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Data-Driven Process Discovery: A Discrete Time Algebra for Relational Signal Analysis | 
 enlarge | Author: David M. Conrad
Publisher: Storming Media
Media: Spiral-bound
Pages: 77
ISBN: 1423579496
EAN: 9781423579496
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| Editorial Reviews:
Product Description
This is a AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A549723. The abstract provided by the Pentagon follows: This research presents an autonomous and computationally tractable method for scientific process analysis, combining an iterative algorithmic search and a recognition technique to discover multivariate linear and non- linear relations within experimental data series. These resultant data-driven relations provide researchers with a potentially real-time insight into experimental process phenomena and behavior. This method enables the efficient search of a potentially infinite space of relations within large data series to identify relations that accurately represent process phenomena. Proposed is a time series transformation that encodes and compresses real-valued data into a well defined, discrete-space of 13 primitive elements where comparative evaluation between variables is both plausible and heuristically efficient. Additionally, this research develops and demonstrates binary discrete-space operations which accurately parallel their numeric-space equivalents. These operations extend the method's utility into trivariate relational analysis, and experimental evidence is offered supporting the existence of traceable multivariate signatures of incremental order within the discrete-space that can be exploited for higher dimensional analysis by means of an iterative best-n first search.
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