Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Kubrak, Kateryna, Milani, Fredrik, Nolte, Alexander, Dumas, Marlon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
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
_version_ 1784811409011376128
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