Organizations maintain process models that describe or prescribe how cases (e.g., orders) are handled. However, reality may not agree with what is modeled. Conformance checking techniques reveal and diagnose differences between the behavior that is modeled and what is observed. Existing conformance checking approaches tend to focus on the control-flow in a process, while abstracting from data dependencies, resource assignments, and time constraints. Even in those situations when other perspectives are considered, the control-flow is aligned first, i.e., priority is given to this perspective. Data dependencies, resource assignments, and time constraints are only considered as "second-class citizens", which may lead to misleading conformance diagnostics. For example, a data attribute may provide strong evidence that the wrong activity was executed. Existing techniques will still diagnose the data-flow as deviatWhen conducting most of the research reported on in this paper, Dr. de Leoni was also affiliated with University of Padua, Italy, and financially supported by the Eurostar-Eureka Project PROMPT (E! 6696). ing, whereas our approach will indeed point out that the control-flow is deviating. In this paper, a novel algorithm is proposed that balances the deviations with respect to all these perspectives based on a customizable cost function. Evaluations using both synthetic and real data sets show that a multi-perspective approach is indeed feasible and may help to circumvent misleading results as generated by classical single-perspective or staged approaches. F. Mannhardt (B)·
Mannhardt et al. REF proposed a process mining algorithm to check process conformance with respect to control flow, data dependencies, resource assignments, and time constraints.
Balanced multi-perspective checking of process conformance
Computing
Computing
Computer Science