Augustus- a PMML model producer and consumer. Scoring engine.

A Bold GNU Head
Image via Wikipedia

I just checked out this new software for making PMML models. It is called Augustus and is created by the Open Data Group ( , which is headed by Robert Grossman, who was the first proponent of using R on Amazon Ec2.

Probably someone like Zementis ( ) can use this to further test , enhance or benchmark on the Ec2. They did have a joint webinar with Revolution Analytics recently.

Recent News

  • Augustus v has been released
  • Added a guide (pdf) for including Augustus in the Windows System Properties.
  • Updated the install documentation.
  • Augustus 2010.II (Summer) release is available. This is v More information is here.
  • Added performance discussion concerning the optional cyclic garbage collection.

See Recent News for more details and all recent news.


Augustus is a PMML 4-compliant scoring engine that works with segmented models. Augustus is designed for use with statistical and data mining models. The new release provides Baseline, Tree and Naive-Bayes producers and consumers.

There is also a version for use with PMML 3 models. It is able to produce and consume models with 10,000s of segments and conforms to a PMML draft RFC for segmented models and ensembles of models. It supports Baseline, Regression, Tree and Naive-Bayes.

Augustus is written in Python and is freely available under the GNU General Public License, version 2.

See the page Which version is right for me for more details regarding the different versions.


Predictive Model Markup Language (PMML) is an XML mark up language to describe statistical and data mining models. PMML describes the inputs to data mining models, the transformations used to prepare data for data mining, and the parameters which define the models themselves. It is used for a wide variety of applications, including applications in finance, e-business, direct marketing, manufacturing, and defense. PMML is often used so that systems which create statistical and data mining models (“PMML Producers”) can easily inter-operate with systems which deploy PMML models for scoring or other operational purposes (“PMML Consumers”).

Change Detection using Augustus

For information regarding using Augustus with Change Detection and Health and Status Monitoring, please see change-detection.

Open Data

Open Data Group provides management consulting services, outsourced analytical services, analytic staffing, and expert witnesses broadly related to data and analytics. It has experience with customer data, supplier data, financial and trading data, and data from internal business processes.

It has staff in Chicago and San Francisco and clients throughout the U.S. Open Data Group began operations in 2002.


The above example contains plots generated in R of scoring results from Augustus. Each point on the graph represents a use of the scoring engine and a chart is an aggregation of multiple Augustus runs. A Baseline (Change Detection) model was used to score data with multiple segments.

Typical Use

Augustus is typically used to construct models and score data with models. Augustus includes a dedicated application for creating, or producing, predictive models rendered as PMML-compliant files. Scoring is accomplished by consuming PMML-compliant files describing an appropriate model. Augustus provides a dedicated application for scoring data with four classes of models, Baseline (Change Detection) ModelsTree ModelsRegression Models and Naive Bayes Models. The typical model development and use cycle with Augustus is as follows:

  1. Identify suitable data with which to construct a new model.
  2. Provide a model schema which proscribes the requirements for the model.
  3. Run the Augustus producer to obtain a new model.
  4. Run the Augustus consumer on new data to effect scoring.

Separate consumer and producer applications are supplied for Baseline (Change Detection) models, Tree models, Regression models and for Naive Bayes models. The producer and consumer applications require configuration with XML-formatted files. The specification of the configuration files and model schema are detailed below. The consumers provide for some configurability of the output but users will often provide additional post-processing to render the output according to their needs. A variety of mechanisms exist for transmitting data but user’s may need to provide their own preprocessing to accommodate their particular data source.

In addition to the producer and consumer applications, Augustus is conceptually structured and provided with libraries which are relevant to the development and use of Predictive Models. Broadly speaking, these consist of components that address the use of PMML and components that are specific to Augustus.

Post Processing

Augustus can accommodate a post-processing step. While not necessary, it is often useful to

  • Re-normalize the scoring results or performing an additional transformation.
  • Supplements the results with global meta-data such as timestamps.
  • Formatting of the results.
  • Select certain interesting values from the results.
  • Restructure the data for use with other applications.

