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. If data dependencies, resource assignments and time constraints are not considered, the diagnostics may be misleading and classifies non-compliant cases as compliant.
For example, a data attribute may provide strong evidence that the wrong activity was executed. Conformance-checking techniques that classifying cases as compliant or non-compliant are of little use.Conversely, these techniques should pinpoint where deviations precisely occur and what are the root-causes.
This talk will discuss a novel algorithm, which employs AI and data-mining techniques.Evaluations using real data sets show that the results are indeed useful for stakeholder to improve the execution of their processes.
Massimiliano de Leoni is an Assistant Professor of Information Systems at the Eindhoven University of Technology (TU/e), The Netherlands. In 2009, he earned a PhD in Engineering in Computer Science at Sapienza - University of Rome (Italy) discussing a dissertation on Adaptive Process Management in Highly Dynamic and Pervasive Scenarios.
His research interests are in the area of Process-aware Information Systems and Business Process Management, predominantly focusing on multi-perspective process mining, process-aware decision support systems as well as Scientific Workflows and OLAP-based techniques for process mining.