  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.
