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Solutions for IBM SPSS Users


Real-time and Big Data Scoring for IBM SPSS 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 IBM SPSS 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 IBM SPSS 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 IBM SPSS 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



IBM SPSS offers extensive support for PMML through IBM SPSS Modeler (formerly known as Clementine) and Statistics. Both systems allow users to export a multitude of models in PMML, for details click HERE).

IBM products such as DB2 Intelligent Miner and ILOG JRules also offer support for PMML. Zementis recently published an article on the IBM developer Works website explaining the process of exporting a model from IBM SPSS Statistics into PMML. The article describes all of the steps necessary to export data pre-processing as well as a neural network model. It also covers model deployment in the Zementis ADAPA Scoring Engine.

IBM developer Works article

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 IBM SPSS 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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