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Welcome to the 17th issue of the Deploy! Newsletter. In this issue, we focus on a real-time recommender system in the context of social media as well as KNIME's extensive PMML capabilities.     


PMML, the Predictive Model Markup Language, is the de facto standard used by all the major statistical tools to represent predictive models. Recommender systems highlight the business benefits of operational predictive analytics, executing models in real-time to gain a competitive advantage in the market. Using KNIME for model development and our PMML-based ADAPA Scoring Engine for model deployment, PMML becomes the bridge between the data scientist and operational IT.   


KNIME is an excellent open source data mining platform and I encourage you to download the latest version which includes the PMML features described below. 




Kind regards,


Michael Zeller

CEO, Zementis Inc.  

Social Media, Recommendations, and Real-Time Execution with KNIME and ADAPA   


There is a lot of theory and hype around the topics of social media, recommendation engines and real time modeling, but until now not many practical examples that can be measured in terms of ROI. KNIME AG and Zementis have joined together to provide a white paper, which summarizes a practical case study that combines all three topics, and delivers a measured and solid business case.

Our case study is just one example as to how advanced analytics combined with real-time execution has real world benefits for organizations. Regardless of whether a requirement to control risk, increase personalization with the customer or maximize sales and margin exists, the combination of KNIME and ADAPA are ideal for leveraging the power of data by providing an end-to-end solution, from model development to operational deployment and real-time execution within any business process.  

Download our white-paper today: Social Media, Recommendation Engines and Real-Time Model Execution ...  
Issue: 17
PMML Support in KNIME     


What is KNIME? According to

KNIME (Konstanz Information Miner) is a use
r-friendly and comprehensive open-source data integration, processing, analysis, and exploration platform.

Yes, KNIME is user-friendly, not only because it offers an intuitive GUI to analyze data, but also because it is open-source. KNIME is also standards friendly. KNIME 2.0 released in 2008 was the first release to offer PMML support. Since then, PMML support in KNIME has matured considerably, from the import and export of predictive models all the way to the pre-processing of input variables. KNIME 2.5, released December 01, 2011 offers a series of PMML-enabled pre-processing nodes which can be embedded automatically in the final PMML model. All these features are documented in a paper presented at the KDD 2011 PMML Workshop:

The picture below shows part of a typical workflow in KNIME. Note that KNIME nodes now come with "blue" ports which signify PMML support. In this way, one can link a series of PMML-enabled pre-processing nodes to a model and obtain not only the model but also all the pre-processing steps in the resulting PMML file. 
Want to see more? Take a look at a step-by-step example of KNIME and PMML at work  

Whenever a PMML file is exported by KNIME, it can be directly deployed in any of the Zementis scoring products, including the ADAPA Scoring Engine or the Universal PMML Plug-in for in-database scoring. This enables models to be ready for operational use right away. 
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