Quotation Yeshchenko, Anton, Bayomie Sobh, Dina Sayed, Groß, Steven, Mendling, Jan. 2020. Visualizing Business Process Evolution. In Lecture Notes in Business Information Processing, Hrsg. Camille Salinesi, Université Paris 1 Panthéon Sorbonne, France Dominique Rieu, Université Grenoble Alpes, France, 185-192. Grenoble (France): Springer.


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Abstract

Literature in business process research has recognized that process execution adjusts dynamically to the environment, both intentionally and unintentionally. This dynamic change of frequently followed actions is called process drift. Existing process drift approaches focus to a great extent on drift point detection, i.e., on points in time when a process execution changes significantly. What is largely neglected by process drift approaches is the identification of temporal dynamics of different clusters of process execution, how they interrelate, and how they change in dominance over time. In this paper, we introduce process evolution analysis (PEA) as a technique that aims to support the exploration of process cluster interrelations over time. This approach builds on and synthesizes existing approaches from the process drift, trace clustering, and process visualization literature. Based on the process evolution analysis, we visualize the interrelation of trace clusters over time for descriptive and prescriptive purposes.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Contribution to conference proceedings
Language English
Title Visualizing Business Process Evolution
Title of whole publication Lecture Notes in Business Information Processing
Editor Camille Salinesi, Université Paris 1 Panthéon Sorbonne, France Dominique Rieu, Université Grenoble Alpes, France
Page from 185
Page to 192
Location Grenoble (France)
Publisher Springer
Year 2020
ISBN 978-3-642-31068-3
URL https://europepmc.org/article/pmc/pmc7254532
Open Access Y
Open Access Link https://europepmc.org/article/pmc/pmc7254532

Associations

People
Yeshchenko, Anton (Details)
Bayomie Sobh, Dina Sayed (Details)
Groß, Steven (Details)
Mendling, Jan (Details)
Organization
Institute for Data, Process and Knowledge Management (AE Mendling) (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1138 Information systems (Details)
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