Event-based Failure Prediction in Distributed Business Processes

TL;DR

This paper introduces an event-based failure prediction method for distributed business processes, combining traditional BPM systems with event-based systems using machine learning to accurately detect and predict errors in real-world scenarios.

Contribution

It presents a novel approach that integrates event-based failure prediction into distributed business processes, leveraging machine learning and multiple event types.

Findings

High accuracy in error detection and failure prediction

Effective on real-world business process data

Combines traditional BPM with event-based systems

Abstract

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.