Abstract
Today’s organizations store lots of data tracking the execution of their business processes. These data often contain valuable information that can be used to predict the evolution of running process executions. The present paper investigates the combined use of Instance Graphs and Deep Graph Convolutional Neural Networks to predict which activity will be performed next given a partial process execution. In addition to the exploitation of graph structures to encode the control-flow information, we investigate how to couple it with additional data perspectives. Experiments show the feasibility of the proposed approach, whose outcomes are consistently placed in the top ranking then compared to those obtained by well-known state-of-the-art approaches.
Original language | English |
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Pages (from-to) | 5-25 |
Number of pages | 21 |
Journal | Journal of Intelligent Information Systems |
Volume | 61 |
Issue number | 1 |
Early online date | 1 May 2023 |
DOIs | |
Publication status | Published - Aug 2023 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Keywords
- Graph neural network
- Instance graph
- Next activity prediction
- Predictive process monitoring
- Process mining