ThinkAnalytics

Integrated Intelligence

The release of Think IES 7.0 further enhances the Think IES Business Analysis Automation platform, with ease of use, performance and enterpise integration features.

The delivery of real-time analytics and modeling to a business is vital, however, the ability to achieve this and at the same time integrate with strategic technologies within an organization is critical. The ThinkAnalytics Intelligent Enterprise Server provides a range of components to connect the revolutionary data-mining platform to the major CRM and Business Intelligence technologies, improving and automating the decision making processes and protecting technology investments. For example, a call center operator retrieves customer information and an assessment of their value to the business. A personalized product offering based on buying patterns of other customers as well as the individual's customer profile is produced and the result is an instant cross-sell opportunity.

Listed below is some of the new functionality included in Think IES 7.0:

• The Think Enterprise architecture has been added to simplify real-time deployments, provide enhanced resilience, load balancing and failure recovery. Think Enterprise also supports a Java Connector Architecture (JCA) adapter for access from EJB containers and support for .NET integration

• Web Service interface to call external Web Services and call Think IES as a Web Service itself

• Support for Random, Systematic, Cluster and Stratified sampling

• Addition of Simple, Linear and Holt-Winters Exponential Smoothing forecasting

• Integration with statistical product 'R'

• Addition of One and Two Sample Kolmogorov-Smirnov (K-S) Test, One Way ANOVA and ChiSquare Independence Test

• Added support for ChiSquare, F, Lognormal, Normal, StudentT, Exponential, Weibull, Pareto, and Poisson distributions

• Extensive support for probability distribution data generation, sampling, estimation lookup and comparison.

• Added support for financial risk analysis using Monte Carlo simulation to study confidence limits in distribution analysis.