dc.creator | Márquez Chamorro, Alfonso Eduardo | es |
dc.creator | Resinas Arias de Reyna, Manuel | es |
dc.creator | Ruiz Cortés, Antonio | es |
dc.creator | Toro Bonilla, Miguel | es |
dc.date.accessioned | 2020-09-24T10:09:31Z | |
dc.date.available | 2020-09-24T10:09:31Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Márquez Chamorro, A.E., Resinas Arias de Reyna, M., Ruiz Cortés, A. y Toro Bonilla, M. (2017). Run-time prediction of business process indicators using evolutionary decision rules. Expert Systems with Applications, 87 (november 2017), 1-14. | |
dc.identifier.issn | 0957-4174 | es |
dc.identifier.uri | https://hdl.handle.net/11441/101431 | |
dc.description.abstract | Predictive monitoring of business processes is a challenging topic of process mining which is concerned with the prediction of process indicators of running process instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for predictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process management systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS). | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2015-70560-R | es |
dc.description.sponsorship | Junta de Andalucía P12TIC-1867 | es |
dc.format | application/pdf | es |
dc.format.extent | 14 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Expert Systems with Applications, 87 (november 2017), 1-14. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Business Process Management | es |
dc.subject | Process mining | es |
dc.subject | Predictive monitoring | es |
dc.subject | Business process indicator | es |
dc.subject | Evolutionary algorithm | es |
dc.title | Run-time prediction of business process indicators using evolutionary decision rules | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2015-70560-R | es |
dc.relation.projectID | P12TIC-1867 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417417303950 | es |
dc.identifier.doi | 10.1016/j.eswa.2017.05.069 | es |
dc.journaltitle | Expert Systems with Applications | es |
dc.publication.volumen | 87 | es |
dc.publication.issue | november 2017 | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 14 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Junta de Andalucía | es |