Wednesday, November 14, 2012

Universal PMML Scoring for Teradata and Aster

Big Data and PMML, the Predictive Model Markup Language, are hot topics these days. But, when combined with in-database scoring, they take a new and powerful meaning. It is then no wonder that Zementis is thrilled to announce its partnership with Teradata, a global leader in data warehousing and analytics.

Teradata and Zementis

Zementis is pleased to announce that its Universal PMML Scoring Engine (UPPI) will soon be available on the Teradata and Aster databases.

Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in all the top commercial and open-source data mining vendors. Our products include the ADAPA Decisioning Engine for real-time scoring and UPPI, which is currently available for a host of database platforms as well as Hadoop/Datameer.

With UPPI for Teradata and UPPI for Aster, Zementis is expanding considerably the number of advanced platforms able to combine in-database scoring and data warehousing for rapid, on-the-fly predictive analytics on large volumes of data. 

UPPI for Teradata and UPPI for Aster enable analytic enterprises to realize significant business value from new business models and help companies drive both top-line revenue growth and bottom-line cost savings.
Check out the Zementis website for webinars, presentations and product data sheets and to learn more about in-database scoring with UPPI.

Thursday, November 8, 2012

Model Deployment with PMML, the Predictive Model Markup Language

The idea behind this demo is to show you how easy it is to operationally deploy a predictive solution once it is represented in PMML, the Predictive Model Markup Language.

As a model building environment, I use KNIME to generate a neural network model for predicting customer churn. Once data pre-processing and model are represented in PMML, I go on to deploy it in the Amazon Cloud using the ADAPA Scoring Engine and on top of Hadoop using the Universal PMML Plug-in (UPPI) for Datameer. So, the very same model is readily available for execution in two very distinct Big Data platforms: cloud and Hadoop.

The easy of model deployment and interoperability between platforms is the power of PMML, the de facto standard for predictive analytics and data mining models.


  1. Download the KNIME workflow used to generate a sample neural network for predicting churn
  2. Download the PMML file created during the demo

Tuesday, November 6, 2012

Big Data and Real-Time Scoring with ADAPA and the Universal PMML Plug-in

PMML, the Predictive Model Markup Language, allows for predictive models to be easily moved into production and operationally deployed on-site, in the cloud, in-database or Hadoop. Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in IBM SPSS, SAS, StatSoft STATISTICA, KNIME, SAP KXEN, R, etc. Our products include the ADAPA Scoring Engine and the Universal PMML Plug-in (UPPI). 


ADAPA, the Babylonian god of wisdom, is the first PMML-based, real-time predictive decisioning engine available on the market, and the first scoring engine accessible on the Amazon Cloud and IBM SmartCloud as a service. ADAPA on the Cloud combines the benefits of Software as a Service (SaaS) with the scalability of cloud computing. ADAPA is also available as a traditional software license for deployment on site.

As even the god of wisdom knows, not all analytic tasks are born the same. If one is confronted with massive volumes of data that need to be scored on a regular basis, in-database scoring sounds like the logical thing to do. In all likelihood, the data in these cases is already stored in a database and, with in-database scoring, there is no data movement. Data and models reside together; hence, scores and predictions flow at an accelerated pace. ADAPA’s sister product, the Universal PMML Plug-in (UPPI), is the Zementis solution for Hadoop and in-database scoring. UPPI is available for the IBM Netezza appliance, SAP Sybase IQ, and EMC Greenplum/Pivotal, Teradata and Teradata Aster. It is also available for Hadoop/Datameer. 


ADAPA and UPPI consume model files that conform to the PMML standard, version 2.0 through 4.2. If your model development environment exports an older version of PMML, our products will automatically convert your file into a 4.2 compliant format. 

Our products support an extensive collection of statistical and data mining algorithms. These include:
  • Neural Networks (Back-Propagation, Radial-Basis Function, and Neural-Gas) 
  • Regression Models (Linear, Polynomial, and Logistic)
  • General Regression Models (General Linear, Ordinal Multinomial, Generalized Linear, Cox) 
  • Support Vector Machines (for regression and multi-class and binary classification) 
  • Decision Trees (for classification and regression)
  • Scorecards (including support for reason codes and complex attributes) 
  • Association Rules 
  • Ruleset Models (flat Decision Trees)
  • Clustering Models (Distribution-Based, Center-Based, and 2-Step Clustering) 
  • Naive Bayes Classifiers 
  • Multiple Models (model composition, chaining, segmentation, and ensemble - including Random Forest Models and Stochastic Boosting)
A myriad of functions for implementing data pre- and post-processing are also supported, including:
  • Text Mining (introduced in PMML 4.2)
  • Regular Expressions
  • Value Mapping
  • Discretization
  • Normalization
  • Scaling
  • Logical and Arithmetic Operators
  • Conditional Logic
  • Built-in Functions
  • Lookup Tables
  • Business Decisions and Thresholds
  • Custom Functions ... and much much more

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