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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.04114 |
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| _version_ | 1866916085109882880 |
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| author | Kaur, Harleen Mendling, Jan Rubensson, Christoffer Kampik, Timotheus |
| author_facet | Kaur, Harleen Mendling, Jan Rubensson, Christoffer Kampik, Timotheus |
| contents | A key concern of automatic process discovery is to provide insights into performance aspects of business processes. Waiting times are of particular importance in this context. For that reason, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models, but often miss the opportunity to explicitly represent the time axis. In this paper, we present an approach for automatically constructing process models that explicitly align with a time axis. We exemplify our approach for directly-follows graphs. Our evaluation using two BPIC datasets and a proprietary dataset highlight the benefits of this representation in comparison to standard layout techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04114 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Timeline-based Process Discovery Kaur, Harleen Mendling, Jan Rubensson, Christoffer Kampik, Timotheus Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning A key concern of automatic process discovery is to provide insights into performance aspects of business processes. Waiting times are of particular importance in this context. For that reason, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models, but often miss the opportunity to explicitly represent the time axis. In this paper, we present an approach for automatically constructing process models that explicitly align with a time axis. We exemplify our approach for directly-follows graphs. Our evaluation using two BPIC datasets and a proprietary dataset highlight the benefits of this representation in comparison to standard layout techniques. |
| title | Timeline-based Process Discovery |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2401.04114 |