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ProMiSE: Process Mining Support for End-Users

Journal
Proceedings of the Research Projects Exhibition Papers Presented at the 35th International Conference on Advanced Information Systems Engineering (CAiSE 2023)
Type
conference paper
Date Issued
2023-07-06
Author(s)
Zerbato, Francesca  
;
Zimmermann, Lisa  
;
Völzer, Hagen  
;
Weber, Barbara  
Editor(s)
CEUR Workshop Proceedings
Abstract
In the past decade, process mining has gained momentum in academia and the industry, as it supports organizations in deriving insights from event data recorded from process executions. The increasing adoption of process mining in practice entails supporting process analysts in their work. Indeed, their analysis includes many exploratory tasks that require them to rely on their experience to interpret the data and steer the analysis. This knowledge-intensive nature of process mining can be challenging for less experienced analysts and calls for methodological and operational guidance tailored to their needs. In this paper, we present ProMiSE, a project funded by the Swiss National Science Foundation that embraces this novel direction in process mining research. The first goal of the project is to improve our understanding of how analysts work in practice, i.e., the process of process mining. Then, methodological guidance and software-based support are developed to assist novice analysts during their analysis. The results obtained in the first two years of ProMiSE have helped to build a solid empirical basis on process mining, laying the foundation for the development of user-centered support, which we will realize in the coming years with the help of our project partners and international collaborators.
Funding(s)
This work is funded by the Swiss National Science Foundation under grant no. 200021_197032
Language
English
Keywords
Process of Process Mining
User Behavior Analysis
Process Mining Guidance
Software Support
Official URL
https://ceur-ws.org/Vol-3413/paper9.pdf
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/118646
File(s)
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open.access

Name

CAiSE_2023_ProjectPresentation.pdf

Size

278.63 KB

Format

Adobe PDF

Checksum (MD5)

ac6c8c0269e96978570d9013a4ad0256

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