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Towards Automated Process Planning and Mining

arXiv 2208.08943 ↗First posted 18 Aug 2022cs.AI cs.SEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 18 Aug 2022.

Builds on

3 of 4 references.

References · 3

  1. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  2. 2022Systems Mining with Heraklit: The Next Stepcited by 1
  3. 2022Easy, adaptable and high-quality Modelling with domain-specific Constraint Patternscited by 1

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