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In-database Scoring - Universal PMML Plug-in for the EMC Greenplum Database

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As advanced analytics becomes pervasive across the enterprise to drive better business decisions, the need for efficient execution of predictive models is paramount. Zementis and Greenplum joined forces to help companies easily bring predictive models into their database and score in-place and in-parallel huge amounts of data.

This joint product combines the Zementis Universal PMML Plug-in™ for execution of predictive models with the power and scale of the EMC Greenplum database. The result is an end-to-end solution that enhances Greenplum's large scale analytics processing capabilities with scoring of standards-based predictive models on a massively parallel architecture. By embedding predictive analytics directly in the database, this solution minimizes the movement of data and enables the efficient in-place processing of very large data sets.

In this whitepaper, we demonstrate how to deploy and execute predictive models and give specific examples using models built on IBM SPSS and the open source R program.

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