Tag: Machine

Machine Learning Contest

New Contest at http://www.ecmlpkdd2011.org/dcOverview.php

 

 

Discovery Challenge Overview

Organization | Overview | Task and Datasets | Timeline

 

General description: tasks and dataset

VideoLectures.net is a free and open access multimedia repository of video lectures, mainly of research and educational character. The lectures are given by distinguished scholars and scientists at the most important and prominent events like conferences, summer schools, workshops and science promotional events from many fields of Science. The portal is aimed at promoting science, exchanging ideas and fostering knowledge sharing by providing high quality didactic contents not only to the scientific community but also to the general public. All lectures, accompanying documents, information and links are systematically selected and classified through the editorial process taking into account also users’ comments.

The ECML-PKDD 2011 Discovery Challenge is organized in order to improve the website’s current recommender system. The challenge consists of two main tasks and a “side-by” contest. The provided data is for both of the tasks, and it is up to the contestants how it will be used for learning (building up) a recommender.

Due to the nature of the problem, each of the tasks has its own merit: task 1 simulates new-user and new- item recommendation (cold-start mode), task 2 simulates clickstream based recommendation (normal mode). Continue reading “Machine Learning Contest”

Hacking Hackers

This is a ten step program to fight hacking attacks. You may or may not choose to ignore it, laugh at it, or ponder on it.

1) Internet security is a billion dollar business which will only grow in size as cloud computing approaches. Pioneers in providing security will earn considerable revenue like McAffee  , Norton did in the PC era. Incidentally it also means the consulting/partner group that is willing to work with virtual workers and virtual payments to offshore consultants.

2) Industrial espionage has existed from the days the West stole Gunpowder and Silk formula from China (and China is now doing the same to its software). The company and country will the best hackers will win. Keep your team motivated mate, or it is very easy for them to defect to the other side of the (cyber) wall.

3) When 2 billion people have access to internet the number of hackers will grow in number and quality much more rapidly than when only 100 million people across the world had access. Thanks to Google Translate, Paypal, Skype video Call, Tor Project, and Google Voice i can and have collaborative with hackers almost in all geographies. You can only imagine what the black hats are doing.

4) Analyzing hackers is like reading Chinese Tea Leaves. If you have experienced analysts, you will slip up. recruit the hackers in the dormitory before China recruits them using Lulz Security as a bogus cover. or USA recruits them as cover for spreading democracy in the Arab countries.

5) get your website audited for security breaches. sponsor a hack my website contest. before someone else does it for you.

6) Fighting hackers was always tough. But now we have part time hackers , people with perfectly respectable jobs who look like Mr Andersen and hack like Neo from the Matrix. Every kid once wanted to be a firefighter. Every geek dreams  of the one ultimate hack.

7) if you cant beat hackers, join them.

8) the more machine data is generated, the more you need external experts and newer software interfaces. Investing in open data, datasets is good. Keeping Bradley manning naked in his cell is bad. ignore the bad PR at your own cost.

9) Stop blaming China for every hack attack. You are a techie not a politician

10) Hack hard. Hack well. If someone hacks you, you will need to hack them off offensively unless you just want to be an easy mark for the rest of your lives. Counter -hacking expertise needs to be strengthened and groomed. hacking is an offense not just a defense game.

 

 

Amazon Ec2 goes Red Hat

message from Amazing Amazon’s cloud team- this will also help for #rstats users given that revolution Analytics full versions on RHEL.

—————————————————-

on-demand instances of Amazon EC2 running Red Hat Enterprise Linux (RHEL) for as little as $0.145 per instance hour. The offering combines the cost-effectiveness, scalability and flexibility of running in Amazon EC2 with the proven reliability of Red Hat Enterprise Linux.

Highlights of the offering include:

  • Support is included through subscription to AWS Premium Support with back-line support by Red Hat
  • Ongoing maintenance, including security patches and bug fixes, via update repositories available in all Amazon EC2 regions
  • Amazon EC2 running RHEL currently supports RHEL 5.5, RHEL 5.6, RHEL 6.0 and RHEL 6.1 in both 32 bit and 64 bit formats, and is available in all Regions.
  • Customers who already own Red Hat licenses will continue to be able to use those licenses at no additional charge.
  • Like all services offered by AWS, Amazon EC2 running Red Hat Enterprise Linux offers a low-cost, pay-as-you-go model with no long-term commitments and no minimum fees.

