Visual Analytics in Process Mining: Classification of Process Mining Techniques
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Date
2016Author
Kriglstein, Simone
Pohl, Margit
Rinderle-Ma, Stefanie
Stallinger, Magdalena
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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.
BibTeX
@inproceedings {10.2312:eurova.20161123,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Natalia Andrienko and Michael Sedlmair},
title = {{Visual Analytics in Process Mining: Classification of Process Mining Techniques}},
author = {Kriglstein, Simone and Pohl, Margit and Rinderle-Ma, Stefanie and Stallinger, Magdalena},
year = {2016},
publisher = {The Eurographics Association},
ISSN = {-},
ISBN = {978-3-03868-016-1},
DOI = {10.2312/eurova.20161123}
}
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Natalia Andrienko and Michael Sedlmair},
title = {{Visual Analytics in Process Mining: Classification of Process Mining Techniques}},
author = {Kriglstein, Simone and Pohl, Margit and Rinderle-Ma, Stefanie and Stallinger, Magdalena},
year = {2016},
publisher = {The Eurographics Association},
ISSN = {-},
ISBN = {978-3-03868-016-1},
DOI = {10.2312/eurova.20161123}
}