Poem – To Much

Chalazion of the Eyelid This is the classic li...
Image via Wikipedia

to read to ponder

so much more this earth in wonder

to work to sweat

finish task before dreaded deadline regret

 

to relax to ease

recharge renew fresh surge release

to family to friend

share joys daily day comprehend

to move to ride

swallow obstacle uncertainty ego pride

to pause to cease

total losses bandage hurt elbow knees

to write to express

thoughts tightly word compress

to all to none

end this poem fresh one begun

to die to sleep

deep secret beneath shut eyelid keep

to live be awake

eyes open wide much more beauty to partake

 

BI Software

Here is the brand new release from Jaspersoft at a groovy price of 9000$. Somebody stop these guys!

It’s a great company to watch for buyouts as well- given their expertise in REPORTING and clientele- especially for anyone looking to im prove thier standing in both open source world and reporting software branding.

From AOL owned Arrogantion’s site http://www.crunchbase.com/company/jaspersoft

 

Total $24.5M
Series D, 8/07 1
Scale Venture Partners
SAP Ventures
Doll Capital Management
Partech International
Morgenthaler Ventures
$12M
Unattributed, 12/08 2
Adams Street Partners
Red Hat
Morgenthaler Ventures
Doll Capital Management
Partech International

 

 

The news-

Announcing JasperReports Server Professional

More Resources

Webinar: Introducing JasperReports Server Professional

Thursday October 14

In this live webinar, learn how a new solution from Jaspersoft combines the world’s favorite reporting server with powerful, mature report server functionality—for about 80% less.

  • Date: Thu, Oct 14
  • Time: 10:00 AM PDT
  • Duration: 60 minutes

The World’s Most Powerful and Affordable Reporting Server

Limited Time Introductory Offer: Starting from $9,000 (restrictions apply)

JasperReports Server is the recommended product for organizations requiring an affordable reporting solution for interactive, operational, and production-based reporting. Deployed as a standalone reporting server or integrated inside another application, JasperReports Server is a flexible, powerful, interactive reporting environment for small or large enterprises.

Powered by the world’s most popular reporting tools in JasperReports and iReport, developers and users can take advantage of more interactivity, security, and scheduling of their reports.

Key Benefits:

  • Affordable: Unlimited reports for unlimited users starting at $9,000
  • Powerful: Report scheduling and distribution to 1,000s of users on a single server
  • Flexible: Web service architecture simplifies application integration
  • Secure: Centralized repository authenticates report access
  • Interactive: Easy to interact, self-serve parameterized-based reports
  • Visual appeal: Flash-based charts and maps engage users and enhance applications
  • Open: Access to any data source including relational, XML, Hibernate, EJB, POJO, and custom

 

Speaking of videos -here is a great video on BI from good ol Tennessee-a great 27 min tutorial on BI for newbies

 

Dataists shake up R community with a rocking contest

Flipboard
Image by Johan Larsson via Flickr

Newly created Dataists are creating waves on Hacker News and beyond with their innovative contest- A Recommendation Engine for R Packages.

Not only is the contest useful, it is likely to teach R Users some data hacking skills, as well as the basics of creating a GitHub Project.

Read more here-http://www.dataists.com/2010/10/using-data-tools-to-find-data-tools-the-yo-dawg-of-data-hacking/

For that reason, we’ve settled on the more manageable question, “which packages are most often installed by normal R users?”

This last question could potentially be answered in a variety of ways. Our current approach uses a convenience sample of installation data that we’ve collected from volunteers in the R community, who kindly agreed to send us a list of the packages they have on their systems. We’ve anonymized this data and compiled a set of metadata-based predictors that allow us to predict the installation probabilities quite well. We’re releasing all of our current work, including the data we have and all of the code we’ve used so far for our exploratory analyses. The contest itself will go live on Kaggle on Sunday and will end four months from Sunday on February 10, 2011. The rules, prizes and official data sets are all described below.

