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.

在业务流程实例执行期间预测不良事件将为流程参​​与者提供机会，使其干预并使流程与其目标保持一致。解决这种挑战的方法很少考虑多角度的视图，其中流程的流程角度与周围环境相结合。鉴于当今世界的数据来源众多，上下文可能千差万别，并具有多种含义。本文讨论了上下文是下一个事件的原因或结果及其对下一个事件预测的影响的问题。我们利用先前在概率模型上的工作来开发动态贝叶斯网络技术。概率模型被认为是可理解的，它们使最终用户及其对域的理解可以参与预测。我们的技术对与事件具有因果关系的上下文属性进行建模。我们使用两个真实的数据集评估我们的技术，并使用预测性过程监控领域的其他技术对其进行基准测试。结果表明，如果将上下文信息正确引入模型中，我们的解决方案将获得出色的预测结果。