ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks. Hoffmann, M., Malburg, L., & Bergmann, R. In Marrella, A. & Weber, B., editors, Business Process Management Workshops - BPM 2021, Rome, Italy, September 6 - 10, 2021, volume 436, of Lecture Notes in Business Information Processing, pages 43–55, 2022. Springer.. The original publication is available at www.springerlink.com![ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex 18 downloads Monitoring the state of currently running processes and reacting to deviations during runtime is a key challenge in Business Process Management (BPM). The MAPE-K control loop describes four phases for approaching this challenge: Monitor, Analyze, Plan, Execute. In this paper, we present the ProGAN framework, an idea of an approach for implementing the monitor, analyze, and plan phases of MAPE-K. For this purpose, we leverage a deep learning architecture that builds upon Generative Adversarial Networks (GANs): The discriminator is used for monitoring the process in its environment by using sensor data and for detecting deviations w.r.t. the desired process state (monitor phase). The generator is used afterwards for analyzing the detected deviation and its symptoms as well as for adapting the current process to resolve the deviation and to restore the desired state. Both components are trained together by utilizing each other's feedback in a self-supervised way. We demonstrate the application of our approach for an exemplary scenario in the manufacturing domain.
@inproceedings{Hoffmann.2022_ProGAN,
title = {{ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks}},
author = {Maximilian Hoffmann and Lukas Malburg and Ralph Bergmann},
year = 2022,
booktitle = {Business Process Management Workshops - {BPM} 2021, Rome, Italy, September 6 - 10, 2021},
publisher = {Springer.},
series = {Lecture Notes in Business Information Processing},
volume = 436,
pages = {43--55},
doi = {10.1007/978-3-030-94343-1\_4},
url = {https://doi.org/10.1007/978-3-030-94343-1\_4},
url = {http://www.wi2.uni-trier.de/shared/publications/2022_AI4BPM_HoffmannEtal_ProGAN.pdf},
note = {The original publication is available at www.springerlink.com},
editor = {Andrea Marrella and Barbara Weber},
keywords = {{Business process prediction, Generative Adversarial Networks, Flexibility by change, Process adaptation}},
abstract = {Monitoring the state of currently running processes and reacting to deviations during runtime is a key challenge in Business Process Management (BPM). The MAPE-K control loop describes four phases for approaching this challenge: Monitor, Analyze, Plan, Execute. In this paper, we present the ProGAN framework, an idea of an approach for implementing the monitor, analyze, and plan phases of MAPE-K. For this purpose, we leverage a deep learning architecture that builds upon Generative Adversarial Networks (GANs): The discriminator is used for monitoring the process in its environment by using sensor data and for detecting deviations w.r.t. the desired process state (monitor phase). The generator is used afterwards for analyzing the detected deviation and its symptoms as well as for adapting the current process to resolve the deviation and to restore the desired state. Both components are trained together by utilizing each other's feedback in a self-supervised way. We demonstrate the application of our approach for an exemplary scenario in the manufacturing domain.}
}
Downloads: 18
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