Abstract
Next activity prediction is one of the most important problems concerning the operational monitoring of processes, that is, supporting the user in predicting the activity that will be executed as the next step during process execution. However, traditional algorithms do not cope with the presence of parallel activities, thus failing to devise accurate prediction of multiple parallel activities that will be simultaneously executed. Moreover, they often require a trace alignment pre-processing step, which can be infeasible during process executions. In this paper, we propose the ParallAct methodology, in which multi-target regression is used to predict the next parallel activities in event logs without the need of aligning traces during process executions. Experimental results show that the proposed solution achieve more accurate predictions compared to the single-target setting.
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References
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Acknowledgments
We acknowledge the support of the MIUR - Ministero dell’Istruzione dell’Università e della Ricerca through the project “TALIsMan - Tecnologie di Assistenza personALizzata per il Miglioramento della quAlità della vitA" (Grant ID: ARS01_01116), funding scheme PON RI 2014–2020.
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Ceci, M., Impedovo, A., Pellicani, A. (2020). Leveraging Multi-target Regression for Predicting the Next Parallel Activities in Event Logs. In: Koprinska, I., et al. ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_15
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DOI: https://doi.org/10.1007/978-3-030-65965-3_15
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