Scoring Data from MySQL or SQL Server using KNIME and ADAPA

The video below shows the use of KNIME for handling data (reading data from a flat file and/or a database) as well as model building (data massaging and training a neural network). It also highlights how easy and straightforward it is to move a predictive model represented in PMML, the Predictive Model Markup Language, into the Zementis ADAPA Scoring Engine. ADAPA is then used for model deployment and scoring. PMML is the de facto standard to represent data mining models. It allows for predictive models to be moved between applications and systems without the need for model re-coding.

When training a model, scientists rely on historical data, but when using the model on a regular basis, the model is moved or deployed in production where it presented with new data. ADAPA provides a scalable and blazing fast scoring engine for models in production. And, although KNIME data mining nodes are typically used by scientists to build models, its database and REST nodes nodes can simply be used to create a flow for reading data from a database (MySQL, SQL Server, Oracle, ...) and passing it for scoring in ADAPA via its REST API.

 

Use-cases are:

1. Read data from a flat file, use KNIME for data pre-processing and building of a neural network model. Export the entire predictive workflow as a PMML file and then take this PMML file and upload and score it in ADAPA via its Admin Web Console. 
2. Read data from a database (MySQL, SQLServer, Oracle, ...), build model in KNIME, export model as a PMML file and deploy it in ADAPA using its REST API. This use-case also shows new or testing data flowing from the database and into ADAPA for scoring via a sequence of KNIME nodes. The video also shows a case in which one can use KNIME nodes to simply read a PMML file produced in any PMML-compliant data mining tool (R, SAS EM, SPSS, ...), upload it in ADAPA using the REST API and score new data from MySQL in ADAPA also through the REST interface. Note that in this case, the model has already been trained and we are just using KNIME to deploy the existing PMML file in ADAPA for scoring.

 

Spotlight on Zementis

Zementis, Inc. is a company that makes software for the operational deployment and integration of predictive analytics and data-mining solutions. Its main products are the ADAPA Decision Engine, a platform for statistics and data processing, and the Universal PMML Plug-in for Hadoop and in-database scoring.

The name Zementis, symbolizing "concrete thoughts", is derived from the German word Zement (cement, concrete) and the Latin word Mentis (thought, intellect) and relates to the company's core competence in machine learning and AI.

Road to ADAPA

Founded in 2004 with the goal of providing predictive analytics to the marketplace, Zementis is composed of two main divisions, analytics and engineering. Although it started as a company focused on building predictive models, Zementis scientists soon realized that their models needed a platform in which they could be easily deployed and managed. From this need, the ADAPA Decision Engine came to be.

ADAPA initially supported only neural networks, but it soon became a platform for the deployment of a myriad of statistical techniques as well as data processing (download the ADAPA Product Datasheet for a list of supported techniques). From its inception, ADAPA has been based on open-standards, including PMML, the Predictive Model Markup Language. As a member of the Data Mining Group (DMG), the committee defining PMML, Zementis has helped shaped the standard as it becomes the necessary vehicle for the sharing of predictive solutions between applications.

In 2008, ADAPA was launched as a service on the Amazon Elastic Compute Cloud (Amazon EC2) and is currently being used worldwide by companies and individuals who want to execute their predictive models and decision logic.

In 2012, ADAPA cloud offering was extended to the IBM SmartCloud. In this way, IBM provides companies around the world predictive decisions when and where they are needed.

Universal PMML Scoring Engine - UPPI

Building on the heritage of its ADAPA Decision Engine, Zementis launched the Universal PMML Plug-in (UPPI), a highly optimized, in-database scoring engine for predictive models, fully supporting the PMML standard. With PMML, UPPI delivers a wide range of predictive analytics for high performance scoring. It shortens time to market for predictive models and empowers users through instant deployment of predictive models. UPPI is available for the following DB platforms:

• EMC Greenplum
• IBM Netezza
• SAP Sybase IQ
• Teradata and Teradata Aster

The Universal PMML Scoring Engine is also available for Datameer for scoring in Hadoop.

Zementis Locations

Zementis HQ is located in San Diego in California. It also has an office in Hong Kong for servicing clients in the Asia-Pacific region.

References

• R. Nisbet, J. Elder, and G. Miner. Handbook of Statistical Analysis and Data Mining Applications. Academic Press, 2009.

• A. Guazzelli, M. Zeller, W. Lin, and G. Williams. PMML: An Open Standard for Sharing Models. The R Journal, Volume 1/1, May 2009.

• A. Guazzelli, K. Stathatos, M. Zeller. Efficient Deployment of Predictive Analytics through Open Standards and Cloud Computing. The ACM SIGKDD Explorations Newsletter, Volume 11/1, July 2009.

• A. Guazzelli, T. Jena, W. Lin, M. Zeller. The PMML Path Towards True Interoperability in Data Mining. In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 2011.

• A. Guazzelli, W. Lin, T. Jena (2012). PMML in Action (2nd Edition): Unleashing the Power of Open Standards for Data Mining and Predictive Analytics. CreateSpace.