For more information, please visit the Amazon EC2 Red Hat Enterprise Linux page.

which is

Amazon EC2 Running Red Hat Enterprise Linux

Amazon EC2 running Red Hat Enterprise Linux provides a dependable platform to deploy a broad range of applications. By running RHEL on EC2, you can leverage the cost effectiveness, scalability and flexibility of Amazon EC2, the proven reliability of Red Hat Enterprise Linux, and AWS premium support with back-line support from Red Hat.. Red Hat Enterprise Linux on EC2 is available in versions 5.5, 5.6, 6.0, and 6.1, both in 32-bit and 64-bit architectures.

Amazon EC2 running Red Hat Enterprise Linux provides seamless integration with existing Amazon EC2 features including Amazon Elastic Block Store (EBS), Amazon CloudWatch, Elastic-Load Balancing, and Elastic IPs. Red Hat Enterprise Linux instances are available in multiple Availability Zones in all Regions.

Sign Up

Pricing

Pay only for what you use with no long-term commitments and no minimum fee.

On-Demand Instances

On-Demand Instances let you pay for compute capacity by the hour with no long-term commitments.

Region:US – N. VirginiaUS – N. CaliforniaEU – IrelandAPAC – SingaporeAPAC – Tokyo
Standard Instances Red Hat Enterprise Linux
Small (Default) $0.145 per hour
Large $0.40 per hour
Extra Large $0.74 per hour
Micro Instances Red Hat Enterprise Linux
Micro $0.08 per hour
High-Memory Instances Red Hat Enterprise Linux
Extra Large $0.56 per hour
Double Extra Large $1.06 per hour
Quadruple Extra Large $2.10 per hour
High-CPU Instances Red Hat Enterprise Linux
Medium $0.23 per hour
Extra Large $0.78 per hour
Cluster Compute Instances Red Hat Enterprise Linux
Quadruple Extra Large $1.70 per hour
Cluster GPU Instances Red Hat Enterprise Linux
Quadruple Extra Large $2.20 per hour

Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed are billed as full hours.

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and

Available Instance Types

Standard Instances

Instances of this family are well suited for most applications.

Small Instance – default*

1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small

Large Instance

7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large

Extra Large Instance

15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge

Micro Instances

Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.

Micro Instance

613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro

High-Memory Instances

Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.

High-Memory Extra Large Instance

17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge

High-Memory Double Extra Large Instance

34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge

High-Memory Quadruple Extra Large Instance

68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge

High-CPU Instances

Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.

High-CPU Medium Instance

1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium

High-CPU Extra Large Instance

7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge

Cluster Compute Instances

Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.

Cluster Compute Quadruple Extra Large Instance

23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge

Cluster GPU Instances

Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.

Cluster GPU Quadruple Extra Large Instance

22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge

 

Getting Started

To get started using Red Hat Enterprise Linux on Amazon EC2, perform the following steps:

  • Open and log into the AWS Management Console
  • Click on Launch Instance from the EC2 Dashboard
  • Select the Red Hat Enterprise Linux AMI from the QuickStart tab
  • Specify additional details of your instance and click Launch
  • Additional details can be found on each AMI’s Catalog Entry page

The AWS Management Console is an easy tool to start and manage your instances. If you are looking for more details on launching an instance, a quick video tutorial on how to use Amazon EC2 with the AWS Management Console can be found here .
A full list of Red Hat Enterprise Linux AMIs can be found in the AWS AMI Catalog.

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Support

All customers running Red Hat Enterprise Linux on EC2 will receive access to repository updates from Red Hat. Moreover, AWS Premium support customers can contact AWS to get access to a support structure from both Amazon and Red Hat.

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Resources

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About Red Hat

Red Hat, the world’s leading open source solutions provider, is headquartered in Raleigh, NC with over 50 satellite offices spanning the globe. Red Hat provides high-quality, low-cost technology with its operating system platform, Red Hat Enterprise Linux, together with applications, management and Services Oriented Architecture (SOA) solutions, including the JBoss Enterprise Middleware Suite. Red Hat also offers support, training and consulting services to its customers worldwide.

 

also from Revolution Analytics- in case you want to #rstats in the cloud and thus kill all that talk of RAM dependency, slow R than other softwares (just increase the RAM above in the instances to keep it simple)

,or Revolution not being open enough

http://www.revolutionanalytics.com/downloads/gpl-sources.php

GPL SOURCES

Revolution Analytics uses an Open-Core Licensing model. We provide open- source R bundled with proprietary modules from Revolution Analytics that provide additional functionality for our users. Open-source R is distributed under the GNU Public License (version 2), and we make our software available under a commercial license.

