Review of Visual Encodings in Common Process Mining Tools
| dc.contributor.author | Knoblich, Steven | en_US |
| dc.contributor.author | Mendling, Jan | en_US |
| dc.contributor.author | Jambor, Helena | en_US |
| dc.contributor.editor | Arleo, Alessio | en_US |
| dc.contributor.editor | van den Elzen, Stef | en_US |
| dc.contributor.editor | von Landesberger, Tatiana | en_US |
| dc.contributor.editor | Rehse, Jana-Rebecca | en_US |
| dc.contributor.editor | Pufahl, Luise | en_US |
| dc.contributor.editor | Zerbato, Francesca | en_US |
| dc.date.accessioned | 2024-05-21T09:09:49Z | |
| dc.date.available | 2024-05-21T09:09:49Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Process mining tools empower process analysts to scrutinize business processes by leveraging algorithmic techniques and event log datasets. To support the analysis of inefficiencies of business processes, different types of visualization techniques have been introduced for process mining. These techniques enhance process models by incorporating performance data, for instance to highlight activity duration by using gradational color palettes, and by mapping statistical parameters as text notes directly into the model. So far, tool vendors have designed a diverse spectrum of visual features for enhancing models, but research has not systematically provided insights into their mutual effectiveness. In this paper, we review the visualizations of six common business process mining tools. To account for the variability in the visual display, we expanded existing taxonomies for evaluating event sequences with marks and channels as well as accessibility dimensions, each important for end-user comprehension. Then, we performed an expert survey to assess the legibility of the visualizations to test the validity of our expanded taxonomy. In this way, we demonstrate the potential for improving process mining visualizations to expand its value in today's process mining tools. | en_US |
| dc.description.seriesinformation | VIPRA 2024 - Visual Process Analytics Workshop | |
| dc.identifier.doi | 10.2312/vipra.20241104 | |
| dc.identifier.isbn | 978-3-03868-254-7 | |
| dc.identifier.pages | 6 pages | |
| dc.identifier.uri | https://doi.org/10.2312/vipra.20241104 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/vipra20241104 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Human-centered computing → Visual analytics; Visual analytics | |
| dc.subject | Human centered computing → Visual analytics | |
| dc.subject | Visual analytics | |
| dc.title | Review of Visual Encodings in Common Process Mining Tools | en_US |
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