Data Mining

Big Data Analytics

Ilias Ortega — Mon, 07 May 2012 11:28:15 GMT

Read my article (in German) on "Big Data Analytics" in ComputerWorld, May 4 2012:

"Wissen als Wettbewerbsvorteil"

PDF article version attached.

"Real Time BI" in ComputerWoche

Ilias Ortega — Wed, 28 Mar 2012 16:32:14 GMT

Read my article (in German) on "Real Time Business Intelligence" in ComputerWoche, March 19 2012:

"Echtzeit-BI bringt Firmen Vorteile im Wettbewerb"

Online: http://www.computerwoche.de/software/bi-ecm/2507145/

Article page in Trinet: https://intranet.trivadis.com/wiki/bin/view/Trivadis/ErhoehungDerWettbewerbsfaehigkeitDurchRealTimeBusinessIntelligence2012Article

PDF version in Trinet: https://intranet.trivadis.com/wiki/pub/Trivadis/ErhoehungDerWettbewerbsfaehigkeitDurchRealTimeBusinessIntelligence2012Article/1203_Trivadis_Realtime_BI_CoWo_12_2012.pdf?t=1332946892

PDF article version attached.

Book Review - Super Crunchers: Why Thinking-By-Numbers is the New Way to be Smart

Ilias Ortega — Wed, 18 Nov 2009 12:03:00 GMT

Super Crunchers: Why Thinking-By-Numbers is the New Way to be Smart
Ian Ayres
Bantam
2007

At a little more than 200 pages, 'Super Crunchers' is an entertaining and informative book for generally interested readers with or without technical (i.e. on statistics or computer technology) background. The author, Ian Ayres, is a law and management professor at Yale Law and Management School.

In the book, Ayres tries to show that, in many cases, predictions are better carried out by computer-based algorithms (which he calls 'Super Crunchers') than by human experts. To make his point, Ayres presents a multitude of vignettes. He starts with Orley Oshenfelter, a quantitative economist who empirically came up with a simple regression formula to predict wine quality based on rainfall and temperature data. Even though wine critics rejected his formula, it made better predictions than the critics. The next example concerns Bill James, who developed a simple formula to predict the runs created by baseball players. The formula was intended to do the job of scouts, looking for big league baseball players.

Most examples in the book apply the statistical techniques of regression, randomized trials, and neuronal networks. Some of the examples covered concern firms that help people find matching partners through regression analysis. More interesting are the cases of firms like Offermatica, which found an original approach to randomly test web pages. Questionable is the case of Epagogyx, a firm that uses neuronal algorithms to predict the box office receipts of films based on the movies' scripts. Even though the author is overly enthusiastic about the future of computer based decision making, he presents interesting counterexamples from his own academical experience. There, he was involved in providing statistical evidence to support claims of racial and gender discrimination.

The book beautifully concludes with a valuable and intuitive explanation of the uses of the statistical concepts of mean and standard deviation that even the author's eight years old daughter understands, even though - according to the author - she is smart but no genius.

Welcome to the Data Mining Blog!

Ilias Ortega — Wed, 21 Oct 2009 08:09:00 GMT

The blog was conceived to exchange information, opinions, and - hopefully - answer questions on Data Mining.

Some of the topics covered by the blog are (the list is not conclusive):

Experience with Data Mining software; e.g. Oracle Data Mining, Oracle Crystal Ball, Microsoft SQL Server Data Mining, SAS Base, and SPSS
Use of open source software in Data Mining; e.g. R
Data Mining applications; e.g. fraud detection
Exchange of information on current trends, events (e.g. conferences), and publications related to Data Mining
Data Mining standards; e.g. PMML (Predictive Model Markup Language)