Business Process Simulation represents a powerful instrument for business analysts when analyzing and comparing business processes. Most of the state-of-the-art business process simulators, however, rely on Discrete event simulation, which requires various unrealistic assumptions and simplifications to perform experiments. Predictive Process Monitoring, on the other hand, offers a viable way to complete ongoing traces or to generate entire traces from scratch, via predictions of the next activities and their attributes. Predictive models, though, are usually based on black-box approaches that make it difficult to reason on what-if scenarios. RIMS_Tool is a hybrid business process simulator that aims at combining predictive models built from data and Discrete event simulation at runtime in a white-box manner. The proposed tool, thus, is able to exploit the strengths and avoid the limitations of both approaches.

ICPM Doctoral Consortium and Demo Track 2023 / Meneghello, Francesca; Di Francescomarino, Chiara; Ghidini, Chiara. - (2024). (Intervento presentato al convegno Doctoral Consortium and Demo Track 2023 at the International Conference on Process Mining 2023 co-located with the 5th International Conference on Process Mining (ICPM 2023) tenutosi a Rome, Italy).

ICPM Doctoral Consortium and Demo Track 2023

Francesca Meneghello
;
2024

Abstract

Business Process Simulation represents a powerful instrument for business analysts when analyzing and comparing business processes. Most of the state-of-the-art business process simulators, however, rely on Discrete event simulation, which requires various unrealistic assumptions and simplifications to perform experiments. Predictive Process Monitoring, on the other hand, offers a viable way to complete ongoing traces or to generate entire traces from scratch, via predictions of the next activities and their attributes. Predictive models, though, are usually based on black-box approaches that make it difficult to reason on what-if scenarios. RIMS_Tool is a hybrid business process simulator that aims at combining predictive models built from data and Discrete event simulation at runtime in a white-box manner. The proposed tool, thus, is able to exploit the strengths and avoid the limitations of both approaches.
2024
Doctoral Consortium and Demo Track 2023 at the International Conference on Process Mining 2023 co-located with the 5th International Conference on Process Mining (ICPM 2023)
Business Process Simulation, Machine Learning, Hybrid Simulation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
ICPM Doctoral Consortium and Demo Track 2023 / Meneghello, Francesca; Di Francescomarino, Chiara; Ghidini, Chiara. - (2024). (Intervento presentato al convegno Doctoral Consortium and Demo Track 2023 at the International Conference on Process Mining 2023 co-located with the 5th International Conference on Process Mining (ICPM 2023) tenutosi a Rome, Italy).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1726848
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