Zementis partners with R Analytics Vendor- Revo

Logo for R
Image via Wikipedia

Just got a  PR email from Michael Zeller,CEO , Zementis annoucing Zementis (ADAPA) and Revolution  Analytics just partnered up.

Is this something substantial or just time-sharing or a Barney Partnership (

Summary- Thats cloud computing scoring of models on EC2 (Zementis) partnering with the actual modeling software in R (Revolution Analytics RevoDeployR)

See previous interviews with both Dr Zeller at , and

and Revolution guys at


strategic partnership with Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language. With this partnership, predictive models developed on Revolution R Enterprise are now accessible for real-time scoring through the ADAPA Decisioning Engine by Zementis. 

ADAPA is an extremely fast and scalable predictive platform. Models deployed in ADAPA are automatically available for execution in real-time and batch-mode as Web Services. ADAPA allows Revolution R Enterprise to leverage the Predictive Model Markup Language (PMML) for better decision management. With PMML, models built in R can be used in a wide variety of real-world scenarios without requiring laborious or expensive proprietary processes to convert them into applications capable of running on an execution system.


“By partnering with Zementis, Revolution Analytics is building an end-to-end solution for moving enterprise-level predictive R models into the execution environment,” said Jeff Erhardt, Revolution Analytics Chief Operation Officer. “With Zementis, we are eliminating the need to take R applications apart and recode, retest and redeploy them in order to obtain desirable results.”


Got demo? 

Yes, we do! Revolution Analytics and Zementis have put together a demo which combines the building of models in R with automatic deployment and execution in ADAPA. It uses Revolution Analytics’ RevoDeployR, a new Web Services framework that allows for data analysts working in R to publish R scripts to a server-based installation of Revolution R Enterprise.

Action Items:

  2. DOWNLOAD the white paper

RevoDeployR & ADAPA allow for real-time analysis and predictions from R to be effectively used by existing Excel spreadsheets, BI dashboards and Web-based applications, all in real-time.

RevoADAPAPredictive analytics with RevoDeployR from Revolution Analytics and ADAPA from Zementis put model building and real-time scoring into a league of their own. Seriously!

Scoring SAS and SPSS Models in the cloud

Outline of a cloud containing text 'The Cloud'
Image via Wikipedia

An announcement from Zementis and Predixion Software– about using cloud computing for scoring models using PMML. Note R has a PMML package as well which is used by Rattle, data mining GUI for exporting models.



ALISO VIEJO, Calif., Oct 19, 2010 (BUSINESS WIRE) — Predixion Software today introduced Predixion PMML Connexion(TM), an interface that provides Predixion Insight(TM), the company’s low-cost, self-service in the cloud predictive analytics solution, direct and seamless access to SAS, SPSS (IBM) and other predictive models for use by Predixion Insight customers. Predixion PMML Connexion enables companies to leverage their significant investments in legacy predictive analytics solutions at a fraction of the cost of conventional licensing and maintenance fees.

The announcement was made at the Predictive Analytics World conference in Washington, D.C. where Predixion also announced a strategic partnership with Zementis, Inc., a market leader in PMML-based solutions. Zementis is exhibiting in Booth #P2.

The Predictive Model Markup Language (PMML) standard allows for true interoperability, offering a mature standard for moving predictive models seamlessly between platforms. Predixion has fully integrated this PMML functionality into Predixion Insight, meaning Predixion Insight users can now effortlessly import PMML-based predictive models, enabling information workers to score the models in the cloud from anywhere and publish reports using Microsoft Excel(R) and SharePoint(R). In addition, models can also be written back into SAS, SPSS and other platforms for a truly collaborative, interoperable solution.

“Predixion’s investment in this PMML interface makes perfect business sense as the lion’s share of the models in existence today are created by the SAS and SPSS platforms, creating compelling opportunity to leverage existing investments in predictive and statistical models on a low-cost cloud predictive analytics platform that can be fed with enterprise, line of business and cloud-based data,” said Mike Ferguson, CEO of Intelligent Business Strategies, a leading analyst and consulting firm specializing in the areas of business intelligence and enterprise business integration. “In this economy, Predixion’s low-cost, self-service predictive analytics solutions might be welcome relief to IT organizations chartered with quickly adding additional applications while at the same time cutting costs and staffing.”

