ADAPA Demo: Seamless Integration of Predictive Analytics and Business Rules

Organizations are looking to maximize the value of their analytics investment. They need to accelerate the deployment process, reduce costs and get the analytic insight where they need it, when they need it. Increasingly organizations must deploy and manage many models, use those models in real-time and integrate predictive analytics into a wide range of operational systems.

The Zementis ADAPA Decision Engine offers the perfect platform for moving complex solutions, which may include a combination of predictive analytics and business rules, from the development to the operational environment -- on the cloud, on-site, for Hadoop and in-database. 

In this demo, Dr. Alex Guazzelli, VP of Analytics at Zementis, shows a pre-qualification app that uses predictive models and rules to analyze the risk of mortgage default on loan applications. An application is accepted or referred for a variety of loan products depending on its perceived risk. ADAPA is the engine driving this application in the back-end.

Once logged in ADAPA, Dr. Guazzelli uses the ADAPA Web Console to download the mortgage solution files which are used throughout the demo. Predictive models expressed in PMML format are uploaded and verified in ADAPA along with rulesets expressed in tabular format. The ADAPA Web Console is used for managing predictive models, rulesets, and resource files as well as for batch-scoring. Real-time scoring is obtained via web-services or the Java API.

Finally, Dr. Guazzelli shows how the ADAPA Add-in for Excel is used to score data directly from within Excel. This part of the demo features the scoring of loan and tax data as well as the visualization of results via dashboards.

PMML-based In-database Scoring for Sybase IQ - Webinar Available!

View the on-demand replay of the webinar

In this webinar, Dr. Alex Guazzelli from Zementis and Courtney Claussen from Sybase, discuss predictive analytics in Healthcare - outlining a use case focusing on heart disease prevention. They examine how the Predictive Model Markup Language (PMML) enables data scientists to use a variety of tools to build their predictive models and then easily deploy them in Sybase IQ using the Universal PMML Plug-in. Once in Sybase IQ, models and predictions benefit from existing capabilities for text and multimedia analytics, which provide a breadth of techniques for analyzing big data.

The introductory piece is followed by a demo of the Zementis Universal PMML plug-in (UPPI) for Sybase IQ that allows in-database scoring of predictive models.

Further Resources:

1) Download the whitepaper: PMML: Accelerating the Time to Value for Predictive Analytics in the Big Data Era

2) Download the Product Data Sheet

PMML in Action - 2nd Edition is Out!

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ADAPA is now available on all Amazon Cloud regions

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In-database Scoring with PMML, Zementis, and Sybase IQ: Big Data Analytics Made Easy

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KNIME PMML Support: Model Import and Export + Pre-processing

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Operational Deployment of Predictive Solutions: Lost in Translation? Not with PMML

Traditionally, the deployment of predictive solutions have been, to put it mildly, cumbersome. As shown in the Figure below, data mining scientists work hard to analyze historical data and to build the best predictive solutions out it. Engineers, on the other hand, are usually responsible for bringing these solutions to life, by recoding them into a format suitable for production deployment. Given that data mining scientists and engineers tend to inhabit different information worlds, the process of moving a predictive solution from the scientist's desktop to production can get lost in translation.


Luckily, the advent of PMML (Predictive Model Markup Language) changed this scenario radically. PMML is the de facto standard used to represent predictive solutions. In this way, there is no need for scientists to write a word document describing the solution. They can just export it as a PMML file. Today, all major data mining tools and statistical packages support PMML. These include IBM SPSS, SAS, R, KNIME, RapidMiner, KXEN, ... Also, tools such as the Zementis Transformations Generator and KNIME allow for easy PMML coding for pre- and post-processing steps.

Great! Once a PMML file exists, it can be easily deployed in production with ADAPA, the Zementis scoring engine. ADAPA even allows for models to be deployed in the Amazon Cloud and be accessed from anywhere via web-services. Zementis also offers in-database scoring via its Universal PMML Plug-in, which is also available for Hadoop. In this way, a process that could take 6 months, now takes minutes.


PMML and ADAPA have transformed model deployment forever. If you or your company are still spending time and resources in deploying your predictive analytics the traditional way, make sure to contact us. The secret behind exceptional predictive analytics is out!

With PMML, interoperability is truly attainable

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 already 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 DMG.org).

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.

The PMML Converter

As part of the Data Mining Group, Zementis is committed to the continual development of PMML. It is our vision for the community that users will be free to share models among many solutions, benefiting from an environment in which interoperability is truly attainable.

In this spirit, Zementis has made available a tool called the PMML Converter which converts older versions of PMML to its latest, Version 4.0. The converter 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, and 4.0. If validation is not successful, the converter gives back a file containing explanations for why the validation failed (click on the "details" button).

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 PMML Converter will actually correct the model file so that it can be converted appropriately.

The PMML converter currently converts the following model elements to PMML 4.0:

• Association Rules
• Clustering Models
• Decision Trees
• General Regression Models Regression
• Naive Bayes Classifiers
• Neural Networks Regression Models
• Ruleset Models
• Support Vector Machines

It will also convert pre- and post-processing PMML elements.

The PMML Converter can be found in the Zementis PMML Tools page.

For more information on how to use the converter, please refer to the how-to guide.

The ADAPA Decision Engine

If you are using the ADAPA Decision Engine, there is no need to use the PMML Converter before uploading your models into the engine. That's because ADAPA encapsulates the PMML Converter. By doing that, it understands PMML files generated by different vendors in all the different PMML versions. ADAPA will actually take a step further than syntactic validation provided bythe PMML Converter, it will also validate PMML from a semantic perspective.

And so, once a model is successfully uploaded in ADAPA, it is syntactically and semantically sound.

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

Start executing your models right now!