1 code implementation • 13 Oct 2023 • Suhwan Lee, Marco Comuzzi, Xixi Lu, Hajo A. Reijers
This paper proposes an evaluation framework for assessing the stability of models for online predictive process monitoring.
no code implementations • 24 Mar 2022 • Gyunam Park, Marco Comuzzi, Wil M. P. van der Aalst
In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates.
no code implementations • 15 Feb 2022 • Williams Rizzi, Marco Comuzzi, Chiara Di Francescomarino, Chiara Ghidini, Suhwan Lee, Fabrizio Maria Maggi, Alexander Nolte
The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them.
1 code implementation • 1 Mar 2021 • Jonghyeon Ko, Marco Comuzzi
The proposed approach has been evaluated on both artificial and real event streams.
no code implementations • 24 Aug 2020 • Bayu Adhi Tama, Marco Comuzzi, Jonghyeon Ko
While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available.