Zementis and Teradata Announce In-database Scoring for Big Data

As a result of its partnership with Teradata, Zementis is excited to announce the availability of the Universal PMML Plug-in (UPPI) for Teradata analytic platforms. It does not get easier than this! Simply deploy your predictive models built in R, IBM SPSS, SAS EM, ... and score your big data, directly in-database, where it resides.

The Zementis Universal PMML Plug-in (UPPI) enables the execution of standards-based predictive analytics directly within the Teradata Unified Data Architecture™. Users can now easily deploy predictive models built in R, IBM SPSS, SAS EM and other popular analytic tools on Aster and/or Teradata to achieve scale. The bridge between these systems is PMML, the Predictive Model Markup Language standard. It allows for models to be instantly moved from the scientist's desktop to the database where they will be executed.

As described by Teradata's Chris Twogood, VP for Product and Services Marketing, "by partnering with Zementis, we are able to offer high performance, enterprise-level predictive analytics scoring for the major analytics tools that support PMML. With Zementis and PMML, we are eliminating the need for customers to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today." Available for Teradata and Teradata Aster databases, UPPI leverages the massively parallel databases as a scalable, high-performance, scoring engine that easily processes through petabyte-scale data volumes. UPPI takes full advantage of the high-performance data warehouse with its massively parallel processing capabilities for rapid execution of standards-based predictive analytics based on the PMML standard. Models built in most commercial and open source data mining tools can now instantly be deployed in Teradata or Aster. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides. Read the full press release!

CIO Review: Zementis selected as one of the top 20 most promising big data companies

Selected by a distinguished panel comprising of CEOs, CIOs, VCs, industry analysts and the editorial board of CIO Review, Zementis has been named by CIO Review as one of the "Top 20 Most Promising Big Data Companies in 2013." Congratulations Zementis!

Read CIO Review - FULL ARTICLE

That comes as no surprise since Zementis is all about kicking down barriers for the fast deployment and execution of predictive solutions. By leveraging the PMML (Predictive Model Markup Language) standard, Zementis' products allow for predictive models built anywhere (IBM SPSS, KXEN, KNIME R, SAS, ...) to be deployed right-away on-site, in the cloud (Amazon, IBM, FICO), in-database (Pivotal/Greenplum, SAP Sybase IQ,  IBM PureData for Analytics/Netezza, Teradata and Teradata Aster) or in Hadoop (Hive or Datameer).

Predictive analytics has been used for many years to learn patterns from historical data to literally predict the future. Well known techniques include neural networks, decision trees, and regression models. Although these techniques have been applied to a myriad of problems, the advent of big data, cost-efficient processing power, and open standards have propelled predictive analytics to new heights.


Big data involves large amounts of structured and unstructured data that are captured from people (e.g., on-line transactions, tweets, ... ) as well as sensors (e.g., GPS signals in mobile devices). With big data, companies can now start to assemble a 360 degree view of their customers and processes. Luckily, powerful and cost-efficient computing platforms such as the cloud and Hadoop are here to address the processing requirements imposed by the combination of big data and predictive analytics.

Creating predictive solutions is just part of the equation. Once built, they need to be transitioned to the operational environment where they are actually put to use. In the agile world we live today, the Predictive Model Markup Language (PMML) delivers the necessary representational power for solutions to be quickly and easily exchanged between systems, allowing for predictions to move at the speed of business.  

Zementis' PMML-based products: ADAPA for real-time scoring and UPPI for big data scoring, are designed from the ground up to deliver the agility necessary for models to be easily deployed in a variety of platforms and to be put to work right-away. 

Zementis ADAPA and UPPI kick-down the barriers for big data adoption!

The Zementis Partnership with Infocom in Japan

It is our pleasure to announce a strategic partnership with Infocom. If you missed out on our press release, here is the headline:

Zementis and Infocom partner to deliver predictive analytic solutions in Japan.



Dedicated to the Japanese market, Infocom combines strong expertise in data mining and predictive analytics with extensive delivery and consulting capabilities.

Zementis offers software solutions that enable scalable, real-time execution of predictive analytics across a variety of platforms based on the PMML standard. These include the ADAPA Scoring Engine available for on-site deployment or in the cloud, and UPPI, the Universal PMML Plug-in for in-database scoring and Hadoop (available for IBM Netezza, Teradata/Aster, EMC Greenplum, SAP Sybase IQ as well as Hadoop and Datameer).

