Process Mining for Unstructured Data: Challenges and Research Directions
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866909329867669504 |
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| author | Koschmider, Agnes Aleknonytė-Resch, Milda Fonger, Frederik Imenkamp, Christian Lepsien, Arvid Apaydin, Kaan Harms, Maximilian Janssen, Dominik Langhammer, Dominic Ziolkowski, Tobias Zisgen, Yorck |
| author_facet | Koschmider, Agnes Aleknonytė-Resch, Milda Fonger, Frederik Imenkamp, Christian Lepsien, Arvid Apaydin, Kaan Harms, Maximilian Janssen, Dominik Langhammer, Dominic Ziolkowski, Tobias Zisgen, Yorck |
| contents | The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13677 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Process Mining for Unstructured Data: Challenges and Research Directions Koschmider, Agnes Aleknonytė-Resch, Milda Fonger, Frederik Imenkamp, Christian Lepsien, Arvid Apaydin, Kaan Harms, Maximilian Janssen, Dominik Langhammer, Dominic Ziolkowski, Tobias Zisgen, Yorck Databases Artificial Intelligence Machine Learning The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic. |
| title | Process Mining for Unstructured Data: Challenges and Research Directions |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.13677 |