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Journal of Engineering
Volume 2016, Article ID 8569694, 11 pages
Research Article

Changing States of Multistage Process Chains

1West Virginia University, Morgantown, WV, USA
2University of Strathclyde, Glasgow G1 1XQ, UK
3University of Bremen, 28359 Bremen, Germany

Received 5 July 2016; Revised 25 October 2016; Accepted 1 November 2016

Academic Editor: Luis Carlos Rabelo

Copyright © 2016 Thorsten Wuest et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Generally, a process describes a change of state of some kind (state transformation). This state change occurs from an initial state to a concluding state. Here, the authors take a step back and take a holistic look at generic processes and process sequences from a state perspective. The novel perspective this concept introduces is that the processes and their parameters are not the priority; they are rather included in the analysis by implication. A supervised machine learning based feature ranking method is used to identify and rank relevant state characteristics and thereby the processes’ inter- and intrarelationships. This is elaborated with simplified examples of possible applications from different domains to make the theoretical concept and results more feasible for readers from varying domains. The presented concept allows for a holistic description and analysis of complex, multistage processes sequences. This stands especially true for process chains where interrelations between processes and states, processes and processes, or states and states are not fully understood, thus where there is a lack of knowledge regarding causations, in dynamic, complex, and high-dimensional environments.