Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects.
The discipline of business process management aims at capturing, understanding, and improving work in organizations by using process models as central artifacts. Since business-oriented tasks require different information from such models to be highlighted, a range of abstraction techniques has been developed over the past years to manipulate overly detailed models. At this point, a clear understanding of what distinguishes these techniques and how they address real world use cases has not yet been established. In this paper we systematically develop, classify, and consolidate the use cases for business process model abstraction and present a case study to illustrate the value of this technique. The catalog of use cases that we present is based on a thorough evaluation of the state of the art, as well as on our cooperation with end users in the health insurance sector. It has been subsequently validated by experts from the consultancy and tool vendor domains. Based on our findings, we evaluate how the existing business process model abstraction approaches support the discovered use cases and reveal which areas are not adequately covered, as such providing an agenda for further research in this area. Communicated by Asuman Dogac. S. Smirnov ( ) 路 M. Weske
Bibliografische Information der Deutschen NationalbibliothekAbstract. Business process modeling is a creative task carried out by humans. Business analysts capture process knowledge in models. Process models are decompositions of processes into well recognized business tasks and their structuring by means of control flow. As outcome of a creative practice, models can be composed from tasks of different abstraction levels, i.e., low level tasks with a short and centralized lifecycles and general activities spanning over company departments. In this paper we propose to utilize process model control flow structure for the purpose of generalization of low level tasks to tasks of higher abstraction level. We use SPQR-tree hierarchical process model decomposition for identification of process model components-control flow structures with a self-contained logic suitable for abstraction. The approach allows the highest granularity as compared to existing techniques.
Models of business processes can easily become large and difficult to understand. Abstraction has proven to be an effective means to present a readable, high-level view of a business process model, by showing aggregated activities and leaving out irrelevant details. Yet, it is an open question how to combine activities into high-level tasks in a way that corresponds to such actions by experienced modelers. In this paper, an approach is presented that exploits semantic information within a process model, beyond structural information, to decide on which activities belong to one another. In an experimental validation, we used an industrial process model repository to compare this approach with actual modeling decisions, showing a strong correlation between the two. As such, this paper contributes to the development of modeling support for the application of effective process model abstraction, easing the use of business process models in practice.
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