Wednesday, September 12, 2012

Predictive model deployment and execution made easy with PMML

Developed by the Data Mining Group (DMG), an independent, vendor led committee, PMML provides an open standard for representing data mining models. In this way, models can easily be shared between different applications avoiding proprietary issues and incompatibilities. Currently, all major commercial and open source data mining tools support PMML. These include IBM/SPSS, SAS, KXEN, TIBCO, STATISTICA, Microstrategy, R, KNIME, and RapidMiner (for a list of PMML-compliant tools, see of PMML-powered tools at

PMML is an XML-based language which follows a very intuitive structure to describe data pre- and post-processing as well as predictive algorithms. Not only does PMML represent a wide range of statistical techniques, but it can also be used to represent input data as well as the data transformations necessary to transform raw data into meaningful features.

PMML Conversion

Given that a tool may generate an older version of PMML (earlier than its latests), Zementis has worked out a way to convert older versions of PMML to its latest, version 4.1. This conversion proces is also used to validate a data mining model against the PMML specification for versions 2.0, 2.1, 3.0, 3.1, 3.2, 4.0 and 4.1. If validation is not successful, the conversion process gives back a file containing explanations for why the validation failed as comments embedded in the PMML file.

Before actual conversion takes place, the validation phase needs to be successful, i.e. the model file needs to conform to the PMML specification as published by the DMG (for any of the older PMML versions listed above). For known PMML issues (from a variety of sources/vendors), the conversion process will actually correct the model file so that it can be converted appropriately.

The ADAPA Decision Engine

If you are using the ADAPA Decision Engine (or any of our scoring products), the conversion process described above is automatically executed every time a PMML file is uploaded. By doing that, ADAPA understands PMML files generated by different vendors in all the different PMML versions. Besides syntactic validation, ADAPA also validates PMML from a semantic perspective.

And so, once a model is successfully uploaded in ADAPA, it is syntactically and semantically sound. For more details, click HERE.

You can benefit from ADAPA today by signing up for your private ADAPA instance on the Amazon Cloud or on the IBM SmartCloud. You can also sign up for the ADAPA free trial.

Start executing your models right now!

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