Revolution Analytics respects the importance of open source licenses and has contributed code to the open source R project and will continue to do so. We have carefully reviewed our compliance with GPLv2 and have worked with Mark Radcliffe of DLA Piper, the outside General Legal Counsel of the Open Source Initiative, to ensure that we fully comply with the obligations of the GPLv2.

For our Revolution R distribution, we may make some minor modifications to the R sources (the ChangeLog file lists all changes made). You can download these modified sources of open-source R under the terms of the GPLv2, using either the links below or those in the email sent to you when you download a specific version of Revolution R.

Download GPL Sources

Product Version Platform Modified R Sources
Revolution R Community 3.2 Windows R 2.10.1
Revolution R Community 3.2 MacOS R 2.10.1
Revolution R Enterprise 3.1.1 RHEL R 2.9.2
Revolution R Enterprise 4.0 Windows R 2.11.1
Revolution R Enterprise 4.0.1 RHEL R 2.11.1
Revolution R Enterprise 4.1.0 Windows R 2.11.1
Revolution R Enterprise 4.2 Windows R 2.11.1
Revolution R Enterprise 4.2 RHEL R 2.11.1
Revolution R Enterprise 4.3 Windows & RHEL R 2.12.2

 

 

 

RapidMiner launches extensions marketplace

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

Welcome to the Rapid-I Marketplace Public Beta Test

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.

Hot Downloads
«« « 1 2 3 » »»
[Icon]The Image Processing Extension provides operators for handling image data. You can extract attributes describing colour and texture in the image, you can make several transformation of a image data which allows you to perform segmentation and detection of suspicious areas in image data.The extension provides many of image transformation and extraction operators ranging from Wavelet Decomposition, Hough Circle to Block Difference of Inverse probabilities.
[Icon]RapidMiner is unquestionably the world-leading open-source system for data mining. It is available as a stand-alone application for data analysis and as a data mining engine for the integration into own products. Thousands of applications of RapidMiner in more than 40 countries give their users a competitive edge.

  • Data Integration, Analytical ETL, Data Analysis, and Reporting in one single suite
  • Powerful but intuitive graphical user interface for the design of analysis processes
  • Repositories for process, data and meta data handling
  • Only solution with meta data transformation: forget trial and error and inspect results already during design time
  • Only solution which supports on-the-fly error recognition and quick fixes
  • Complete and flexible: Hundreds of data loading, data transformation, data modeling, and data visualization methods
[Icon]All modeling methods and attribute evaluation methods from the Weka machine learning library are available within RapidMiner. After installing this extension you will get access to about 100 additional modelling schemes including additional decision trees, rule learners and regression estimators.This extension combines two of the most widely used open source data mining solutions. By installing it, you can extend RapidMiner to everything what is possible with Weka while keeping the full analysis, preprocessing, and visualization power of RapidMiner.
[Icon]Finally, the two most widely used data analysis solutions – RapidMiner and R – are connected. Arbitrary R models and scripts can now be directly integrated into the RapidMiner analysis processes. The new R perspective offers the known R console together with the great plotting facilities of R. All variables and R scripts can be organized in the RapidMiner Repository.A directly included online help and multi-line editing makes the creation of R scripts much more comfortable.
Related articles

#Rstats for Business Intelligence

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.

  • Use RMongo

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

  • First install RCurl and RJSONIO. You’ll have to download the tar.gz’s if you’re on a Mac. For the second part, we’ll need to installR4CouchDB,

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

 

Using R and Pentaho
Extending Pentaho with R analytics”R” is a popular open source statistical and analytical language that academics and commercial organizations alike have used for years to get maximum insight out of information using advanced analytic techniques. In this twelve-minute video, David Reinke from Pentaho Certified Partner OpenBI provides an overview of R, as well as a demonstration of integration between R and Pentaho.
and from
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.

Spotlight on R

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.
I think that the report generation package Brew would also qualify as a BI package, but large scale implementation remains to be seen in
a commercial business environment
  • brew: Creating Repetitive Reports
 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
  • Yarr- creating reports in R
to be continued ( when I have more time and the temperature goes down from 110F in Delhi, India)
Related articles

Interview- Top Data Mining Blogger on Earth , Sandro Saitta

Surajustement Modèle 2
Image via Wikipedia

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.

Ajay- Describe your journey as a scientist and data miner, from early experiences, to schooling to your work/research/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

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