“We are pleased to be partnering with Zementis, truly a PMML market leader and innovator,” said Predixion CEO Simon Arkell. “To allow any SAS or SPSS customer to immediately score any of their predictive models in the cloud from within Predixion Insight, compare those models to those created by Predixion Insight, and share the results within Excel and Sharepoint is an exciting step forward for the industry. SAS and SPSS customers are fed up with the high prices they must pay for their business users just to access reports generated by highly skilled PhDs who are burdened by performing routine tasks and thus have become a massive bottleneck. That frustration is now a thing of the past because any information worker can now unlock the power of predictive analytics without relying on experts — for a fraction of the cost and from anywhere they can connect to the cloud,” Arkell said.

Dr. Michael Zeller, Zementis CEO, added, “Our mission is to significantly shorten the time-to-market for predictive models in any industry. We are excited to be contributing to Predixion’s self-service, cloud-based predictive analytics solution set.”

About Predixion Software

Predixion Software develops and markets collaborative predictive analytics solutions in the public and private cloud. Predixion enables self-service predictive analytics, allowing customers to use and analyze large amounts of data to make actionable decisions, all within the familiar environment of Excel and PowerPivot. Predixion customers are achieving immediate results across a multitude of industries including: retail, finance, healthcare, marketing, telecommunications and insurance/risk management.

Predixion Software is headquartered in Aliso Viejo, California with development offices in Redmond, Washington. The company has venture capital backing from established investors including DFJ Frontier, Miramar Venture Partners and Palomar Ventures. For more information please contact us at 949-330-6540, or visit us

About Zementis

Zementis, Inc. is a leading software company focused on the operational deployment and integration of predictive analytics and data mining solutions. Its ADAPA(R) decision engine successfully bridges the gap between science and engineering. ADAPA(R) was designed from the ground up to benefit from open standards and to significantly shorten the time-to-market for predictive models in any industry. For more information, please visit


Event: Predictive analytics with R, PMML and ADAPA


The September meeting is at the Oracle campus. (This is next door to the Oracle towers, so there is plenty of free parking.) The featured talk is from Alex Guazzelli (Vice President – Analytics, Zementis Inc.) who will talk about “Predictive analytics with R, PMML and ADAPA”.

* 6:15 – 7:00 Networking and Pizza (with thanks to Revolution Analytics)
* 7:00 – 8:00 Talk: Predictive analytics with R, PMML and ADAPA
* 8:00 – 8:30 General discussion

Talk overview:

The rule in the past was that whenever a model was built in a particular development environment, it remained in that environment forever, unless it was manually recoded to work somewhere else. This rule has been shattered with the advent of PMML (Predictive Modeling Markup Language). By providing a uniform standard to represent predictive models, PMML allows for the exchange of predictive solutions between different applications and various vendors.

Once exported as PMML files, models are readily available for deployment into an execution engine for scoring or classification. ADAPA is one example of such an engine. It takes in models expressed in PMML and transforms them into web-services. Models can be executed either remotely by using web-services calls, or via a web console. Users can also use an Excel add-in to score data from inside Excel using models built in R.

R models have been exported into PMML and uploaded in ADAPA for many different purposes. Use cases where clients have used the flexibility of R to develop and the PMML standard combined with ADAPA to deploy range from financial applications (e.g., risk, compliance, fraud) to energy applications for the smart grid. The ability to easily transition solutions developed in R to the operational IT production environment helps eliminate the traditional limitations of R, e.g. performance for high volume or real-time transactional systems and memory constraints associated with large data sets.

