2025/07/21 by Wissam Gherissi, Mehdi Acheli, Gherissi, Wissam +5 · 1 voice
Business, Management and Accounting · Computer Science · Decision Sciences · #Business Process Modeling and Analysis #Data Quality and Management #Semantic Web and Ontologies
paper · doi:10.1007/978-981-96-7238-7_5
openalex publication_date 2025/07/22 · crossref created 2025/07/22 · crossref issued 2025/07/23 · crossref published 2025/07/23 · crossref published-online 2025/07/23 · openalex created_date 2025/10/10 · crossref published-print 2026/01/01 · crossref deposited 2026/03/27 · crossref indexed 2026/06/08 · openalex updated_date 2026/07/29
Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In this paper, we propose an end-to-end model that predicts future process behavior, focusing on two tasks: next activity prediction and next event time. The proposed model employs a graph attention network to encode activities and their relationships, combined with an LSTM network to handle temporal dependencies. Evaluated on one reallife and three synthetic event logs, the model demonstrates competitive performance compared to state-of-the-art methods.