Rules and Prizes

To win the contest, you need to predict the probability that a user U has a package P installed on their system for every pair, (U, P). We’ll assess your performance using ROC methods, which will be evaluated against a held out test data set. The winning team will receive 3 UseR! books of their choosing. In order to win the contest, you’ll have to provide your analysis code to us by creating a fork of our GitHub repository. You’ll also be required to provide a written description of your approach. We’re asking for so much openness from the winning team because we want this contest to serve as a stepping stone for the R community. We’re also hoping that enterprising data hackers will extend the lessons learned through this contest to other programming languages.

Extract from-http://www.dataists.com/2010/10/using-data-tools-to-find-data-tools-the-yo-dawg-of-data-hacking/

Read the full article there

Microsoft Online Games

No, this is not about the X Box kind of games. It is about Microsoft ‘s tactical shift in the online space from going it alone, and building stuff itself, –to partnering, and sometimes investing and exiting business.

In Blogs- It recently announced a migration of MS Live Spaces to WordPress.com – It gives Automattic 30 million more users- no small change consider there were 26 million existing WP users.

Microsoft Messenger, which is the oldest online app in the suite, now provides instant messaging services to about 350 million users, and from now on Windows Live Writer works specifically with the WordPress.com blog service by default. Hopefully Skype, and Google Voice will show MS the way to monitize that business app yet.

Google buying blogger-blogspot seems to have done little, but given Biz Stone room to create another content disruption-Twitter.

With the round of lawsuits by proxy, in Android -Motorola, or for acquisitions – MS is just doing what Marc Anderseen (who’s apparently a better VC than Paul Allen was), Sun and co did to it in the nineties.

Google seems to be regretting putting a spade in the Yahoo acquisition- that would have tied up a big chunk of Idle MS cash- leaving it little room for niche investments (like the 250 mill that helped Facebook ramp up in time).

The real surprise here could be Apple- it has shown little interest in cloud computing- and it seems to be testing the waters with Ping. But Apple sure smells competition- and Android is doing to Iphone what Windows did to the Mac in the early 1990’s.

Google lacks presence in online gaming (despite it’s own Zynga investment)- and needs to start monetizing properties like Android OS (say 10$ for every phone license ??), Google Maps (as an app for GPS) and Google Voice. Indeed it may be time for the big G to start thinking of spinning off atleast some products- earning better returns, while retaining control (dual stock splits) and killing those anti trust lawyer fees forever.

As the Ancient Chinese said, May you live in interesting times. Fun to watch the online games people play.

 

 

Interview Michael J. A. Berry Data Miners, Inc

Here is an interview with noted Data Mining practitioner Michael Berry, author of seminal books in data mining, noted trainer and consultantmjab picture

Ajay- Your famous book “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management” came out in 2004, and an update is being planned for 2011. What are the various new data mining techniques and their application that you intend to talk about in that book.

Michael- Each time we do a revision, it feels like writing a whole new book. The first edition came out in 1997 and it is hard to believe how much the world has changed since then. I’m currently spending most of my time in the on-line retailing world. The things I worry about today–improving recommendations for cross-sell and up-sell,and search engine optimization–wouldn’t have even made sense to me back then. And the data sizes that are routine today were beyond the capacity of the most powerful super computers of the nineties. But, if possible, Gordon and I have changed even more than the data mining landscape. What has changed us is experience. We learned an awful lot between the first and second editions, and I think we’ve learned even more between the second and third.

One consequence is that we now have to discipline ourselves to avoid making the book too heavy to lift. For the first edition, we could write everything we knew (and arguably, a bit more!); now we have to remind ourselves that our intended audience is still the same–intelligent laymen with a practical interest in getting more information out of data. Not statisticians. Not computer scientists. Not academic researchers. Although we welcome all readers, we are primarily writing for someone who works in a marketing department and has a title with the word “analyst” or “analytics” in it. We have relaxed our “no equations” rule slightly for cases when the equations really do make things easier to explain, but the core explanations are still in words and pictures.

