Process modeling

About: Process modeling is a research topic. Over the lifetime, 11639 publications have been published within this topic receiving 223996 citations. The topic is also known as: process simulation.

Modularity in Process Models: Review and Effects

Abstract: The use of subprocesses in large process models is an important step in modeling practice to handle complexity. While there are several advantages attributed to such a modular design, including ease of reuse, scalability, and enhanced understanding, the lack of precise guidelines turns out to be a major impediment for applying modularity in a systematic way. In this paper we approach this area of research from a critical perspective. Our first contribution is a review of existing approaches to process model modularity. This review shows that aside from some limited insights, a systematic and grounded approach to finding the optimal modularization of a process model is missing. Therefore, we turned to modular process models from practice to study their merits. In particular, we set up an experiment involving professional process modelers and tested the effect of modularization on understanding. Our second contribution, stemming from this experiment, is that modularity appears to pay off. We discuss some of the limitations of our research and implications for future design-oriented approaches.

Instantaneous Soundness Checking of Industrial Business Process Models

Abstract: We report on a case study on control-flow analysis of business process models. We checked 735 industrial business process models from financial services, telecommunications and other domains. We investigated these models for soundness (absence of deadlock and lack of synchronization) using three different approaches: the business process verification tool Woflan, the Petri net model checker LoLA, and a recently developed technique based on SESE decomposition. We evaluate the various techniques used by these approaches in terms of their ability of accelerating the check. Our results show that industrial business process models can be checked in a few milliseconds, which enables tight integration of modeling with control-flow analysis. We also briefly compare the diagnostic information delivered by the different approaches.

Process mining techniques: an application to stroke care.

Abstract: In a competitive health-care market, hospitals have to focus on ways to streamline their processes in order to deliver high quality care while at the same time reducing costs. To accomplish this goal, hospital managers need a thorough understanding of the actual processes. Diffusion of Information and Communication Technology tools within hospitals, such as electronic clinical charts, computerized guidelines and, more generally, decision support systems, make huge collections of data available, not only for data analysis, but also for process analysis. Process mining can be used to extract process related information (e.g., process models) from data, i.e., process mining describes a family of a-posteriori analysis techniques exploiting the information recorded in the event logs. This process information can be used to understand and redesign processes to become efficient high quality processes. In this paper, we apply process mining on two datasets for stroke patients and present the most interesting results. Above all, the paper demonstrates the applicability of process mining in the health-care domain.