The Datascape Process is typically comprised of the four steps illustrated in the diagram below.

Quickly Construct Models that Capture the Complex Behavior of your Data...


Preparing the data is the first step in creating a model. Each data point consists of a series of inputs and an associated output. Datascape will create a model of the output based on the relationships it learns from the data points. Sources of data can include: test and acquired data or data tables, output from a simulation.


Datascape uses an iterative learning process to build a model from the data points. You can even observe the evolution of your model during the learning cycle.
    Datascape provides the analyst with interactive:
    • 3-D Surface Visualizations, with Input Controls for Additional Dimensions
    • 2-D Plotting Functions of Correlation and Model Response
    • Quality of Fit Metrics
    • Quantitative Ranking of Input Importance
    • Visualization of Data Points, Including those that Don't Follow the Trend


After learning, a model can be validated by testing it with the verification data set developed in the first step.

Deploying the Model

After creating a model, you can use it to evaluate new inputs through the Datascape API, allowing you to:

    • Create New Standalone Applications
    • Link into Existing Applications
    • Embed the Model into Real-Time Systems

The Datascape API is currently implemented for the following environments:

    • Microsoft C++ (DLL)
    • SGI IRIX (DSO)
    • RedHat Linux (shared library)
    • HP-UX 64-bit Itanium (shared library)
    • Embedded RTOS
Other platforms will be supported as required.

Datascape models can also be used in Microsoft Excel spreadsheets via the Datascape/Excel Add-In.

Copyright© 2009 Third Millennium Productions, Inc. All rights reserved. | Legal information | Privacy policy |