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Discovering generative models from event logs: data-driven simulation vs deep learning
A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business proces...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293933/ https://www.ncbi.nlm.nih.gov/pubmed/34322588 http://dx.doi.org/10.7717/peerj-cs.577 |
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author | Camargo, Manuel Dumas, Marlon González-Rojas, Oscar |
author_facet | Camargo, Manuel Dumas, Marlon González-Rojas, Oscar |
author_sort | Camargo, Manuel |
collection | PubMed |
description | A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths. |
format | Online Article Text |
id | pubmed-8293933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82939332021-07-27 Discovering generative models from event logs: data-driven simulation vs deep learning Camargo, Manuel Dumas, Marlon González-Rojas, Oscar PeerJ Comput Sci Artificial Intelligence A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths. PeerJ Inc. 2021-07-12 /pmc/articles/PMC8293933/ /pubmed/34322588 http://dx.doi.org/10.7717/peerj-cs.577 Text en © 2021 Camargo 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 | Artificial Intelligence Camargo, Manuel Dumas, Marlon González-Rojas, Oscar Discovering generative models from event logs: data-driven simulation vs deep learning |
title | Discovering generative models from event logs: data-driven simulation vs deep learning |
title_full | Discovering generative models from event logs: data-driven simulation vs deep learning |
title_fullStr | Discovering generative models from event logs: data-driven simulation vs deep learning |
title_full_unstemmed | Discovering generative models from event logs: data-driven simulation vs deep learning |
title_short | Discovering generative models from event logs: data-driven simulation vs deep learning |
title_sort | discovering generative models from event logs: data-driven simulation vs deep learning |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293933/ https://www.ncbi.nlm.nih.gov/pubmed/34322588 http://dx.doi.org/10.7717/peerj-cs.577 |
work_keys_str_mv | AT camargomanuel discoveringgenerativemodelsfromeventlogsdatadrivensimulationvsdeeplearning AT dumasmarlon discoveringgenerativemodelsfromeventlogsdatadrivensimulationvsdeeplearning AT gonzalezrojasoscar discoveringgenerativemodelsfromeventlogsdatadrivensimulationvsdeeplearning |