The third edition completes a transition that was already happening in the second edition. We have fully embraced standard statistical modeling techniques as full-fledged components of the data miner’s toolkit. In the first edition, it seemed important to make a distinction between old, dull, statistics, and new, cool, data mining. By the second edition, we realized that didn’t really make sense, but remnants of that attitude persisted. The third edition rectifies this. There is a chapter on statistical modeling techniques that explains linear and logistic regression, naive Bayes models, and more. There is also a brand new chapter on text mining, a curious omission from previous editions.

There is also a lot more material on data preparation. Three whole chapters are devoted to various aspects of data preparation. The first focuses on creating customer signatures. The second is focused on using derived variables to bring information to the surface, and the third deals with data reduction techniques such as principal components. Since this is where we spend the greatest part of our time in our work, it seemed important to spend more time on these subjects in the book as well.

Some of the chapters have been beefed up a bit. The neural network chapter now includes radial basis functions in addition to multi-layer perceptrons. The clustering chapter has been split into two chapters to accommodate new material on soft clustering, self-organizing maps, and more. The survival analysis chapter is much improved and includes material on some of our recent application of survival analysis methods to forecasting. The genetic algorithms chapter now includes a discussion of swarm intelligence.

Ajay- Describe your early career and how you came into Data Mining as a profession. What do you think of various universities now offering MS in Analytics. How do you balance your own teaching experience with your consulting projects at The Data Miners.

Michael- I fell into data mining quite by accident. I guess I always had a latent interest in the topic. As a high school and college student, I was a fan of Martin Gardner‘s mathematical games in in Scientific American. One of my favorite things he wrote about was a game called New Eleusis in which one players, God, makes up a rule to govern how cards can be played (“an even card must be followed by a red card”, say) and the other players have to figure out the rule by watching what plays are allowed by God and which ones are rejected. Just for my own amusement, I wrote a computer program to play the game and presented it at the IJCAI conference in, I think, 1981.

That paper became a chapter in a book on computer game playing–so my first book was about finding patterns in data. Aside from that, my interest in finding patterns in data lay dormant for years. At Thinking Machines, I was in the compiler group. In particular, I was responsible for the run-time system of the first Fortran Compiler for the CM-2 and I represented Thinking Machines at the Fortran 8X (later Fortran-90) standards meetings.

What changed my direction was that Thinking Machines got an export license to sell our first machine overseas. The machine went to a research lab just outside of Paris. The connection machine was so hard to program, that if you bought one, you got an applications engineer to go along with it. None of the applications engineers wanted to go live in Paris for a few months, but I did.

Paris was a lot of fun, and so, I discovered, was actually working on applications. When I came back to the states, I stuck with that applied focus and my next assignment was to spend a couple of years at Epsilon, (then a subsidiary of American Express) working on a database marketing system that stored all the “records of charge” for American Express card members. The purpose of the system was to pick ads to go in the billing envelope. I also worked on some more general purpose data mining software for the CM-5.

When Thinking Machines folded, I had the opportunity to open a Cambridge office for a Virginia-based consulting company called MRJ that had been a major channel for placing Connection Machines in various government agencies. The new group at MRJ was focused on data mining applications in the commercial market. At least, that was the idea. It turned out that they were more interested in data warehousing projects, so after a while we parted company.

That led to the formation of Data Miners. My two partners in Data Miners, Gordon Linoff and Brij Masand, share the Thinking Machines background.

To tell the truth, I really don’t know much about the university programs in data mining that have started to crop up. I’ve visited the one at NC State, but not any of the others.

I myself teach a class in “Marketing Analytics” at the Carroll School of Management at Boston College. It is an elective part of the MBA program there. I also teach short classes for corporations on their sites and at various conferences.

Ajay- At the previous Predictive Analytics World, you took a session on Forecasting and Predicting Subsciber levels (http://www.predictiveanalyticsworld.com/dc/2009/agenda.php#day2-6) .

