Friday, January 28, 2011

PMML group in LinkedIn: Close to 1,000 members!


The Predictive Model Markup Language (PMML) is the leading standard for representing statistical and data mining models. 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. PMML is currently supported by over 20 vendors and organizations and awareness as well as use of the standard is growing quickly. To establish a conduit in which people can come together to learn and discuss topics related to PMML, we have created a PMML interest group in LinkedIn. The group is going strong, with many PMML-related discussions and announcements every week. And, we are happy to announce that the group is now nearing 1,000 members!

To join the Predictive Model Markup Language (PMML) group on LinkedIn, please follow this link:

http://www.linkedin.com/groupRegistration?gid=2328634

The group aims to serve as a central resource regarding the practical application of PMML, its benefits for business and IT. PMML increases business agility by eliminating the need for proprietary solutions or custom code development. For this reason, it is a critical element in the quest for business process optimization and automated, intelligent decisions.

We encourage active participation in the PMML group from the entire community, please post your questions! The group already contains postings related to
  • The value of PMML for business and IT

  • PMML powered products

  • Links to a general introduction and overview presentation
If your organization is already supporting the PMML standard, please feel welcome to share information about your products which do so.

Thursday, January 20, 2011

ADAPA in the Cloud - Full Feature List

Broad support for predictive algorithms
ADAPA supports an extensive collection of statistical and data mining algorithms. These are:


  • Ruleset Models
  • Clustering Models (Distribution-Based, Center-Based, and 2-Step Clustering)
  • Decision Trees (for classification and regression) together with multiple missing value handling strategies (Default Child, Last Prediction, Null Prediction, Weighted Confidence, Aggregate Nodes)
  • Naive Bayes Classifiers
  • Association Rules
  • Neural Networks (Back-Propagation, Radial-Basis Function, and Neural-Gas)
  • Regression Models (Linear, Polynomial, and Logistic) and General Regression Models (General Linear, Ordinal Multinomial, Generalized Linear, Cox)
  • Support Vector Machines (for regression and multi-class and binary classification)
  • Scorecards (including reason codes and point allocation for categorical, continuous, and complex attributes)
  • Multiple Models (Segmentation, Ensembles - including Random Forest Models, Chaining and Model Composition)

Model interfaces: pre- and post-processing
Additionally, ADAPA supports a myriad of functions for implementing data pre- and post-processing. These include:
  • Text Mining
  • Value Mapping
  • Discretization
  • Normalization
  • Scaling
  • Regular Expressions
  • Logical and Arithmetic Operators
  • Lookup Tables
  • Custom Functions
and much much more

If you think of anything ADAPA cannot do or something else you need to do in terms of data manipulation, let us know.

Automatic conversion (and correction) for older versions of PMML
ADAPA consumes model files that conform to PMML, version 2.0 through 4.2. If your model development environment exports an older version, ADAPA will automatically convert your file into a 4.2 compliant format. It will also correct a number of common problems found in PMML generated by some popular modeling tools, allowing the models to work as intended.

Web-based management and interactive execution of predictive models and business rules
Model management: Models and rule sets are deployed and managed through an intuitive, Web-based management console, the ADAPA Console.
  • Model verification: The ADAPA Console includes a model validation test, allowing models to be verified for correctness. By providing ADAPA a test file containing input data and expected results for a model, the engine will report any deviations from expected results, greatly enhancing traceability of errors and debugging of model deployment issues. The console also provides easy access to our rules testing framework in which business rules are submitted to regression testing and acceptance.
  • Batch-scoring: The console also provides functionality to upload a (compressed) CSV data file and batch-scores it against any of the deployed models. Results are returned in the same format and may be downloaded for further processing and visualization.

Simplified integration via SOA
Service Oriented Architecture (SOA) principles simplify integration with existing IT infrastructure. Since ADAPA publishes all deployed models and rule sets as a Web-Service, you can score data records from within your own environment. With the simple execution of a web service call (SOAP or REST), you are able to leverage the power of predictive models and business rules on-demand or in real-time.

Data scoring from inside Excel
The ADAPA Add-in for Microsoft Office Excel 2007, 2010 and 2013  allows you to easily score data using ADAPA in the Cloud. Once the Add-in is installed, all you need to do is to select your data in Excel, connect to ADAPA and start scoring right away. Your predictions will be made available as new columns.

