Multi-perspective enriched instance graphs for next activity prediction through graph neural network

Andrea Chiorrini (Corresponding author), Claudia Diamantini, Laura Genga, Domenico Potena

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
1 Downloads (Pure)

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 languageEnglish
Pages (from-to)5-25
Number of pages21
JournalJournal of Intelligent Information Systems
Volume61
Issue number1
Early online date1 May 2023
DOIs
Publication statusPublished - 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

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