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Exploiting recurrent graph neural networks for suffix prediction in predictive monitoring

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Abstract

Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time —suffix prediction—. Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, while also disregarding structural information present in the process model. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that selects the most probable suffix according to both the structural information extracted from the process model and the information extracted from the log. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals.

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Acknowledgements

This work has received financial support from the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019-2022 ED431G-2019/04), the European Regional Development Fund (ERDF), which acknowledges the CiTIUS - Centro Singular de Investigación en Tecnoloxías Intelixentes da Universidade de Santiago de Compostela as a Research Center of the Galician University System, and the Spanish Ministry of Science and Innovation (grants PDC2021-121072-C21, PID2020-112623GB-I00, and TED2021-130374B-C21). Furthermore, the authors also wish to thank the supercomputer facilities provided by CESGA.

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Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Efrén Rama-Maneiro, Juan C. Vidal, Manuel Lama & Pablo Monteagudo-Lago

  2. Departamento de Electrónica e Computación, Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Juan C. Vidal

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  1. Efrén Rama-Maneiro
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  2. Juan C. Vidal
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  3. Manuel Lama
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  4. Pablo Monteagudo-Lago
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Contributions

E. R-M: Methodology, Software, Validation, Formal Analysis, Investigation, Data Curation, Writing—Original Draft, J.V: Conceptualization, Resources, Writing—Review & Editing, Visualization, Supervision, Project administration, Funding acquisition, Investigation M.L: Conceptualization, Resources, Writing—Review & Editing, Visualization, Supervision, Project administration, Funding acquisition, Investigation P. M-L Software, Validation, Investigation, Data Curation.

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Correspondence to Efrén Rama-Maneiro.

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Rama-Maneiro, E., Vidal, J.C., Lama, M. et al. Exploiting recurrent graph neural networks for suffix prediction in predictive monitoring. Computing 106, 3085–3111 (2024). https://doi.org/10.1007/s00607-024-01315-9

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