Speaker Bio:

Dr. Alex Guazzelli has co-authored the first book on PMML, the Predictive Model Markup Language which is the de facto standard used to represent predictive models. The book, entitled PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics, is available on As the Vice President of Analytics at Zementis, Inc., Dr. Guazzelli is responsible for developing core technology and analytical solutions under ADAPA, a PMML-based predictive decisioning platform that combines predictive analytics and business rules. ADAPA is the first system of its kind to be offered as a service on the cloud.
Prior to joining Zementis, Dr. Guazzelli was involved in not only building but also deploying predictive solutions for large financial and telecommunication institutions around the globe. In academia, Dr. Guazzelli worked with data mining, neural networks, expert systems and brain theory. His work in brain theory and computational neuroscience has appeared in many peer reviewed publications. At Zementis, Dr. Guazzelli and his team have been involved in a myriad of modeling projects for financial, health-care, gaming, chemical, and manufacturing industries.

Dr. Guazzelli holds a Ph.D. in Computer Science from the University of Southern California and a M.S and B.S. in Computer Science from the Federal University of Rio Grande do Sul, Brazil.

Interview Michael Zeller,CEO Zementis on PMML

Here is a topic specific interview with Micheal Zeller of Zementis on PMML, the de facto standard for data mining.


Ajay- What is PMML?

Mike- The Predictive Model Markup Language (PMML) is the leading standard for statistical and data mining models and supported by all leading analytics vendors and organizations. With PMML, it is straightforward to develop a model on one system using one application and deploy the model on another system using another application. PMML reduces complexity and bridges the gap between development and production deployment of predictive analytics.

PMML is governed by the Data Mining Group (DMG), an independent, vendor led consortium that develops data mining standards

Ajay- Why can PMML help any business?

Mike– PMML ensures business agility with respect to data mining, predictive analytics, and enterprise decision management. It provides one standard, one deployment process, across all applications, projects and business divisions. In this way, business stakeholders, analytic scientists, and IT are finally speaking the same language.

In the current global economic crisis more than ever, a company must become more efficient and optimize business processes to remain competitive. Predictive analytics is widely regarded as the next logical step, implementing more intelligent, real-time decisions across the enterprise.

However, the deployment of decisions based on predictive models and statistical algorithms has been a hurdle for many companies. Typically, it has been a complex, costly process to get such models integrated into operational systems. With the PMML standard, this no longer is the case. PMML simply eliminates the deployment complexity for predictive models.

A standard also provides choices among vendors, allowing us to implement best-of-breed solutions, and creating a common knowledge framework for internal teams – analytics, IT, and business – as well external vendors and consultants. In general, having a solid standard is a sign of a mature analytics industry, creating more options for users and, most importantly, propelling the total analytics market to the next level.

Ajay- Can PMML help your existing software in analytics and BI?

Mike- PMML has been widely accepted among vendors, almost all major analytics and business intelligence vendors already support the standard. If you have any such software package in-house, you most likely have PMML at your disposal already.

For example, you can develop your models in any of the tools that support PMML, e.g., SPSS, SAS, Microstrategy, or IBM, and then deploy that model in ADAPA, which is the Zementis decision engine. Or you can even choose from various open source tools, like R and KNIME.


Ajay- How does Zementis and ADAPA and PMML fit?

Mike- Zementis has been a avid supporter of the PMML standard and is very active in the development of the standard. We contributed to the PMML package for the open source R Project. Furthermore, we created a free PMML Converter tool which helps users to validate and correct PMML files from various vendors and convert legacy PMML files to the latest version of the standard.

Most prominently with ADAPA, Zementis launched the first cloud-computing scoring engine on the Amazon EC2 cloud. ADAPA is a highly scalable deployment, integration and execution platform for PMML-based predictive models. Not only does it give you all the benefits of being fully standards-based, using PMML and web services, but it also leverages the cloud for scalability and cost-effectiveness.

By being a Software as a Service (SaaS) application on Amazon EC2, ADAPA provides extreme flexibility, from casual usage which only costs a few dollars a month all the way to high-volume mission critical enterprise decision management which users can seamlessly launch in the United States or in European data centers.

Ajay- What are some examples where PMML helped companies save money?

