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.
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All datasets used in this paper to support the findings are publicly available. Links are reported in the bibliography.
For the sake of simplicity, we directly show the projected trace obtained by another trace from the Helpdesk log. Furthermore, for the sake of readability, we only use activity acronyms.
Note that for deviations occurring within parallel constructs other repair configurations are available, e.g., by adding an additional parallel branch involving the inserted activities. Refer to Diamantini et al. (2016) for additional details.
Here we refer to the state-of-the-art notion of fitness proposed by Adriansyah et al. (2011)
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Chiorrini, A., Diamantini, C., Genga, L. et al. Multi-perspective enriched instance graphs for next activity prediction through graph neural network. J Intell Inf Syst 61, 5–25 (2023). https://doi.org/10.1007/s10844-023-00777-1