Infocom will market, distribute and support Zementis's predictive analytics software in Japan.

To take a look at the press release, click HERE.

Additional Online Resources

• Visit the Zementis resources pages for videos and articles on our products and PMML
• Follow @Zementis on Twitter
• Join the PMML discussion forum on LinkedIn

The Zementis Partnership with Teradata

The partnership between Zementis and Teradata allows customers with a variety of data mining tools to efficiently deploy predictive models based on the Predictive Model Markup Language (PMML) standard.  Focused on Big Data applications, the Universal PMML Plug-in (UPPI) for Teradata enables scalable execution of standards-based predictive analytics directly within the Teradata data warehouse.

To read more about the benefits of running your predictive solutions inside Teradata and Teradata Aster, please visit:

http://www.teradata.com/templates/Partners/PartnerProfile.aspx?id=12884902321

PMML Scoring

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 Scoring Engine for real-time scoring and UPPI, which is currently available for a host of database platforms as well as Hadoop/Datameer. For a list of available platforms, please visit our in-database products page.

Rationale

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 this case 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 on an accelerated pace

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.

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.

Amazing In-database Analytics with PMML and UPPI

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 on an accelerated pace.

A new day has come!

Zementis is now offering its amazing Universal PMML Plug-in™ (UPPI) for in-database scoring for the IBM Netezza appliance, SAP Sybase IQ, EMC Greenplum, Teradata and Teradata Aster.

Amazing! Why?

For starters, it won't break your budget (feel free to contact us for details). Also, it is simple to deploy and maintain. Our Universal PMML Plug-in was designed from the ground up to take advantage of efficient in-database execution. Last but not least, as its name suggests, it is PMML-based. PMML, the Predictive Model Markup Language is the standard for representing predictive models currently exported from all major commercial and open-source data mining tools. So, if you build your models in either SAS, IBM SPSS, STATISTICA, or R, you are ready to start benefiting from in-database scoring right away.

The PMML plugin seamlessly embeds models within your database. Data scoring requires nothing more than adding a simple function call into your SQL statements. You can score data against one model or against multiple models at the same time. There is no need to code complex data transformations and calculations in SQL or stored procedures. PMML and our Universal Plug-in can easily take care of that.

Modeling techniques currently supported are:

• Neural Networks
• Support Vector Machines
• Naive Bayes Classifiers
• Ruleset Models
• Clustering Models (including Two-Step Clustering)
• Decision Trees
• Regression Models (including Cox Regression Models)
• Scorecards (including reason codes)
• Association Rules
• Multiple Models (model composition, chaining, segmentation, and ensemble - including Random Forest models)

As well as extensive data pre- and post-processing capabilities.

In addition to all these predictive techniques, UPPI accepts PMML models of all versions (2.0, 2.1, 3.0, 3.1, 3.2, 4.0, 4.1 and 4.2) generated by any of the major commercial and open source mining tools (SAS, SPSS/IBM, STATISTICA, MicroStrategy, Microsoft, Oracle, KXEN, Salford Systems, TIBCO, R/Rattle, KNIME, RapidMiner, etc.). It does not get more universal than this!

Universal PMML Plug-in for EMC Greenplum Database

It is our pleasure to announce a new Zementis product, the Universal PMML Plug-in for in-database scoring. Available now for the EMC Greenplum Database, a high-performance massively parallel processing (MPP) database, the plug-in leverages the Predictive Model Markup Language (PMML) to execute predictive models directly within EMC Greenplum, for highly optimized in-database scoring.
Developed by the Data Mining Group (DMG), PMML is supported by all major data mining vendors, e.g., IBM SPSS, SAS, Teradata, FICO, STASTICA, Microstrategy, TIBCO and Revolution Analytics as well as open source tools like R, KNIME and RapidMiner. With PMML, models built in any of these data mining tools can now instantly be deployed in the EMC Greenplum database. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides.
"By partnering with Zementis, a true PMML innovator, we are able to offer a vendor-agnostic solution for moving enterprise-level predictive analytics into the database execution environment," said Dr. Steven Hillion, Vice President of Analytics at EMC Greenplum. "With Zementis and PMML, the de-facto standard for representing data mining models, we are eliminating the need to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today."

Want to learn more?

To learn more about how the EMC Greenplum Database and the Universal PMML Plug-in work together, feel free to:

• Visit the PMML Plug-in product page
• Download the white paper

The Universal PMML Plug-in for the EMC Greenplum Database is available now. Contact us today for more information.