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Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad
Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad
Theses and Dissertations
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 …
Utilizing Data And Knowledge Mining For Probabilistic Knowledge Bases, Daniel J. Stein Iii
Utilizing Data And Knowledge Mining For Probabilistic Knowledge Bases, Daniel J. Stein Iii
Theses and Dissertations
Problems can arise whenever inferencing is attempted on a knowledge base that is incomplete. Our work shows that data mining techniques can be applied to fill in incomplete areas in Bayesian Knowledge Bases (BKBs), as well as in other knowledge-based systems utilizing probabilistic representations. The problem of inconsistency in BKBs has been addressed in previous work, where reinforcement learning techniques from neural networks were applied. However, the issue of automatically solving incompleteness in BKBs has yet to be addressed. Presently, incompleteness in BKBs is repaired through the application of traditional knowledge acquisition techniques. We show how association rules can be …