It seems inability to forecast is a problem many many companies face today. What do you think are the top 5 principles of business forecasting which companies need to follow.

Michael- I don’t think I can come up with five. Our approach to forecasting is essentially simulation. We try to model the underlying processes and then turn the crank to see what happens. If there is a principal behind that, I guess it is to approach a forecast from the bottom up rather than treating aggregate numbers as a time series.

Ajay- You often partner your talks with SAS Institute, and your blog at http://blog.data-miners.com/ sometimes contain SAS code as well. What particular features of the SAS software do you like. Do you use just the Enterprise Miner or other modules as well for Survival Analysis or Forecasting.

Michael- Our first data mining class used SGI’s Mineset for the hands-on examples. Later we developed versions using Clementine, Quadstone, and SAS Enterprise Miner. Then, market forces took hold. We don’t market our classes ourselves, we depend on others to market them and then share in the revenue.

SAS turned out to be much better at marketing our classes than the other companies, so over time we stopped updating the other versions. An odd thing about our relationship with SAS is that it is only with the education group. They let us use Enterprise Miner to develop course materials, but we are explicitly forbidden to use it in our consulting work. As a consequence, we don’t use it much outside of the classroom.

Ajay- Also any other software you use (apart from SQL and J)

Michael- We try to fit in with whatever environment our client has set up. That almost always is SQL-based (Teradata, Oracle, SQL Server, . . .). Often SAS Stat is also available and sometimes Enterprise Miner.

We run into SPSS, Statistica, Angoss, and other tools as well. We tend to work in big data environments so we’ve also had occasion to use Ab Initio and, more recently, Hadoop. I expect to be seeing more of that.

Biography-

Together with his colleague, Gordon Linoff, Michael Berry is author of some of the most widely read and respected books on data mining. These best sellers in the field have been translated into many languages. Michael is an active practitioner of data mining. His books reflect many years of practical, hands-on experience down in the data mines.

Data Mining Techniques cover

Data Mining Techniques for Marketing, Sales and Customer Relationship Management

by Michael J. A. Berry and Gordon S. Linoff
copyright 2004 by John Wiley & Sons
ISB

Mining the Web cover

Mining the Web

by Michael J.A. Berry and Gordon S. Linoff
copyright 2002 by John Wiley & Sons
ISBN 0-471-41609-6

Non-English editions available in Traditional Chinese and Simplified Chinese

This book looks at the new opportunities and challenges for data mining that have been created by the web. The book demonstrates how to apply data mining to specific types of online businesses, such as auction sites, B2B trading exchanges, click-and-mortar retailers, subscription sites, and online retailers of digital content.

Mastering Data Mining

by Michael J.A. Berry and Gordon S. Linoff
copyright 2000 by John Wiley & Sons
ISBN 0-471-33123-6

Non-English editions available in JapaneseItalianTraditional Chinese , and Simplified Chinese

A case study-based guide to applying data mining techniques for solving practical business problems. These “warts and all” case studies are drawn directly from consulting engagements performed by the authors.

A data mining educator as well as a consultant, Michael is in demand as a keynote speaker and seminar leader in the area of data mining generally and the application of data mining to customer relationship management in particular.

Prior to founding Data Miners in December, 1997, Michael spent 8 years at Thinking Machines Corporation. There he specialized in the application of massively parallel supercomputing techniques to business and marketing applications, including one of the largest database marketing systems of the time.

UPDATE Twitter starts using Speedbit (powered by Yahoo-Bing?) for search

UPDATED Twitter is back to usual search results. It may have been a test of new (or newer twitter) which got live before it should be.

EARLIER POST

Unbelievable-!

