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This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining, aiming to tackle its inherent complexity and diverse skill requirements.
Language models are few-shot learners,
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B. van Dongen, Dataset bpi challenge 2019, 4TU.Centre for Research Data, 2019. URL: https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1 . doi: 10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1
2019
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Process mining in action,
L. Reinkemeyer, · 2020
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Recommendations for enhancing the usability and understandability of process mining in healthcare,
N. Martin, J. De Weerdt, C. Fernández-Llatas, A. Gal, R. Gatta, G. Ibáñez, O. Johnson, F. Mannhardt, L. Marco-Ruiz, S. Mertens, et al., · 2020
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J. vom Brocke, M. Jans, J. Mendling, H. A. Reijers, A five-level framework for research on process mining, 2021
2021
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J. Munoz-Gama, X. Lu, Towards a natural language conversational interface for process mining. In Process Mining Workshops: ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31–November 4, 2021, Revised Selected Papers, Springer Nature, 2022
2022
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Process mining: a 360 degree overview,
W. M. van der Aalst, · 2022
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Large language models are zero-shot reasoners,
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A natural language querying interface for process mining,
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