Extending predictive process monitoring for collaborative processes

Calegari, Daniel - Delgado, Andrea

Resumen:

Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.


Detalles Bibliográficos
2024
Agencia Nacional de Investigación e Innovación
Process mining
Inter-organizational collaborative processes
Predictive process monitoring
Ciencias Naturales y Exactas
Ciencias de la Computación e Información
Ciencias de la Computación
Inglés
Agencia Nacional de Investigación e Innovación
REDI
https://hdl.handle.net/20.500.12381/3704
https://doi.org/10.48550/arXiv.2409.09212
Acceso abierto
Reconocimiento 4.0 Internacional. (CC BY)
_version_ 1816771190996336640
author Calegari, Daniel
author2 Delgado, Andrea
author2_role author
author_facet Calegari, Daniel
Delgado, Andrea
author_role author
bitstream.checksum.fl_str_mv a4ce09f01b5dd771727aa05c73851623
4374f4d3e5c2e0a556e37c00e46325e7
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
bitstream.url.fl_str_mv https://redi.anii.org.uy/jspui/bitstream/20.500.12381/3704/2/license.txt
https://redi.anii.org.uy/jspui/bitstream/20.500.12381/3704/1/2409.09212v1.pdf
collection REDI
dc.creator.none.fl_str_mv Calegari, Daniel
Delgado, Andrea
dc.date.accessioned.none.fl_str_mv 2024-11-22T18:04:11Z
dc.date.available.none.fl_str_mv 2024-11-22T18:04:11Z
dc.date.issued.none.fl_str_mv 2024
dc.description.abstract.none.fl_txt_mv Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.
dc.description.sponsorship.none.fl_txt_mv Agencia Nacional de Investigación e Innovación
dc.identifier.anii.es.fl_str_mv FMV_1_2021_1_167483
dc.identifier.doi.none.fl_str_mv https://doi.org/10.48550/arXiv.2409.09212
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12381/3704
dc.language.iso.none.fl_str_mv eng
dc.relation.uri.es.fl_str_mv https://hdl.handle.net/20.500.12381/3700
https://hdl.handle.net/20.500.12381/3701
https://hdl.handle.net/20.500.12381/3702
https://hdl.handle.net/20.500.12381/3703
dc.rights.*.fl_str_mv Acceso abierto
dc.rights.license.none.fl_str_mv Reconocimiento 4.0 Internacional. (CC BY)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.es.fl_str_mv 6th International Conference on Process Mining (ICPM), 3rd Workshop on Collaboration Mining for Distributed Systems (COMINDS), Copenhague, Dinamarca, 14 al 18 de Octubre, 2024
dc.source.none.fl_str_mv reponame:REDI
instname:Agencia Nacional de Investigación e Innovación
instacron:Agencia Nacional de Investigación e Innovación
dc.subject.anii.none.fl_str_mv Ciencias Naturales y Exactas
Ciencias de la Computación e Información
Ciencias de la Computación
dc.subject.es.fl_str_mv Process mining
Inter-organizational collaborative processes
Predictive process monitoring
dc.title.none.fl_str_mv Extending predictive process monitoring for collaborative processes
dc.type.es.fl_str_mv Documento de conferencia
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.type.version.es.fl_str_mv Publicado
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
description Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.
eu_rights_str_mv openAccess
format conferenceObject
id REDI_b177fde2253c8570a09c4300a5d4fda3
identifier_str_mv FMV_1_2021_1_167483
instacron_str Agencia Nacional de Investigación e Innovación
institution Agencia Nacional de Investigación e Innovación
instname_str Agencia Nacional de Investigación e Innovación
language eng
network_acronym_str REDI
network_name_str REDI
oai_identifier_str oai:redi.anii.org.uy:20.500.12381/3704
publishDate 2024
reponame_str REDI
repository.mail.fl_str_mv jmaldini@anii.org.uy
repository.name.fl_str_mv REDI - Agencia Nacional de Investigación e Innovación
repository_id_str 9421
rights_invalid_str_mv Reconocimiento 4.0 Internacional. (CC BY)
Acceso abierto
spelling Reconocimiento 4.0 Internacional. (CC BY)Acceso abiertoinfo:eu-repo/semantics/openAccess2024-11-22T18:04:11Z2024-11-22T18:04:11Z2024https://hdl.handle.net/20.500.12381/3704FMV_1_2021_1_167483https://doi.org/10.48550/arXiv.2409.09212Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.Agencia Nacional de Investigación e Innovaciónenghttps://hdl.handle.net/20.500.12381/3700https://hdl.handle.net/20.500.12381/3701https://hdl.handle.net/20.500.12381/3702https://hdl.handle.net/20.500.12381/37036th International Conference on Process Mining (ICPM), 3rd Workshop on Collaboration Mining for Distributed Systems (COMINDS), Copenhague, Dinamarca, 14 al 18 de Octubre, 2024reponame:REDIinstname:Agencia Nacional de Investigación e Innovacióninstacron:Agencia Nacional de Investigación e InnovaciónProcess miningInter-organizational collaborative processesPredictive process monitoringCiencias Naturales y ExactasCiencias de la Computación e InformaciónCiencias de la ComputaciónExtending predictive process monitoring for collaborative processesDocumento de conferenciaPublicadoinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectUniversidad de la República. Facultad de Ingeniería. Instituto de Computación//Ciencias Naturales y Exactas/Ciencias de la Computación e Información/Ciencias de la ComputaciónCalegari, DanielDelgado, AndreaLICENSElicense.txtlicense.txttext/plain; charset=utf-84967https://redi.anii.org.uy/jspui/bitstream/20.500.12381/3704/2/license.txta4ce09f01b5dd771727aa05c73851623MD52ORIGINAL2409.09212v1.pdf2409.09212v1.pdfversión publicada en arXivapplication/pdf7326985https://redi.anii.org.uy/jspui/bitstream/20.500.12381/3704/1/2409.09212v1.pdf4374f4d3e5c2e0a556e37c00e46325e7MD5120.500.12381/37042024-11-22 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- Agencia Nacional de Investigación e Innovaciónfalse
spellingShingle Extending predictive process monitoring for collaborative processes
Calegari, Daniel
Process mining
Inter-organizational collaborative processes
Predictive process monitoring
Ciencias Naturales y Exactas
Ciencias de la Computación e Información
Ciencias de la Computación
status_str publishedVersion
title Extending predictive process monitoring for collaborative processes
title_full Extending predictive process monitoring for collaborative processes
title_fullStr Extending predictive process monitoring for collaborative processes
title_full_unstemmed Extending predictive process monitoring for collaborative processes
title_short Extending predictive process monitoring for collaborative processes
title_sort Extending predictive process monitoring for collaborative processes
topic Process mining
Inter-organizational collaborative processes
Predictive process monitoring
Ciencias Naturales y Exactas
Ciencias de la Computación e Información
Ciencias de la Computación
url https://hdl.handle.net/20.500.12381/3704
https://doi.org/10.48550/arXiv.2409.09212