probably “Predictive Modelling”. All goals have comments, so you see what they can be used for.
  3. Select “Fetch plans” and wait a bit to get a list of processes that solve your problem. Once the planning completes, select one of the processes (you can see a preview at the right) and run it. Alternatively, select multiple (selecting none means selecting all) and evaluate them on your data in a batch.

The assistant strives to generate processes that are compatible with your data. To do so, it performs a lot of clever operations, e.g., it automatically replaces missing values if missing values exist and this is required by the learning algorithm or performs a normalization when using a distance-based learner.

You can install the extension directly by using the Rapid-I Marketplace instead of the old update server. Just go to the preferences and enter http://rapidupdate.de:8180/UpdateServer as the update URL

Of course Rapid Miner has been of the most professional open source analytics company and they have been doing it for a long time now. I am particularly impressed by the product map (see below) and the graphical user interface.

http://rapid-i.com/content/view/186/191/lang,en/

Product Map

Just click on the products in the overview below in order to get more information about Rapid-I products.

 

Rapid-I Product Overview 

 

Quantitative Modeling for Arbitrage Positions in Ad KeyWords Internet Marketing

Assume you treat an ad keyword as an equity stock. There are slight differences in the cost for advertising for that keyword across various locations (Zurich vs Delhi) and various channels (Facebook vs Google) . You get revenue if your website ranks naturally in organic search for the keyword, and you have to pay costs for getting traffic to your website for that keyword.
An arbitrage position is defined as a riskless profit when cost of keyword is less than revenue from keyword. We take examples of Adsense  and Adwords primarily.
There are primarily two types of economic curves on the foundation of which commerce of the  internet  resides-
1) Cost Curve- Cost of Advertising to drive traffic into the website  (Google Adwords, Twitter Ads, Facebook , LinkedIn ads)
2) Revenue Curve – Revenue from ads clicked by the incoming traffic on website (like Adsense, LinkAds, Banner Ads, Ad Sharing Programs , In Game Ads)
The cost and revenue curves are primarily dependent on two things
1) Type of KeyWord-Also subdependent on
a) Location of Prospective Customer, and
b) Net Present Value of Good and Service to be eventually purchased
For example , keyword for targeting sales of enterprise “business intelligence software” should ideally be costing say X times as much as keywords for “flower shop for birthdays” where X is the multiple of the expected payoffs from sales of business intelligence software divided by expected payoff from sales of flowers (say in Location, Daytona Beach ,Florida or Austin, Texas)
2) Traffic Volume – Also sub-dependent on Time Series and
a) Seasonality -Annual Shoppping Cycle
b) Cyclicality– Macro economic shifts in time series
The cost and revenue curves are not linear and ideally should be continuous in a definitive exponential or polynomial manner, but in actual reality they may have sharp inflections , due to location, time, as well as web traffic volume thresholds
Type of Keyword – For example ,keywords for targeting sales for Eminem Albums may shoot up in a non linear manner after the musician dies.
The third and not so publicly known component of both the cost and revenue curves is factoring in internet industry dynamics , including relative market share of internet advertising platforms, as well as percentage splits between content creator and ad providing platforms.
For example, based on internet advertising spend, people belive that the internet advertising is currently heading for a duo-poly with Google and Facebook are the top two players, while Microsoft/Skype/Yahoo and LinkedIn/Twitter offer niche options, but primarily depend on price setting from Google/Bing/Facebook.
It is difficut to quantify  the elasticity and efficiency of market curves as most literature and research on this is by in-house corporate teams , or advisors or mentors or consultants to the primary leaders in a kind of incesteous fraternal hold on public academic research on this.
It is recommended that-
1) a balance be found in the need for corporate secrecy to protest shareholder value /stakeholder value maximization versus the need for data liberation for innovation and grow the internet ad pie faster-
2) Cost and Revenue Curves between different keywords, time,location, service providers, be studied by quants for hedging inetrent ad inventory or /and choose arbitrage positions This kind of analysis is done for groups of stocks and commodities in the financial world, but as commerce grows on the internet this may need more specific and independent quants.
3) attention be made to how cost and revenue curves mature as per level of sophistication of underlying economy like Brazil, Russia, China, Korea, US, Sweden may be in different stages of internet ad market evolution.
For example-
A study in cost and revenue curves for certain keywords across domains across various ad providers across various locations from 2003-2008 can help academia and research (much more than top ten lists of popular terms like non quantitative reports) as well as ensure that current algorithmic wightings are not inadvertently given away.
Part 2- of this series will explore the ways to create third party re-sellers of keywords and measuring impacts of search and ad engine optimization based on keywords.

