dc.contributor.author | van Zelst, Sebastiaan J. | |
dc.contributor.author | Mannhardt, Felix | |
dc.contributor.author | de Leoni, Massimiliano | |
dc.contributor.author | Koschmider, Agnes | |
dc.date.accessioned | 2021-02-23T10:25:06Z | |
dc.date.available | 2021-02-23T10:25:06Z | |
dc.date.created | 2020-05-28T10:50:56Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 2364-4966 | |
dc.identifier.uri | https://hdl.handle.net/11250/2729732 | |
dc.description.abstract | The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., the focus of process mining, yields a detailed understanding of the process, e.g., we are able to discover the control flow of the process and detect compliance and performance issues. Most process mining techniques assume that the event data are of the same and/or appropriate level of granularity. However, in practice, the data are extracted from different systems, e.g., systems for customer relationship management, Enterprise Resource Planning, etc., record the events at different granularity levels. Hence, pre-processing techniques that allow us to abstract event data into the right level of granularity are vital for the successful application of process mining. In this paper, we present a literature study, in which we assess the state-of-the-art in the application of such event abstraction techniques in the field of process mining. The survey is accompanied by a taxonomy of the existing approaches, which we exploit to highlight interesting novel directions. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.uri | https://link.springer.com/article/10.1007/s41066-020-00226-2 | |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Event abstraction in process mining: literature review and taxonomy | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Granular Computing | en_US |
dc.identifier.doi | 10.1007/s41066-020-00226-2 | |
dc.identifier.cristin | 1812986 | |
dc.description.localcode | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |