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Michael Zeller

It is great to see more mathematical applications migrate to the cloud. This is one of the best opportunities where cloud computing can reduce cost and complexity of implementing computational efforts in HPC, large-scale simulations, and predictive analytics.

In a similar effort, we at Zementis (http://www.zementis.com ) launched our ADAPA predictive analytics decision engine on EC2 which allows users to deploy, integrate, and execute statistical scoring models, e.g., using algorithms like neural networks, support vector machine (SVM), decision tree, and various regression models.

As the model exchange format, we leverage the Predictive Model Markup Language (PMML) standard which is supported by commercial vendors like SPSS, SAS, IBM, Microstrategy, etc.
(see http://adapasupport.zementis.com/2008/10/list-of-pmml-consumers-and-producers.html ) as well as open source tools like R (http://adapasupport.zementis.com/2008/02/how-can-i-export-pmml-code-from-r.html ).

Please see the following review for a brief overview of ADAPA:

Buying predictive analytics like books - Zementis ADAPA
http://smartenoughsystems.com/wp/2008/06/20/buying-predictive-analytics-like-books-zementis-adapa/


Chirag Mehta

I see this as a second wave of the cloud computing. I have a detailed post on my blog at:

http://cloudcomputing.blogspot.com/2008/12/de-coupled-cloud-runtime-and-demand.html

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