Ensuring safety and process reliabilty through open standards and predictive analytics

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.

Predictive maintenance solutions are based on the idea that one is able to know that a machine or equipment is going to fail, and take proactive actions to ensure process reliability and safety. By using data from sensors that capture vibration information from rotating equipment, my team built a predictive maintenance solution that alerted personnel of eminent breakdowns. For that, we used a combination of statistical tools. For example, we used R, an open-source statistical package for data analysis, IBM SPSS Statistics for analysis and model building, and the Zementis ADAPA platform for model deployment. Since all these systems support PMML, the Predictive Model Markup Language, instead of spending time translating code from one system to another, we were able to concentrate on the problem itself and use the tools we trusted the most to get the job done.


PMML is the de facto standard used to represent predictive analytic or data mining models. With PMML, a predictive solution may be built in one system and deployed in another where it can be put to work immediately. The adoption of PMML by the major analytic vendors is a testimony to their commitment to interoperability and the advancement of predictive analytics as a critical factor to the betterment of society. PMML is developed by the Data Mining Group (DMG), a committee composed not only by commercial and open-source analytic companies including IBM, SAS, Zementis, Microstrategy, KNIME and Rapid-I, but also by analytic users such as NASA, Visa, and Equifax.

In the wake of the gulf tragedy, predictive analytics and open standards 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.

We recently wrote a series of two articles for the IBM developerWorks website that covers PMML and predictive maintenance. The first article has just been published. To read it in its entirety, please refer to the following link:

What is PMML? Explore the power of predictive analytics and open standards

Predictive Analytics on a Massive Parallel Scale

As you probably know, Apache Hadoop is a Java-based framework that supports the parallel processing of large data sets in a distributed environment, to scale cost-effectively to thousands of servers and petabytes of data. Hadoop was inspired by Google's MapReduce which is a software framework patented by Google to support distributed computing for large data sets.

Now, imagine being able to execute predictive analytics on such a scale? And, guess what? It just became a reality.


Today, Datameer and Zementis announced a strategic partnership to help companies easily deploy, execute and integrate scalable standards-based predictive analytics.

Click here to read press release!

Datameer is a provider of big data analytics based on Hadoop. Their solution, called DAS (Datameer Analytics Solution), includes data source integration, storage, an analytics engine and visualization. With an ADAPA plug-in for DAS, the Zementis platform for predictive decisioning is now available for companies all around the globe to easily deploy, execute and integrate scalable standards-based predictive analytics on a massive scale.

ADAPA combines predictive analtyics and business rules to provide a true enterprise decision management system. Based on PMML, the Predictive Model Markup Language, ADAPA is able to execute predictive models as well as data pre- and post processing for solutions built in PMML compliant applications.

Conceived by the Data Mining Group (DMG), PMML is the de facto standard to represent predictive solutions. In fact, it is today supported by all the top commercial and open source analytics tools including IBM/SPSS, SAS, Microstrategy, KNIME, Rapid-I, Zementis ADAPA and several others (click here for a complete list).

Standards-based predictive analytics on a massive parallel scale is here!

PMML support is growing rapidly. From down under and into the stars!

Back in November 2009, I reported on PMML and Open Source Data Mining, highlighting the support open source tools offered for PMML.

It is obvious that open source tools for predictive analytics are gaining more and more momentum. Just last month, the KDNuggets.com website ran a poll which asked visitors to vote for the tools they had used for a real project in the last 12 months. The result ... more than 50% of the respondents said they used open-source tools such as R, KNIME, and RapidMiner. Now, it may be that the responses are really not that representative of the entire data mining community, but they do reflect a trend: open source data mining tools are here to stay and their use is growing as they become better and easier to use. And, guess what? Their support for PMML is stronger than ever.

Rapid-I has just released an extension for RapidMiner offering the export of PMML for several modeling techniques. KNIME continues to expand its PMML support with new capabilities ... and Weka has just announced support for Support Vector Machines (in addition to several other PMML elements). Augustus from Open Data supports segmented models expressed in PMML. The Zementis PMML Converter tool, which unifies the different versions of PMML into a single version is now also a corrector and it will soon support PMML 4.0.

There is also news from commercial vendors, Pervasive has announced support for PMML in DataRush. The Zementis ADAPA decisioning platform which is available as a service on the Amazon Cloud now offers the seamless integration of models expressed in PMML and business rules.

