Addressing Convergence, Divergence, and Deficiency Issues

The application of process mining algorithms to event logs requires the extraction of cases, describing end-to-end runs through the process. When extracting cases for object-centric event data, this extraction is often subject to convergence, divergence, and deficiency issues. Recently, connected-components extraction was proposed, extracting graph-based cases, called process executions, from the graph of event precedence constraints. This paper shows that only case extraction based on connected-components is free of convergence, divergence, and deficiency issues. This proof has several implications for future research in object-centric process mining. First, if a downstream process mining task is negatively affected by these quality issues, connected-components extraction is the only way to mitigate these. Second, additional requirements that would conflict with connected-components extraction would render the mitigation of quality issues infeasible, making trade-offs between quality issues necessary. Third, as traditional event logs are a special case of object-centric event logs and connected-components extraction is equivalent to the traditional case concept for a traditional event log, new extraction techniques, as well as object-centric adaptations of algorithms, should be backward-compatible.