ProMiSE: Process Mining Support for End-users

In the past decade, process mining has gained momentum both in academia and industry, aiming to support organizations in analyzing the vast amounts of data collected in their information systems from a process perspective in order to extract processes, detect deviations, identify bottlenecks with the goal to improve their busines processes. However, process mining encompasses several exploratory analysis tasks, which require analysts to rely on their own experience to make sense of data. As a result, analysts’ work remains mostly unstructured and knowledge-intensive, posing challenging for the less experienced analysts and the development of methodological and operational guidance.

This project opens up a new direction for process mining research and pursues the following main goals:

(1) Gaining a comprehensive understanding of how analysts do process mining in practice, i.e., the “process of process mining,” including frequent patterns of effective and noneffective behavior, analysis profiles, common analysis strategies, and typical challenges.

(2) Developing methodological guidance and software-based operational support to assist novice analysts during the analysis, building upon effective and noneffective patterns of behavior observed in practice.

This project will significantly advance our understanding of how analysts conduct exploratory process mining tasks, potentially providing useful empirical evidence to increase the accessibility of process mining approaches and applications to non-expert users and support the training and education of novice analysts process mining students.

In this project, financed by the Swiss National Science Foundation, researchers from the Institute of Computer Science at the University of St. Gallen will work together with distinguished researchers from the University of Haifa (IL), the Technical University of Denmark (DK), University of Copenhagen (DK). All the members of the international project team are well-known in the field of process mining and have relevant and complementary expertise in the areas of the proposed project, namely process mining, business process management, information systems, and software engineering.