Does the Internet need its own version of credit bureaus

Data Miners love data. The more data they have the better model they can build. Consumers do not love data so much and find sharing data generally a cumbersome task. They need to be incentivize for filling out survey forms , and for signing to loyalty programs. Lawyers, and privacy advocates love to use examples of improper data collection and usage as the harbinger of an ominous scenario. George Orwell’s 1984 never “mentioned” anything about Big Brother trying to sell you one more loan, credit card or product.

Data generated by customers is now growing without their needing to fill out forms and surveys. This data is about their preferences , tastes and choices and is growing in size and depth because it is generated from social media channels on the Internet.It is this data that can be and is captured by social media analytics.

Mobile data is also growing, including usage of location based applications and usage of Internet from the mobile phone is leading to further increases in data about consumers.Increasingly , location based applications help to provide a much more relevant context to the data generated. Just mobile data is expected to grow to 15 exabytes by 2015.

People want to have more and more conversations online publicly , share pictures , activity and interact with a large number of people whom  they have never met. But resent that information being used or abused without their knowledge.

Also the Internet is increasingly being consolidated into a few players like Microsoft, Amazon, Google  and Facebook, who are unable to agree on agreements to share that data between themselves. Interestingly you can use Yahoo as a data middleman between Google and Facebook.

At the same time, more and more purchases are being done online by customers and Internet advertising has grown much above the rate of growth of other mediums of communication.
Internet retail sales have the advantage that better demand predictability can lead to lower inventories as retailers need not stock up displays to look good. An Amazon warehouse need not keep material to simply stock up it shelves like a K-Mart does.

Our Hypothesis – An Analogy with how Financial Data Marketing is managed offline

  1. Financial information regarding spending and saving is much more sensitive yet the presence of credit bureaus alleviates these concerns.
  2. Credit bureaus collect information from all sources, aggregate and anonymize the individual components accordingly.They use SSN as a unique identifier.
  3. The Internet has a unique number too , called the Internet Protocol Address (I.P) 
  4. Should there be a unique identifier like Internet Security Number for the Internet to ensure adequate balance between the need for privacy as well as the need for appropriate targeting? 

After all, no one complains about privacy intrusions if their credit bureau data is aggregated , rolled up, and anonymized and turned into a propensity model for sending them direct mailers.

Advertising using Social Media and Internet

https://www.facebook.com/about/ads/#stories

1. A business creates an ad
Let’s say a gym opens in your neighborhood. The owner creates an ad to get people to come in for a free workout.
2. Facebook gets paid to deliver the ad
The owner sends the ad to Facebook and describes who should see it: people who live nearby and like running.
The right people see the ad
3. Facebook only shows you the ad if you live in town and like to run. That’s how advertisers reach you without knowing who you are.

Adding in credit bureau data and legislative regulation for anonymizing  and handling privacy data can expand the internet selling market, which is much more efficient from a supply chain perspective than the offline display and shop models.

Privacy Regulations on Marketing using Internet data
Should laws on opt out and do not mail, do not call, lists be extended to do not show ads , do not collect information on social media. In the offline world, you can choose to be part of direct marketing or opt out of direct marketing by enrolling yourself in various do not solicit lists. On the internet the only option from advertisements is to use the Adblock plugin if you are Google Chrome or Firefox browser user. Even Facebook gives you many more ads than you need to see.

