Product data sheets, white-papers, and published articles in peer reviewed journals covering ADAPA, open-standards, and cloud computing.
Product Data Sheets
ADAPA: a predictive decisioning platform that combines predictive analytics, rules, open-standards, web-services and superior deployment capabilities to offer intelligent solutions that meet your enterprise challenges.
In-database Scoring - Universal PMML Plug-in for Sybase IQ
Zementis and Sybase help companies easily deploy, execute and integrate scalable standards-based predictive analytics. This joint solution combines the Zementis Universal PMML Plug-in for real-time execution of models with the power and scale of Sybase IQ, a highly optimized analytics server designed specifically to deliver superior performance for mission-critical business intelligence, analytics and data warehousing solutions on any standard hardware and operating system.
White Papers
Social Media, Recommendation Engines and Real-Time Model Execution with KNIME and ADAPA
There is a lot of theory and hype around the topics of social media, recommendation engines and real time modeling, but until now not many practical examples that can be measured in terms of ROI. KNIME AG and Zementis have joined together to provide this white paper, which summarizes a practical case study that combines all three topics, and delivers a measured and solid business case.
In-database Scoring - Universal PMML Plug-in for Greenplum Database
As advanced analytics becomes pervasive across the enterprise to drive better business decisions, the need for efficient execution of predictive models is paramount. Zementis and Greenplum joined forces to help companies easily bring predictive models into their database and score in-place and in-parallel huge amounts of data.
This joint product combines the Zementis Universal PMML Plug-in™ for execution of predictive models with the power and scale of the EMC Greenplum database. The result is an end-to-end solution that enhances Greenplum's large scale analytics processing capabilities with scoring of standards-based predictive models on a massively parallel architecture. By embedding predictive analytics directly in the database, this solution minimizes the movement of data and enables the efficient in-place processing of very large data sets.
In this whitepaper, we demonstrate how to deploy and execute predictive models and give specific examples using models built on IBM SPSS and the open source R program.
Integrating Real-Time Predictive Analytics into SAP Applications
In an article recently posted on the SAP Developer Network, Albrecht Weiss describes how to integrate predictive models into SAP applications. The article provides step-by-step instructions to deploy, integrate and execute predictive models based on SAP and ADAPA . First, a predictive model is built using the SAP BusinessObjects Predictive Workbench and exported into PMML. The PMML model is then easily deployed using ADAPA and details are given for executing the model using Web Services from inside SAP using ABAP.
Peer-reviewed Papers
SiGKDD Explorations 2009 - Focus on Interoperability
The latest SIGKDD Explorations newsletter is special in many ways. First, it is a special issue on open source analytics and the interoperability of analytic applications. Second, it features our article "Efficient Deployment of Predictive Analytics through Open Standards and Cloud Computing". In this article, we highlight the use of the PMML standard, which allows for predictive models to be easily exchanged between analytic applications. We also focus on cloud computing and Software as a Service and use ADAPA to illustrate how the benefits of open-standards and cloud computing can be combined.
The R newsletter has been transformed into The R Journal, a refereed journal for articles covering topics that are of interest to users or developers of R. The first issue is now available online. As a supporter of the R PMML Package, Zementis (together with Togaware) is honored that our article "PMML: An Open Standard for Sharing Models" has been selected by the editorial board to be published as part of the inaugural issue.
KDD 2009 - Panel on PMML and Cloud Computing
At KDD 2009 in Paris, the leading conference on Knowledge Discovery and Data Mining, a panel of experts discussed various topics related to open standards and cloud computing, with a particular focus on the practical use of statistical algorithms, reliable production deployment of models and the integration of predictive analytics within other systems. Moderated by Zementis, the panel was comprised of a distinguished group of thought leaders representing key software vendors in the data mining industry including DMG / Open Data Group, IBM, KNIME, KXEN, Microstrategy, Pervasive, SAS and SPSS. Please review the KDD 2009 Panel Report which summarizes questions and answers from the discussion.
KDD 2011 – Scorecard Element in PMML
This paper illustrates the dedicated Scorecard element introduced in the 4.1 specification of the PMML standard, including the various design and computational options available for returning reason codes alongside each computed score. The paper is intended to help both producers and consumers of scorecards as PMML documents.
KDD 2011 – In-Database Predictions using PMML
This paper discusses how PMML enables embedding advanced predictive models directly into the database or the data warehouse, along side the actual data to be scored. More importantly, it shows how one can easily take advantage of highly parallel database architectures to efficiently derive predictions from very large volumes of data.
KDD 2011 – PMML Converter and Transformations Generator
This paper describes the capabilities associated with the "PMML Converter". This application represents a great step in the PMML path towards true interoperability in data mining. Besides converting older versions of PMML to its latest, the PMML converter checks PMML files for syntax issues and, if issues are encountered, automatically corrects them.
This paper also describes the capabilities associated with an interactive PMML-based application, the "Transformations Generator." Auto-generated PMML code can omit important data pre-processing steps which are an integral part of a predictive solution. The Transformations Generator aims to bridge this gap by providing a graphical interface for the development and expression of data pre-processing steps in PMML.
KDD 2011 – PMML Pre-processing in KNIME
This paper describes PMML extensions for the modular open source data analytics platform KNIME adding pre-processing support and the ability to edit existing PMML code. The paper also shows how the PMML model representation in KNIME can be used within meta learning schemes such as boosting and bagging.
Invited Articles
What is PMML? Explore the Power of Predictive Analytics and Open Standards
Predictive analytics is an integral part of our daily lives. At this very moment, predictive solutions are busy at work, monitoring financial transactions for fraud and abuse, recommending movies and other products, or selecting the next best offer you will get from your favorite store. As much as it permeates our lives today, the application of predictive analytics is bound to increase, especially among data intensive situations and fields such as predictive maintenance. In the wake of the gulf tragedy, predictive maintenance can provide yet another tool for safe guarding operations and ensuring safety and process reliability. While predictive analytics can offer solutions to alert us of problems before they actually happen, open standards such as PMML are key ingredients for ensuring that the building and deployment of predictive maintenance solutions is application independent and so agile and transparent.
Please review the article we wrote on PMML and Predictive Maintenance for the IBM developerWorks website.
Representing Predictive Solutions in PMML: Move from Raw Data to Predictions
PMML, the Predictive Model Markup Language, is the de facto standard used to represent a myriad of predictive modeling techniques, such as Association Rules, Cluster Models, Neural Networks, and Decision Trees. These techniques empower companies around the globe to extract hidden patterns from data and use them to forecast behavior. In this article, start with a look at the predictive modeling techniques that are directly supported by the standard. However, given that a predictive solution is more than the statistical techniques it harbors, then dive even deeper into the language and explore the transformations and functions that are used for data processing by illustrating the use of data pre-processing and modeling in PMML as it is used to represent a complete predictive solution.
Please review the article we wrote on PMML and Predictive Solution for the IBM developerWorks website.
Predictive analytics in healthcare: The importance of open standards
As digital records and information become the norm in healthcare, it enables the building of predictive analytic solutions. These predictive models, when interspersed with the day to day operations of healthcare providers and insurance companies, have the potential to lower cost and improve the overall health of the population. As predictive models become more pervasive, the need for a standard, which can be used by all the parties involved in the modeling process: from model building to operational deployment, is paramount. The Predictive Model Markup Language (PMML), is such a standard. It allows for predictive solutions to be easily shared between applications and systems. This article describes the latest release of PMML, Version 4.1, and several ways it can be used to expedite the adoption and use of predictive solutions in the healthcare industry.
Please review the article we wrote on PMML and Healthcare for the IBM developerWorks website.