Mike- For any consulting company focused on developing predictive analytics models for clients, PMML provides tremendous benefits, both for clients and service provider. In standardizing on PMML, it defines a clear deliverable – a PMML model – which clients can deploy instantly. No fixed requirements on which specific tools to choose for development or deployment, it is only important that the model adheres to the PMML standard which becomes the common interface between the business partners. This eliminates miscommunication and lowers the overall project cost. Another example is where a company has taken advantage of the capability to move models instantly from development to operational deployment. It allows them to quickly update models based on market conditions, say in the area of risk management and fraud detection, or to roll out new marketing campaigns.

Personally, I think the biggest opportunities are still ahead of us as more and more businesses embrace operational predictive analytics. The true value of PMML is to facilitate a real-time decision environment where we leverage predictive models in every business process, at every customer touch point and on-demand to maximize value

Ajay- Where can I find more information about PMML?

Mike- First there is the Data Mining Group (DMG) web site at

I strongly encourage any company that has a significant interest in predictive analytics to become a member and help drive the development of the standard.

We also created a knowledge base of PMML-related information at and there is a PMML interest group on Linked


This group is more geared toward a general discussion forum for business benefits and end-user questions, and it is a great way to get started with PMML.

Last but not least, the Zementis web site at

It contains various PMML example files, the PMML Converter tool, as well links to PMML resource pages on the web.

For more on Michael Zeller and Zementis read his earlier interview at

Interview Ron Ramos, Zementis

 HeadShot Here is an interview with Ron Ramos, Director , Zementis. Ron Ramos wants to use put predictions for the desktop and servers to the remote  cloud using Zementis ADAPA scoring solution. I have tested the ADAPA solution myself and made some suggestions on tutorials. Zementis is a terrific company with a great product ADAPA and big early mover advantage ( see for the Zementis 5 minute video and earlier interview a few months back with Michael Zeller, a friend, and CEO of Zementis. )

Ajay- Describe your career journey. How would you motivate your children or young people to follow careers in science or at least to pay more attention to science subjects. What advice would you give to young tech entrepreneurs in this recession- the ones chasing dreams on iMobile Applications, cloud computing etc.

Ron- Science and a curious mind go together. I remember when I first met a friend of mine who is a professor of cognitive sciences at the University of California. To me, he represents the quest for scientific knowledge. Not only has he been studying visual space perception, visual control of locomotion, and spatial cognition, but he is also interested in every single aspect of the world around him. I believe that if we are genuinely interested and curious to know how and why things are the way they are, we are a step closer into appreciating and willing to participate in the collective quest for scientific knowledge.

Our current economic troubles are not affecting a single industry. The problem is widespread. So, tech entrepreneurs should not view this recession as target towards technology. It is new technology in clean, renewable fuels which will most probably define what is to come. I am also old enough to know that everything is cyclical and so, this recession will lead us to great progress. iMobile Applications and Cloud Computing are here to stay since these are technologies that just make sense. Cloud Computing benefits from the pay-as-you-go model, which because of its affordability is bound to allow for the widespread use and availability of computing where we have not seen before.

The most interesting and satisfying effect one can have is transformation – do that which changes people’s lives, and your own at the same time.  I like the concept of doing well and doing good at the same time.  My emphasis has always marketing and sales in every business in which I have been involved.  ADAPA provides for delivering on the promise of predictive analytics – decisioning in real-time.

Ajay-  How do you think Cloud Computing will change the modeling deployment market by 2011. SAS Institute is also building a 70 million dollar facility for private clouds. Do you think private clouds with tied in applications would work.

Ron- Model deployment in the cloud is already a reality. By 2011, we project that most models will be deployed in the cloud (private or not). With time though, private clouds will most probably need to embrace the use of open standards such as PMML. I believe open standards such as PMML, which allows for true interoperability, will become widespread among the data mining community; be used in any kind of computing environment; and, be moved from cloud to cloud.

