Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances

TL;DR

This paper introduces a Dynamic Bayesian Network approach for predicting undesirable events in business processes, emphasizing the importance of understanding whether context acts as a cause or effect, and demonstrates improved accuracy with real-world data.

Contribution

It presents a novel probabilistic model that explicitly distinguishes cause-effect relationships of context attributes in process prediction.

Findings

Superior prediction accuracy when context is correctly modeled

Effective modeling of cause-effect relationships improves predictions

Benchmark results outperform existing techniques

Abstract

Predicting undesirable events during the execution of a business process instance provides the process participants with an opportunity to intervene and keep the process aligned with its goals. Few approaches for tackling this challenge consider a multi-perspective view, where the flow perspective of the process is combined with its surrounding context. Given the many sources of data in today's world, context can vary widely and have various meanings. This paper addresses the issue of context being cause or effect of the next event and its impact on next event prediction. We leverage previous work on probabilistic models to develop a Dynamic Bayesian Network technique. Probabilistic models are considered comprehensible and they allow the end-user and his or her understanding of the domain to be involved in the prediction. Our technique models context attributes that have either a cause or effect relationship towards the event. We evaluate our technique with two real-life data sets and benchmark it with other techniques from the field of predictive process monitoring. The results show that our solution achieves superior prediction results if context information is correctly introduced into the model.