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Due to a lack of event data, organizations using Business Process Management may struggle to update their process models whenever business process changes occur. As such, alternative data sources are needed to detect and monitor business process changes.

We conducted interviews with domain experts to analyze real business processes and classify any identified changes using Reijers and Mansar's framework on business process redesign heuristics. Based on the results, we proposed a taxonomy of entity groups as well as a method for identifying entities in process models based on the taxonomy. With this method, we define how to create a mapping between entities and process models, forming a framework to identify outdated process models through monitoring the entities.

The analyses of 25 business processes let us define three entity groups that form our taxonomy. We confirmed the feasibility of our entity identification method by classifying process elements of 85 pre-existing process models according to the taxonomy. Regarding the mapping that forms the basis of our framework, we found it necessary to define requirement recommendations to ensure its consistency and rigor.

This study offers a novel approach to handling the challenges of business process change. By emphasizing the identification and use of entities in the analysis of business process changes, it provides a holistic approach to monitoring the work systems of an organization.

Process modeling guidelines are a valuable instrument for increasing the quality of process models. Since finding and selecting suitable guidelines are challenging, this paper aims to find and select suitable guidelines because they are scattered across the many studies of the literature. Also, not all of them are supported by empirical studies.

The authors conducted a systematic literature review to collect and analyze process modeling guidelines present in the literature and the empirical evidence that supports them.

The authors investigated a total of 793 articles and identified a total of 45 process modeling guidelines in five different categories. For each of these guidelines, the authors report empirical evidence together with corresponding measures, such as comprehension accuracy and error probability.

Compared to the prior literature reviews on process model quality and process modeling guidelines, this article extends current knowledge by analyzing the empirical evidence and variables associated with each guideline. This analysis provides guidance for practitioners and scholars on which guidelines to use while modeling a process and perform further research on.