Ajay- I am curious- who is Zementis competition in cloud deployed models. Where is ADAPA deployment NOT suitable for scoring models – what break off point does size of data make people realize that cloud is better than server. Do you think Internal Organization IT Support teams fear cloud vendors would take their power away.

Ron- Zementis is the first and only company to provide a scoring engine on the cloud. Other data mining companies have announced their intention to move to cloud computing environments. The size of the data you need to score is not something that should be taken into account for determining if scoring should be done in the cloud or not. In ADAPA, models can uploaded and managed through an intuitive web console and all virtual machines can be launched or terminated with the click of a mouse. Since ADAPA instances run from $0.99/hour, it can appeal to small and large scoring jobs. For small, the cost is minimal and deployment of models is fast. For large, the cloud offers scalability. Many ADAPA instances can be set to run at the same time.


Cloud computing is changing the way models are deployed, but all organizations still need to manage their data and so IT can concentrate on that. Scoring on the cloud makes the job of IT easier.

Ajay- Which is a case where ADAPA deployment is not suited. Software like from KXEN offers model export into many formats like PMML, SQL, C++ , SAS etc. Do you think Zementis would be benefited if it had such a converter like utility/collection of utilities on its site for the PMML conversion say from SAS code to PMML code etc. Do you think PMML is here to stay for a long time.

Ron- Yes, PMML is here to stay. Version 4.0 is about to be release. So, this is a very mature standard embraced by all leading data mining vendors. I believe the entire community will benefit from having converters to PMML, since it allows for models to be represented by an open and well documented standard. Also, since different tools already import and export PMML, data miners and modelers are the set free to move their models around. True interoperability!

Ajay – Name some specific customer success stories and costs saved.

Ron – As a team, we spent our early development time working on assignments in the mortgage business.  That’s what gave rise to the concept of ADAPA – enabling smart decisions as an integral part of the overall business strategy.  It became obvious to us that we were in fact totally horizontal with application in any industry that had knowledge to be gained from its data.  If only they could put their artful predictive models to work – easily integrated and deployed, able to be invoked directly from the business’ applications using web services, with returned results downloaded for further processing and visualization.  There is no expensive upfront investment in software licenses and hardware; no long-term extended implementation and time-to-production.  The savings are obvious, the ROI pyrotechnic.

Our current users, both enterprise installations and Amazon EC2 subscribers report great results, and for a variety of good reasons we tend to respect their anonymity:

Zementis ADAPA Case Study #1:

Financial Institution Embraces Real-time Decisions.

Decision Management:  A leading financial company wanted to implement an enterprise-wide decision system to automate credit decisions across Retail, Wholesale, and Correspondent business channels. A key requirement for the companys Enterprise strategy was to select a solution which could execute and manage rules as well as predictive analytic
s on demand and in real-time. With minimal prior automation in place, the challenge was to execute guidelines and pricing for a variety of business scenarios. Complex underwriting and intricate pricing matrices combined present obstacles for employees and customers in correctly assessing available choices from a myriad of financial products. Although embracing a new processing paradigm, the goal for integration of the solution with the existing infrastructure also was to ensure minimal impact to already established processes and to not jeopardize origination volume.

Following a comprehensive market review, the financial institution selected the Zementis ADAPA Enterprise Edition because of its key benefits as a highly scalable decision engine based on open standards. The ADAPA framework, they concluded, ensures real-time execution capabilities for rules and predictive analytics across all products and all business channels.

Working directly with senior business and IT management, Zementis efficiently executed on an iterative deployment strategy which enabled the joint project team to roll out a comprehensive Retail solution in less than three months. Accessed in Retail offices across the country, the ADAPA decision engine assists more than 700 loan officers to determine eligibility of a borrower with the system instantly displaying conditions or exceptions to guidelines as well as precise pricing for each scenario. The Wholesale division exposes the ADAPA decision engine to a large network of several thousand independent brokers who explore scenarios and submit their applications online. While rules were authored in Excel format, a favorite of many business users, predictive models were developed in various analytics tools and deployed in ADAPA via the Predictive Model Markup Language (PMML) standard. Extending its value across the entire enterprise, ADAPA emerged as the central decision hub for vital credit, risk, pricing, and other operational decisions.

