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)
Resumen:
Sumario: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.