Last but not least, the Data Mining Group (DMG), which is responsible for developing PMML is constantly expanding. NASA has recently joined the DMG, so chances are PMML will grow out of this world and into the stars. According to the DMG website (dmg.org):

"The NASA Data Mining & Trending Working Group (DMTWG) was established to strengthen data/text mining and trending within and across NASA datasets, to aid in the identification and mitigation of adverse trends and to raise the awareness and capability of data/text mining within the agency. "

From down under, Togaware has also joined the DMG. Togaware's most well known product is Rattle, an open-source data mining tool built on top of R that produces and consumes PMML. Togaware maintains the PMML package for R which can be obtained from CRAN, the free "app store" for R users.

PMML Discussion Forums

For an on-going discussion and to read about the latest PMML news, feel free to join the PMML group in LinkedIn or the discussion forum in the PMML group on Analytic Bridge, a social network community for analytics professionals.

ADAPA is now available on the Asia-Pacific Cloud (in addition to the US and Europe)

ADAPA is a revolutionary scoring engine since it allows people anywhere at anytime to deploy and execute their predictive solutions (models + rules) as a Service on the Amazon Elastic Compute Cloud (EC2).

To address regulatory constraints and to bring the cloud closer to end-users, Amazon has been working hard in expanding its EC2 infrastructure to different regions of the globe. The latest cloud was recently launched in Singapore for the Asia-Pacific region.

Since Zementis is committed to bring the best experience to its ADAPA on the Cloud users, in the latest version of ADAPA, users can launch ADAPA instances (virtual machines running ADAPA) on the Amazon Asia-Pacific EC2 (in addition to the US and Europe).


Simply use the ADAPA Control Center to launch your instances in the region closest to you and your customers. By doing so, you insure shorter latency times.



Amazon also defines different "Availability Zones" for every region. Availability zones are distinct locations that are engineered to be insulated from failures in other availability zones. In this case, if you choose to launch a new ADAPA instance in "USA East (us-east-1a)", this will be available in the "USA East" cloud region, availability zone "1a" (as opposed to availability zones "1b" or "1c").

Zementis is here to revolutionize the world of predictive analytics. Join the revolution! Sign up for ADAPA on the Cloud today!

PMML in Action: A practical look at PMML, the standard to represent data mining models.

It is our pleasure to announce the publication of a new (first) book on PMML:


PMML (Predictive Model Markup Language) is the de facto standard used to represent and share predictive analytic solutions between applications. This enables data mining scientists and users alike to easily build, visualize, and deploy their solutions using different platforms and systems. This book presents PMML from a practical perspective. It contains a variety of code snippets so that concepts are made clear through the use of examples.

PMML in Action is a great way to learn how to represent your predictive models through a mature open standard. The book is divided into six parts, taking you in a PMML journey in which language elements and attributes are used to represent not only modeling techniques but also data transformations.

With PMML, users benefit from a single and concise standard to represent data and models, thus avoiding the need for custom code and proprietary solutions.

You too can join the PMML movement! Unleash the power of predictive analytics and data mining today! Available now on Amazon.com.

Reviews:

"The very first book that covers the industry standard for transferring and integrating predictive models across systems, this is a milestone for predictive analytics. If you want the long and short on engineering for versatility in how predictive models can be deployed and put to work, get started by curling up with this book."
Eric Siegel, Ph.D.
President, Prediction Impact, Inc.
Conference Chair, Predictive Analytics World

"Open standards facilitate innovation and progress (web is a great example). PMML (the Predictive Model Markup Language) is an open standard for predictive analytics and data mining, developed over more than 12 years and supported by most industry leaders. This easy to read book covers data transformations, many modeling methods (Associations, Clustering, Decision Trees, Neural Nets, Regression, SVM, and more), model ensembles, and verification. This book is your essential guide to PMML !"
Gregory Piatetsky, Ph.D.
Editor KDNuggets, Founder KDD/SIGKDD
KDNuggets.com

"Next generation enterprise are going to be driven by analytics, especially predictive analytics. Sharing and rapidly deploying predictive analytic models is essential and PMML is the open standard that delivers the interoperability and agility that these predictive enterprises need."
James Taylor
CEO, Decision Management Solutions
Co-author of “Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions ”
JTonEDM.com

“PMML in Action may be destined to become an analog to the famous Kernighan and Richie book, "The C Programming Language", published in 1978. This book (affectionately known as K&R) became the standard guide for ANSII C programming practice. I expect that "PMML in Action" will function likewise in the burgeoning development of PMML in analytical tools now, and in the future. It is the "cookbook" for PMML programming. Julia Child made French cuisine kiss-simple for housewives to create. Now, programmers can follow the descriptions and practices in this book to implement analytical solutions in PMML as easily and efficiently as Julia enabled a housewife to make a French soufflé."
Robert A. Nisbet, Ph.D.
Co-author of “Handbook of Statistical Analysis & Data Mining Applications”