Please use this identifier to cite or link to this item: doi:10.22028/D291-38860
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Title: Towards Automated Process Planning and Mining
Author(s): Fettke, Peter
Rombach, Alexander Michael
Language: English
Publisher/Platform: arXiv
Year of Publication: 2022
DDC notations: 330 Economics
Publikation type: Other
Abstract: AI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At the same time, many interesting concepts and insights have been gained at the intersection of these areas in recent years. For example, the behavior of future processes is now comprehensively predicted with the aid of Machine Learning. For the practical application of these findings, however, it is also necessary not only to know the expected course, but also to give recommendations and hints for the achievement of goals, i.e. to carry out comprehensive process planning. At the same time, an adequate integration of the aforementioned research fields is still lacking. In this article, we present a research project in which researchers from the AI and BPM field work jointly together. Therefore, we discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models from executional process data, derive subsequent planning problems and conduct automated planning in order to adaptively plan and execute business processes using real-time forecasts.
DOI of the first publication: 10.48550/arXiv.2208.08943
URL of the first publication: https://arxiv.org/abs/2208.08943
Link to this record: urn:nbn:de:bsz:291--ds-388608
hdl:20.500.11880/35591
http://dx.doi.org/10.22028/D291-38860
Date of registration: 4-Apr-2023
Third-party funds sponsorship: Bundesministerium für Bildung und Forschung - APPaM
Sponsorship ID: 01IW20006
Notes: Preprint
Faculty: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Department: HW - Wirtschaftswissenschaft
Professorship: HW - Prof. Dr. Peter Loos
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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