Review of Visual Encodings in Common Process Mining Tools

No Thumbnail Available
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description

CCS Concepts: Human-centered computing → Visual analytics; Visual analytics

        
@inproceedings{
10.2312:vipra.20241104
, booktitle = {
VIPRA 2024 - Visual Process Analytics Workshop
}, editor = {
Arleo, Alessio
and
van den Elzen, Stef
and
von Landesberger, Tatiana
and
Rehse, Jana-Rebecca
and
Pufahl, Luise
and
Zerbato, Francesca
}, title = {{
Review of Visual Encodings in Common Process Mining Tools
}}, author = {
Knoblich, Steven
and
Mendling, Jan
and
Jambor, Helena
}, year = {
2024
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-254-7
}, DOI = {
10.2312/vipra.20241104
} }
Citation
Collections