Big data insights through predictive analytics, open-standards and cloud computing

Organizations increasingly recognize the value that predictive analytics and big data offer to their business. The complexity of development, integration, and deployment of predictive solutions, however, is often considered cost-prohibitive for many projects. In light of mature open source solutions, open standards, and SOA principles we propose an agile model development life cycle that quickly leverages predictive analytics in operational environments.

Starting with data analysis and model development, you can effectively use the Predictive Model Markup Language (PMML) standard, to move complex decision models from the scientist's desktop into a scalable production environment hosted in the cloud (Amazon EC2 and IBM SmartCloud Enterprise).

Expressing Models in PMML

PMML is an XML-based language used to define predictive models. It was specified by the Data Mining Group, an independent group of leading technology companies including Zementis. By providing a uniform standard to represent such models, PMML allows for the exchange of predictive solutions between different applications and various vendors.

Open source PMML-compliant statistical tools such as R, KNIME, and RapidMiner can be used to develop data mining models based on historical data. Once models are exported into a PMML file, they can then be imported into an operational decision platform and be ready for production use in a matter of minutes.

On-Demand Predictive Analytics

Both Amazon and IBM offer a reliable and on-demand cloud computing infrastructure on which we offer the ADAPA® Predictive Decisioning Engine based on the Software as a Service (SaaS) paradigm. ADAPA imports models expressed in PMML and executes these in batch mode, or real-time via web-services.

Our service is implemented as a private, dedicated instance of ADAPA. Each client has access to his/her own ADAPA Engine instance via HTTP/HTTPS. In this way, models and data for one client never share the same engine with other clients.

The ADAPA Web Console

Each instance executes a single version of the ADAPA engine. The engine itself is accessible through the ADAPA Web Console which allows for the easy managing of predictive models and data files. The instance owner can use the console to upload new models as well as score or classify records on data files in batch mode. Real-time execution of predictive models is achieved through the use of web-services. The ADAPA Console offers a very intuitive interface which is divided into two main sections: model and data management. These allow for existing models to be used for generating decisions on different data sets. Also, new models can be easily uploaded and existing models can be removed in a matter of seconds.

Predicting in the Cloud

Using a SaaS solution to break down traditional barriers that currently slow the adoption of predictive analytics, our strategy translates predictive solutions into operational assets with minimal deployment costs and leverages the inherent scalability of utility computing.

In summary, ADAPA revolutionizes the world of predictive analytics and cracks the big data code, since it allows for:

• Cost-effective and reliable service based on two outstanding cloud computing infrastructures: Amazon and IBM.


• Secure execution of predictive models through dedicated and controlled instances including HTTPS and Web-Services security


• On-demand computing. Choice of instance type and launch of multiple instances.


• Superior time-to-market by providing rapid deployment of predictive solutions and an agile enterprise decision management environment.

ADAPA in the Cloud: Feature List

Broad support for predictive algorithms

ADAPA supports an extensive collection of statistical and data mining algorithms. These are:

Ruleset Models (flat Decision Trees) 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 and Stochastic Boosting, 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
• Logical and Arithmetic Operators
• Business Rules
• Lookup Tables
• Regular Expressions
• 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 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 on 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.

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. 

Trusted, secure, scalable cloud infrastructure

Zementis leverages FICO and Amazon EC2 for providing on-demand infrastructure for ADAPA in the Cloud. Cloud computing offers utility computing with virtually unlimited scalability.