On-demand predictive analytics solution
ADAPA in the Cloud is a fully hosted Software-as-a-Service (SaaS) solution. You only pay for the service and the capacity that is used, eliminating the necessity for expensive software licenses and in-house hardware resources. As the business grows, ADAPA in the Cloud provides a cost-effective expansion path, for example, by adding multiple ADAPA instances for scalability or failover. The SaaS model removes the burden for you to manage a scalable, on-demand computing infrastructure.
At any given time, launch one or more instances using the ADAPA Control Center Web interface. For each instance, select the most appropriate instance type: “large”, “high-CPU” or “high-IO”.

Private instance for all your decisioning needs
We provide you with a single-tenant architecture. The service is implemented as a private, dedicated instance of ADAPA that encapsulates your predictive models and business rules. Only you have access to your private ADAPA instance(s) via HTTPS. Your decisioning files and data never share the same engine with other clients. You launch and terminate your dedicated ADAPA instances through the secure ADAPA Control Center.

Trusted, secure, scalable cloud infrastructure
Zementis leverages Amazon Web Services providing on-demand infrastructure for ADAPA on the Cloud. The Amazon Elastic Compute Cloud (Amazon EC2) offers utility computing with virtually unlimited scalability. Billing and subscription management are handled through Amazon. Payment information remains secure and confidential while enjoying the convenience of using your existing Amazon.com account. Yes, the same account you use to buy book.

Wednesday, January 19, 2011

ADAPA 3.3 Released: Extended PMML Coverage

PMML, the Predictive Model Markup Language, allows for a predictive analytic model to be developed in one application and easily moved to another for production deployment and execution.

Once a predictive model is exported from a PMML-compliant tool such as SAS EM, SPSS/IBM, R, KNIME, RapidMiner, ... it can be uploaded directly into the Zementis ADAPA engine which makes the model available for execution via its console or as a web-service. ADAPA can already import most of the techniques defined by the PMML standard and now, with the release of ADAPA 3.3, we have expanded it even further to cover Cox Regression and Ruleset models.

Cox Regression Models

Cox proportional hazards model of survival is used in various industries including pharmaceutical and telecommunications.

Ruleset Models

Ruleset models can be thought of as flattened decision tree models. A ruleset consists of a number of rules in which each rule contains a predicate and a predicted class value.

As of now, ADAPA supports the following modeling techniques:
  • Ruleset Models
  • Clustering Models (Distribution-Based, Center-Based, and 2-Step Clustering)
  • Decision Trees (for classification and regression) together with multiple missing value handling strategies (Default Child, Last Prediction, Null Prediction, Weighted Confidence, Aggregate Nodes)
  • Naive Bayes Classifiers
  • Neural Networks (Back-Propagation, Radial-Basis Function, and Neural-Gas)
  • Regression Models (Linear, Polynomial, and Logistic) and General Regression Models (General Linear, Ordinal Multinomial, Generalized Linear, Cox)
  • Support Vector Machines (for regression and multi-class and binary classification)
  • Scorecards (point allocation for categorical, continuous, and complex attributes)

Additionally, ADAPA supports a myriad of functions for implementing data pre- and post-processing. These include:

  • Value Mapping
  • Discretization
  • Normalization
  • Scaling
  • Logical and Arithmetic Operators

and much much more

If you think of anything ADAPA cannot do or something else you need to do in terms of data manipulation, let us know.

For your free trial of ADAPA, please register at: https://myadapa.zementis.com/

Thursday, January 13, 2011

Predictive Analytics + Business Rules = Enhanced Decisioning


Business Rules are ubiquitous today. They manage the day to day operations of thousands of companies worldwide. From stocking to maintenance, rules are an integral part of the way we do business in the 21st century. This kind of knowledge, know as Expert Knowledge is forged from years of experience, or what turned out to be the “logical thing to do”.

However, along with the information age, more and more data started being gathered all over the world about the processes and services we as a society came to benefit from. In this sea of data, predictive algorithms were designed to extract its hidden patterns, i.e. knowledge that is hidden from the human eye. This is known as Data-Driven Knowledge.

In an ideal world, business rules and predictive models live side by side benefiting from each other since both encode complementary types of knowledge.

In the presentation below, originally given at RulesFest 2010, Dr. Alex Guazzelli starts by differentiating the two types of knowledge. He then makes the point that companies can get Enhanced Decisioning whenever expert and data-driven knowledge are combined. Dr. Guazzelli goes on to describe the making of a predictive solution by using a "fish processing plant" as an analogy for any process that can benefit from intelligent decisioning. He ends by showing how such a solution can be deployed using PMML (the Predictive Model Markup Language) and easily moved to the production environment using ADAPA, the Zementis Predictive Decisioning Engine.






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