Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is important to understand which states of the system trigger them. For this purpose, we propose a modeling framework based on continuous-time Bayesian networks (CTBNs) to analyze cascading behavior in complex systems. This framework allows us to describe how events propagate through the system and to identify likely sentry states, that is, system states that may lead to imminent cascading behavior. Moreover, CTBNs have a simple graphical representation and provide interpretable outputs, both of which are important when communicating with domain experts. We also develop new methods for knowledge extraction from CTBNs and we apply the proposed methodology to a data set of alarms in a large industrial system.

翻译：事件交互系统可能表现出级联行为，即事件在时间上趋于聚类。虽然级联现象本身可从数据中明显识别，但理解触发级联的系统状态至关重要。为此，我们提出一种基于连续时间贝叶斯网络（CTBNs）的建模框架，用于分析复杂系统中的级联行为。该框架能够描述事件如何在系统中传播，并识别可能的哨兵状态——即可能导致级联行为临近的系统状态。此外，CTBNs具有简洁的图形表示和可解释的输出，这两点在与领域专家沟通时尤为重要。我们还开发了从CTBNs中提取知识的新方法，并将所提出的方法应用于大型工业系统的告警数据集。