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Solutions for STATISTICA Users


Real-time and Big Data Scoring for STATISTICA Users

Zementis ADAPA® for real-time scoring and UPPI™ for big data scoring provide additional value to all your predictive assets. Both solutions are complimentary to STATISTICA Modeler and Statistics, and extend your modeling environment into the IT operational domain. ADAPA and UPPI™ are compatible with Modeler and Statistics due to their use of PMML, the Predictive Model Markup Language, which is the de facto standard for representing predictive models. PMML allows for models to be developed in one application and deployed within another, as long as both applications are PMML-compliant.

Immediate Benefits of Using ADAPA or UPPI

Once a model built in any of the STATISTICA tools is saved as a PMML file, it can be directly deployed in ADAPA for real-time or batch scoring or in UPPI for scoring in-database or Hadoop. With ADAPA and UPPI, you can:

  • Execute your models independently of the STATISTICA model development tool
  • Overcome speed limitations
  • Dramatically lower your infrastructure cost
  • Benefit from using other PMML-compliant model development tools such as R, KNIME, or SAS
  • Deploy your models in minutes, not months (no need for recoding models into production)
  • Make one or many predictive models operational at once
  • Use multiple models to deploy a model ensemble, segmentation, chaining or cascade

With ADAPA for real-time scoring, you can:

  • Produce scores in real-time (using Web Services or Java API), on-demand, or batch-mode
  • Tap into all the advantages of cloud computing with ADAPA in the Cloud

With UPPI™ for big data scoring, you can:

  • Execute your models in-database, close to where your data resides with UPPI™ In-database (Greenplum/Pivotal, IBM Netezza, SAP Sybase IQ, Teradata and Teradata Aster)
  • Turn your models into UDFs (User-Defined Functions) and write SQL against UDFs
  • Dramatically lower your infrastructure cost
  • Execute your models in Hadoop with UPPI for Hive/Hadoop or Datameer



STATISTICA provides tools to build various types of PMML models using a graphical workflow. In STATISTICA a PMML document is built parallel to the data flow and results in a model that documents all the learning steps performed on the data.

STATISTICA provides support for the export of a wide variety of models in PMML format. They include Generalized Regression models, MARS regresion, k-means clustering, CHAID and CART trees, RandomForest, Boosted Trees, Naive Bayes and Neural Networks.


A Common Industry Standard

PMML allows for the de-coupling of two very important modeling phases: development and operational deployment. With PMML, data scientists can focus on data analysis and model building using best-of-breed model development tools, whereas operational deployment and actual use of the model is made extremely easy and simple with ADAPA and UPPI.

For example, if a data scientist develops a Neural Network Model using STATISTICA Modeler, all she needs to do to effectively deploy her model operationally is to save it as a PMML file and deploy it either in ADAPA for real-time scoring or in UPPI for big data scoring.

Once deployed, the model is available for all to use. ADAPA makes models available for scoring on-premise through ADAPA On-site or in the cloud through ADAPA in the Cloud. UPPI makes models available for scoring in-database or in Hadoop.

PMML allows for the model development environment to be used just for that, model development. Scoring, in real-time or for batch jobs, is handled by ADAPA or UPPI.


To speak with a Zementis representative and get more information about our predictive analytics solutions, please contact us.

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