Zementis ADAPA Case Study #2:

Delivering Predictive Analytics in the Cloud.

A specialized consulting firm with a focus on predictive analytics needed a cost-effective, agile deployment framework to deliver predictive models to their clients.  The firm specializes in outsourcing the development of predictive models for their clients, using various tools like R, SAS, and SPSS. Supporting open standards, the natural choice was to utilize the Predictive Model Markup Language (PMML) to transfer the models from the scientists development environment to a deployment infrastructure.  One key benefit of PMML is to remain development tool agnostic.  The firm selected the Zementis ADAPA Predictive Analytics Edition on the Amazon Elastic Compute Cloud (Amazon EC2) which provides a scalable, reliable deployment platform based on the PMML standard and Service Oriented Architecture (SOA).

With ADAPA, the firm was able to shorten the time-to-market for new models delivered to clients from months to just a few hours.  In addition, ADAPA enables their clients to benefit from a cost-effective SaaS utility-model, whereby the Zementis ADAPA engine is available on-demand at a fraction of the cost of traditional software licenses, eliminating upfront capital expenditures in both hardware and software. The ADAPA Predictive Analytics Edition has given the firm a highly competitive model delivery process and its clients an unprecedented agility in the deployment and integration of predictive analytics in their business processes.

Zementis ADAPA Case Study #3:

Assessing Risk in Real-Time for On-Line Merchant.

An on-line merchant with millions of customers needed to assess risk for submitted transactions before being sent to a credit-card processor.  Following a comprehensive data analysis phase, several models addressing specific data segments were built in a well-know model development platform.  Once model development is complete, models are exported in the PMML (Predictive Model Markup Language) standard. The deployment solution is the ADAPA Enterprise Edition, using its capabilities for data segmentation, data transformation, and model execution. ADAPA was selected as the optimal choice for deployment, not only because PMML-based models can easily be uploaded and are available for execution in seconds, but also because ADAPA Enterprise edition offers the seamless integration of rules and predictive analytics within a single Enterprise Decision Management solution.

ADAPA was deployed on-site and configured to handle high-volume, mission-critical transactions.  The firm not only leveraged the real-time capabilities of ADAPA, but also its integrated reporting framework.  It was very important for the merchant to assess model impact on credit card transactions on a daily basis. Given that ADAPA allows for reports to be uploaded and managed via its web administration console, the reporting team was able to design new reports, schedule them for routine execution, and send the results in PDF format for analysis to the business department with the required agility. During the implementation of the roll-out strategy, the ADAPA web console and its ease of use allowed for effective management of rules and models as well as active monitoring of deployed models and impact of decisions on the business operation.


For More on Zementis see here

Using Web 2.0 for Analytics 2.0

Here is a great video tutorial on You Tube by Zementis, creator of ADAPA,the cloud scoring engine for next gen predictive analytics. You can watch it on the URL or below-


A few weeks back, I was working with the ADAPA engine on a consulting gig, and Ron Ramos, the head of sales mentioned that though they have extensive documentation, they were planning a video tutorial as well on You Tube.

Beats a pdf everytime , doesnt it !!!

I wonder why companies continue to spend huge and I mean huge amounts on white papers and PDFs when they can have much better customer support using a bit of audio, video and even twitter support.

Surprisingly true even for companies working at the cutting edge with other technologies.And the essentially free availability of these tools.


I mean if companies can spend huge amounts for predictive solutions for the big big datasets , why cant they offer some solutions or apps for the web and social media- An exception is KXEN of course with a new Social Network Analysis Module here ).

Imagine a future –

( Example

  • Hello SAS , My code wont run blah blah blah

SAS Support on Twitter..okay do this


  • Hello SPSS, Where Can I find some stuff on Python because I got lost on the website
  • SPSS Support on Skype/Twitter- Dude , do this , click this link !


It is much better than endless rounds of email, aggravation and the list server method is well the users should try and test for user groups )

%d bloggers like this: