Dissertation Abstract: Flexible Workflows - A Constraint- and Case-Based Approach

Traditional workflow systems provide guidance to process participants in carrying out business tasks according to a predefined workflow model. Flexibility has gained attention in recent decades due to a shift from mass production to customization. Various methods for achieving workflow flexibility exist. The pursuit of flexibility by deviation is to allow unforeseen alternative execution paths during run-time, without necessitating process participants to adapt the workflow model. However, this approach has received limited research attention thus far. To address this research gap, the main contribution of the presented work is a novel approach for workflow flexibility by deviation, which is implemented through the integration of two renowned methods from the field of Artificial Intelligence, constraint satisfaction problems and process-oriented case-based reasoning. The main research results of this work consist of a constraint-based workflow engine and a case-based deviation management that utilizes a time-series based similarity measure and either a null or generative constraint-based adaptation method. Each of the methods is able to compute new work items, either based on domain-independent strategies or based on experiential knowledge that is derived from previous workflow executions. This abstract is a summary of a dissertation submitted to Trier University which was successfully defended in February 2023 [5].