dc.contributor.author | Kriglstein, Simone | en_US |
dc.contributor.author | Pohl, Margit | en_US |
dc.contributor.author | Rinderle-Ma, Stefanie | en_US |
dc.contributor.author | Stallinger, Magdalena | en_US |
dc.contributor.editor | Natalia Andrienko and Michael Sedlmair | en_US |
dc.date.accessioned | 2016-06-09T09:32:10Z | |
dc.date.available | 2016-06-09T09:32:10Z | |
dc.date.issued | 2016 | en_US |
dc.identifier.isbn | 978-3-03868-016-1 | en_US |
dc.identifier.issn | - | en_US |
dc.identifier.uri | http://dx.doi.org/10.2312/eurova.20161123 | en_US |
dc.identifier.uri | https://diglib.eg.org:443/handle/10 | |
dc.description.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. | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.subject | H.4.m [Information Systems Applications] | en_US |
dc.subject | Miscellaneous | en_US |
dc.title | Visual Analytics in Process Mining: Classification of Process Mining Techniques | en_US |
dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | en_US |
dc.description.sectionheaders | Temporal Data Analysis | en_US |
dc.identifier.doi | 10.2312/eurova.20161123 | en_US |
dc.identifier.pages | 43-47 | en_US |