Título : | Extending predictive process monitoring for collaborative processes |
Autor(es) : | Calegari, Daniel Delgado, Andrea |
Fecha de publicación : | 2024 |
Tipo de publicación: | Documento de conferencia |
Versión: | Publicado |
Publicado en: | 6th International Conference on Process Mining (ICPM), 3rd Workshop on Collaboration Mining for Distributed Systems (COMINDS), Copenhague, Dinamarca, 14 al 18 de Octubre, 2024 |
Areas del conocimiento : | Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Computación |
Otros descriptores : | Process mining Inter-organizational collaborative processes Predictive process monitoring |
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. |
URI / Handle: | https://hdl.handle.net/20.500.12381/3704 |
Recursos relacionados en REDI: | 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 |
DOI: | https://doi.org/10.48550/arXiv.2409.09212 |
Institución responsable del proyecto: | Universidad de la República. Facultad de Ingeniería. Instituto de Computación |
Financiadores: | Agencia Nacional de Investigación e Innovación |
Identificador ANII: | FMV_1_2021_1_167483 |
Nivel de Acceso: | Acceso abierto |
Licencia CC: | Reconocimiento 4.0 Internacional. (CC BY) |
Aparece en las colecciones: | Publicaciones de ANII |
Archivos en este ítem:
archivo | Descripción | Tamaño | Formato | ||
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2409.09212v1.pdf | Descargar | versión publicada en arXiv | 7.16 MB | Adobe PDF |
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Reconocimiento 4.0 Internacional. (CC BY)