Abstract
Predictive process monitoring approaches aim to make predictions about the future behavior for running instances of business processes, such as next activity or remaining time. Most of these approaches use single object type event logs as if the business process is operating in isolation. Whereas, in an organization, several instances of different processes related to a set of objects can be executed at the same time and may interact with each other. This paper investigate the use of object-centric event logs as they offer information about events and their related objects, allowing access to a global view about the running processes in an organization. We propose an object-centric predictive approach considering interactions between different object types. The proposed approach is evaluated on a publicly available object-centric log. The analysis of the results shows that using additional features (i.e., several object types’ information) can generally help increase prediction performances.
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Softmax function: \(\sigma (z_i) = \frac{e^{z_{i}}}{\sum _{j=1}^K e^{z_{j}}} \ \ \ for\ i=1,2,\dots ,K\).
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Gherissi, W., El Haddad, J., Grigori, D. (2023). Object-Centric Predictive Process Monitoring. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_3
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