Go to twitter-I did

http://twitter.com/invitations/find_on_twitter

and search for keywords

When you hit search you go to Speedbit, powered by Yahoo!

http://start.speedbit.com/search.aspx?site=suggest&cnl=7645705662&sitesearch=twitter.com&q=bara&bi=255309&si=7124&cc=IN&pop=0


More PAWS

Dr Eric Siegel  (interviewed here at https://decisionstats.wordpress.com/2009/07/14/interview_eric-siege/ )

continues his series of excellent analytical conferences-

Oct 19-20 – WASHINGTON DC: PAW Conference & Workshops (pawcon.com/dc)

Oct 28-29 – SAN FRANCISCO: Workshop (businessprediction.com)

Nov 15-16 – LONDON: PAW Conference & Workshop (pawcon.com/london)

March 14-15, 2011 – SAN FRANCISCO: PAW Conference & Workshops

* Register by Sep 30 for PAW London Early-Bird – Save £200
http://pawcon.com/london/register.php

* For the Oct 28-29 workshop, see http://businessprediction.com

———————–

INFORMATION ABOUT THE PAW CONFERENCES:

Predictive Analytics World ( http://pawcon.com ) is the business-focused event for predictive analytics professionals, managers and commercial practitioners, covering today’s commercial deployment of predictive analytics, across industries and across software vendors.

PAW delivers the best case studies, expertise, keynotes, sessions, workshops, exposition, expert panel, live demos, networking coffee breaks, reception, birds-of-a-feather lunches, brand-name enterprise leaders, and industry heavyweights in the business.

Case study presentations cover campaign targeting, churn modeling, next-best-offer, selecting marketing channels, global analytics deployment, email marketing, HR candidate search, and other innovative applications. The Conference agendas cover hot topics such as social data, text mining, search marketing, risk management, uplift (incremental lift) modeling, survey analysis, consumer privacy, sales force optimization and other innovative applications that benefit organizations in new and creative ways.

PAW delivers two rich conference programs in Oct./Nov. with very little content overlap featuring a wealth of speakers with front-line experience. See which one is best for you:

PAW’s DC 2010 (Oct 19-20) program includes over 25 sessions across two tracks – an “All Audiences” and an “Expert/Practitioner” track — so you can witness how predictive analytics is applied at 1-800-FLOWERS, CIBC, Corporate Executive Board, Forrester, LifeLine, Macy’s, MetLife, Miles Kimball, Monster, Paychex, PayPal (eBay), SunTrust, Target, UPMC Health Plan, Xerox, YMCA, and Yahoo!, plus special examples from the U.S. government agencies DoD, DHS, and SSA.

Sign up for event updates in the US http://pawcon.com/signup-us.php
View the agenda at-a-glance: http://pawcon.com/dc/2010/agenda_overview.php
For more: http://pawcon.com/dc
Register: http://pawcon.com/dc/register.php

PAW London 2010 (Nov 15-16) will feature over 20 speakers from 10 countries with case studies from leading enterprises in e-commerce, finance, healthcare, retail, and telecom such as Canadian Automobile Association, Chessmetrics, e-Dialog, Hamburger Sparkasse, Jeevansathi.com (India’s 2nd-largest matrimony portal), Life Line Screening, Lloyds TSB, Naukri.com (India’s number 1 job portal), Overtoom, SABMiller, Univ. of Melbourne, and US Bank, plus special examples from Anheuser-Busch, Disney, HP, HSBC, Pfizer, U.S. SSA, WestWind Foundation and others.

Sign up for event updates in the UK http://pawcon.com/signup-uk.php
View the agenda at-a-glance: http://pawcon.com/london/2010/agenda_overview.php
For more: http://pawcon.com/london
Register: http://pawcon.com/london/register.php

——————————-

PAW San Francisco Save-the-Date and Call-for-Speakers:

March 14-15, 2011
San Francisco Marriott Marquis
San Francisco, CA

PAW call-for-speakers information and submission form: (Due Oct 8)
http://www.predictiveanalyticsworld.com/submit.php

If you wish to receive periodic call-for-speakers notifications regarding Predictive Analytics World, email chair@predictiveanalyticsworld.com with the subject line “call-for-speakers notifications”.

Predictive Analytics World
http://www.predictiveanalyticsworld.com
Washington DC – London – San Francisco

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