Abstract
Nowadays predictive process mining is playing a fundamental role in the business scenario as it is emerging as an effective means to monitor the execution of any business running process. In particular, knowing in advance the next activity of a running process instance may foster an optimal management of resources and promptly trigger remedial operations to be carried out. The problem of next activity prediction has been already tackled in the literature by formulating several machine learning and process mining approaches. In particular, the successful milestones achieved in computer vision by deep artificial neural networks have recently inspired the application of such architectures in several fields. The original contribution of this work consists of paving the way for relating computer vision to process mining via deep neural networks. To this aim, the paper pioneers the use of an RGB encoding of process instances useful to train a 2-D Convolutional Neural Network based on Inception block. The empirical study proves the effectiveness of the proposed approach for next-activity prediction on different real-world event logs.
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For this deep neural network we have used the implementation provided in [12];.
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Acknowledgement
Authors wish to thank unknown reviewers for the useful suggestions provided to improve the final quality of the paper. The research of Vincenzo Pasquadibisceglie is funded by PON RI 2014-2020 - Big Data Analytics for Process Improvement in Organizational Development - CUP H94F18000260006. The work is partially supported by the POR Puglia FESR-FSE 2014-2020 - Asse prioritario 1 - Ricerca, sviluppo tecnologico, innovazione - Sub Azione 1.4.b bando Innolabs - Research project KOMETA (Knowledge Community for Efficient Training through Virtual Technologies), funded by Regione Puglia.
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Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D. (2020). Predictive Process Mining Meets Computer Vision. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management Forum. BPM 2020. Lecture Notes in Business Information Processing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-58638-6_11
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