Comprehensive Process Drift Detection with Visual Analytics
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2019
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| _version_ | 1866915802632945664 |
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| author | Yeshchenko, Anton Di Ciccio, Claudio Mendling, Jan Polyvyanyy, Artem |
| author_facet | Yeshchenko, Anton Di Ciccio, Claudio Mendling, Jan Polyvyanyy, Artem |
| contents | Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1907_06386 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Comprehensive Process Drift Detection with Visual Analytics Yeshchenko, Anton Di Ciccio, Claudio Mendling, Jan Polyvyanyy, Artem Artificial Intelligence Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique. |
| title | Comprehensive Process Drift Detection with Visual Analytics |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/1907.06386 |