Intelligent Support of the Business Process Model Analysis and Improvement Method Since business process modeling is considered as the foundation of Business Process Management, it is required to design understandable and modifiable process models used to analyze and improve depicted business processes. Therefore, this article proposes a method for business process model analysis and improvement. The lifecycle of Business Process Management from business process modeling to applying the Business Intelligence and process mining techniques is considered. Existing approaches to business process model analysis are reviewed. Proposed method is based on best practices in business process modeling, process model metrics, and corresponding thresholds. The usage of business process model metrics and thresholds to formalize process modeling guidelines is outlined, as well as the procedure of business process model analysis and improvement is shown. The application of Business Intelligence techniques to support the proposed method is demonstrated, as well as the obtained results are shown and discussed.

Discovering and navigating a collection of process models using multiple quality dimensions Process discovery algorithms typically aim at discovering a process model from an event log that best describes the recorded behavior. However, multiple quality dimensions can be used to evaluate a process model. In previous work we showed that there often is not one single process model that describes the observed behavior best in all quality dimensions. Therefore, we present an extension to our flexible ETM algorithm that does not result in a single best process model but in a collection of mutually nondominating process models. This is achieved by constructing a Pareto front of process models. We show by applying our approach on a real life event log that the resulting collection of process models indeed contains several good candidates. Furthermore, by presenting a collection of process models, we show that it allows the user to investigate the different tradeoffs between different quality dimensions. Keywords: Process mining; Process model quality; Process model collection