One reason for so many ads on the Internet is lack of central anonymize data repositories for giving high quality data to these marketing companies.Software that can be used for social media analytics is already available off the shelf.

The growth of the Internet has helped carved out a big industry for Internet web analytics so it is a matter of time before social media analytics becomes a multi billion dollar business as well. What new developments would be unleashed in this brave new world is just a matter of time, and of course of the social media data!

Ads Alliance on Internet

Just saw

the Digital Advertising Alliance’s (DAA) Self-Regulatory Program for Online Behavioral Advertising.

Multi-Site Data Collection Principles Broaden Self Regulation Beyond Online Behavioral Advertising
WASHINGTON, D.C., NOVEMBER 7, 2011

The new Principles consist of the following specific requirements:

  1. Transparency and consumer control for purposes other than OBA – The Multi-Site Data Principles call for organizations that collect Multi-Site Data for purposes other than OBA to provide transparency and control regarding Internet surfing across unrelated Websites.
  2. Collection / use of data for eligibility determination – The Multi-Site Data Principles prohibit the collection, use or transfer of Internet surfing data across Websites for determination of a consumer’s eligibility for employment, credit standing, healthcare treatment and insurance.
  3. Collection / use of children’s data – The Multi-Site Data Principles state that organizations must comply with the Children’s Online Privacy Protection Act (COPPA).
  4. Meaningful accountability – The Multi-Site Data Principles are subject to enforcement through strong accountability mechanisms.

http://www.aboutads.info/principles

The DAA Self-Regulatory Principles

 

The cross-industry Self-Regulatory Principles for Multi-Site Data augment the Self-Regulatory   Principles for Online Behavioral Advertising  (OBA)  by covering the prospective  collection of Web site   data beyond that collected for OBA purposes.  The existing OBA  Principles and definitions  remain in   full force and effect and are not limited by the new  principles.

The cross-industry Self-Regulatory Principles for Online Behavioral Advertising was developed by   leading industry associations to apply  consumer-friendly standards to online  behavioral advertising  across the Internet. Online behavioral advertising increasingly supports the convenient access to  content, services, and applications over the Internet that consumers have come to expect at no cost   to them.

The Education Principle calls for organizations to participate in efforts to educate individuals and businesses about online behavioral advertising and the Principles.

The Transparency Principle calls for clearer and easily accessible disclosures to consumers about data collection and use practices associated with online behavioral advertising. It will result in new, enhanced notice on the page where data is collected through links embedded in or around advertisements, or on the Web page itself.

The Consumer Control Principle provides consumers with an expanded ability to choose whether data is collected and used for online behavioral advertising purposes. This choice will be available through a link from the notice provided on the Web page where data is collected.

The Consumer Control Principle requires “service providers”, a term that includes Internet access service providers and providers of desktop applications software such as Web browser “tool bars” to obtain the consent of users before engaging in online behavioral advertising, and take steps to de-identify the data used for such purposes.

The Data Security Principle calls for organizations to provide appropriate security for, and limited retention of data, collected and used for online behavioral advertising purposes.

The Material Changes Principle calls for obtaining consumer consent before a Material Change is made to an entity’s Online Behavioral Advertising data collection and use policies unless that change will result in less collection or use of data.

The Sensitive Data Principle recognizes that data collected from children and used for online behavioral advertising merits heightened protection, and requires parental consent for behavioral advertising to consumers known to be under 13 on child-directed Web sites. This Principle also provides heightened protections to certain health and financial data when attributable to a specific individual.

The Accountability Principle calls for development of programs to further advance these Principles, including programs to monitor and report instances of uncorrected non-compliance with these Principles to appropriate government agencies. The CBBB and DMA have been asked and agreed to work cooperatively to establish accountability mechanisms under the Principles.

 

Ajay- So why the self regulations?

Answer- Shoddy Maths in behaviorally targeted ads is leading to a very high glut in targeted ads, more than can be reasonably expected to click based on consumer spending. On the internet- unlike on television- cost is less of a barrrier to OVER ADVERTISING.