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Prescriptive process monitoring: Quo vadis?
Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or address poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This article studies existing met...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575877/ https://www.ncbi.nlm.nih.gov/pubmed/36262156 http://dx.doi.org/10.7717/peerj-cs.1097 |
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author | Kubrak, Kateryna Milani, Fredrik Nolte, Alexander Dumas, Marlon |
author_facet | Kubrak, Kateryna Milani, Fredrik Nolte, Alexander Dumas, Marlon |
author_sort | Kubrak, Kateryna |
collection | PubMed |
description | Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or address poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This article studies existing methods in this field via a systematic literature review (SLR). In order to structure the field, this article proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. This article highlights the need to validate existing and new methods in real-world settings, extend the types of interventions beyond those related to the temporal and cost perspectives, and design policies that take into account causality and second-order effects. |
format | Online Article Text |
id | pubmed-9575877 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95758772022-10-18 Prescriptive process monitoring: Quo vadis? Kubrak, Kateryna Milani, Fredrik Nolte, Alexander Dumas, Marlon PeerJ Comput Sci Data Science Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or address poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This article studies existing methods in this field via a systematic literature review (SLR). In order to structure the field, this article proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. This article highlights the need to validate existing and new methods in real-world settings, extend the types of interventions beyond those related to the temporal and cost perspectives, and design policies that take into account causality and second-order effects. PeerJ Inc. 2022-09-29 /pmc/articles/PMC9575877/ /pubmed/36262156 http://dx.doi.org/10.7717/peerj-cs.1097 Text en ©2022 Kubrak et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Data Science Kubrak, Kateryna Milani, Fredrik Nolte, Alexander Dumas, Marlon Prescriptive process monitoring: Quo vadis? |
title | Prescriptive process monitoring: Quo vadis? |
title_full | Prescriptive process monitoring: Quo vadis? |
title_fullStr | Prescriptive process monitoring: Quo vadis? |
title_full_unstemmed | Prescriptive process monitoring: Quo vadis? |
title_short | Prescriptive process monitoring: Quo vadis? |
title_sort | prescriptive process monitoring: quo vadis? |
topic | Data Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575877/ https://www.ncbi.nlm.nih.gov/pubmed/36262156 http://dx.doi.org/10.7717/peerj-cs.1097 |
work_keys_str_mv | AT kubrakkateryna prescriptiveprocessmonitoringquovadis AT milanifredrik prescriptiveprocessmonitoringquovadis AT noltealexander prescriptiveprocessmonitoringquovadis AT dumasmarlon prescriptiveprocessmonitoringquovadis |