Event-based Failure Prediction in Distributed Business Processes
This addresses the need for improved failure prediction in increasingly distributed business processes, which is an incremental advancement over traditional centralized methods.
The paper tackles the problem of predicting failures in distributed business processes by employing an event-based approach, achieving high accuracy in error detection and failure prediction as demonstrated on two datasets including a real-world event log.
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.