Methods for Enabling a Scalable Transformation of Diverse Data into Hypotheses, Models and Dynamic Simulations to Drive the Discovery of New Knowledge
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Sep 10, 2009
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Sep 16, 2008
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Abstract
The present invention relates to a method for the automatic identification of at least one informative data filter from a data set that can be used to identify at least one relevant data subset against a target feature for subsequent hypothesis generation, model building and model testing. The present invention describes methods, and an initial implementation, for efficiently linking relevant data both within and across multiple domains and identifying informative statistical relationships across this data that can be integrated into agent-based models. The relationships, encoded by the agents, can then drive emergent behavior across the global system that is described in the integrated data environment.
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