Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competit...
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/c6cfaf8323724a16a6b6764029f4a06b |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competitiveness in the market. Predictive Business Process Monitoring (PBPM) can provide measures such as the prediction of the remaining time of an ongoing process instance (case) by taking past activities in running process instances into account, as based on the event logs of previously completed process instances. With the prediction provided, we expect that organizations can respond quickly to deviations from the desired process. In the context of the growing popularity of deep learning and the need to utilize heterogeneous representation of data; in this study, we derived a new deep-learning approach that utilizes two types of data representation based on a parallel-structure model, which consists of a convolutional neural network (CNN) and a multi-layer perceptron (MLP) with an embedding layer, to predict the remaining time. Conducting experiments with real-world datasets, we compared our proposed method against the existing deep-learning approach to confirm its utility for the provision of more precise prediction (as indicated by error metrics) relative to the baseline method. |
|---|