Kriglstein, S., Pohl, M., Rinderle-Ma, S., & Stallinger, M. (2016). Visual Analytics in Process Mining: Classification of Process Mining Techniques. In N. Andrienko & M. Sedlmair (Eds.), EuroVis Workshop on Visual Analytics (pp. 43–47). The Eurographics Association. https://doi.org/10.2312/eurova.20161123
E193-05 - Forschungsbereich Human Computer Interaction
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Published in:
EuroVis Workshop on Visual Analytics
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ISBN:
978-3-03868-016-1
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Date (published):
2016
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Event name:
7th International Eurovis Workshop on Visual Analytics (EuroVA)
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Event date:
6-Jun-2016 - 7-Jun-2016
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Event place:
Groningen, Netherlands (the)
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Number of Pages:
5
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Publisher:
The Eurographics Association
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Publisher:
The Eurographics Association
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Peer reviewed:
Yes
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Abstract:
The increasing interest from industry and academia has driven the development of process mining techniques over the last years. Since the process mining entails a strong explorative perspective, the combination of process mining and visual analytics methods is a fruitful multidisciplinary solution to enable the exploration and the understanding of large amounts of event log data. In this paper, we propose a first approach how process mining techniques can be categorized with respect to visual analytics aspects. Since ProM is a widely used open-source framework which includes most of the existing process mining techniques as plug-ins, we concentrate on the plugins of ProM as use case to show the applicability of our approach.
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Research Areas:
Visual Computing and Human-Centered Technology: 100%
