Changing States of Multistage Process Chains

Abstract

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.