# Towards Automated Process Planning and Mining

arXiv: https://arxiv.org/abs/2208.08943 · HTML: https://mlsanity.com/paper/2208.08943/

## Key facts

- Cited by 1 papers 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 (most cited first, 3 of 3)

- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [Systems Mining with Heraklit: The Next Step](https://mlsanity.com/paper/2202.01289/) (2022)
- [Easy, adaptable and high-quality Modelling with domain-specific Constraint Patterns](https://mlsanity.com/paper/2206.02479/) (2022)

## Built on by (most cited first, 1 of 1)

- [Skill Learning Using Process Mining for Large Language Model Plan Generation](https://mlsanity.com/paper/2410.12870/) (2024)

## 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.