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