2017/12/27 by Michael Borkowski, Walid Fdhila, Matteo Nardelli +3
Business, Management and Accounting · Computer Science · #Artifact-centric business process model #Artificial intelligence #Business Process Modeling and Analysis #Business process #Business process discovery #Business process management #Business process modeling #Business rule #Complex event processing #Computer science #Data mining #Data science #Distributed computing #Event (particle physics) #Focus (optics) #Operations management #Process (computing) #Process management #Process mining #Process modeling #Service-Oriented Architecture and Web Services #Software System Performance and Reliability #Work in process #cs.DC #cs.SE
paper · pdf · doi:10.1016/j.is.2017.12.005
openalex publication_date 2017/12/27 · arxiv created 2018/01/09 · arxiv updated 2018/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Traditionally, research in Business Process Management has put a strong focus on centralized and intra-organizational processes. However, today's business processes are increasingly distributed, deviating from a centralized layout, and therefore calling for novel methodologies of detecting and responding to unforeseen events, such as errors occurring during process runtime. In this article, we demonstrate how to employ event-based failure prediction in business processes. This approach allows to make use of the best of both traditional Business Process Management Systems and event-based systems. Our approach employs machine learning techniques and considers various types of events. We evaluate our solution using two business process data sets, including one from a real-world event log, and show that we are able to detect errors and predict failures with high accuracy.