Imminence Monitoring of Critical Events: A Representation Learning Approach
Imminence Monitoring of Critical Events: A Representation Learning Approach
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DOI：
10.1145/3448016.3452804
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发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Yan Li;Tingjian Ge
中科院分区：
文献类型：
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作者：
Yan Li;Tingjian Ge
复杂事件监测是数据流领域的一个重要问题，受到了广泛的关注。以前的大多数工作都假设用户知道并提供复杂的事件模式供系统持续监控。然而，我们观察到，在许多真实的应用程序中，例如医疗保健、安全和业务，存在异构的子流和不同的属性集。在关键事件之前，通常没有简单统一的模式;也没有干净简单的语言来描述导致关键事件的模式。人们往往只在事后才知道--例如，当一些不好的事情发生时。我们提出了一种基于关系机器学习和表示学习的新方法。我们提出并学习概率状态机模式，用于监测和预测紧急事件的发生。我们的实验证明了我们的方法的效率和有效性，以及其明显优于最接近的先前方法，如IL-Miner和基于LSTM的早期预测。
Complex event monitoring is an important problem in data streams that has drawn much attention. Most previous work assumes that the user knows and provides a complex event pattern for the system to continuously monitor. However, we observe that in many real applications, such as healthcare, security, and businesses, there are heterogeneous substreams and a diverse set of attributes. Often there is no simple uniform pattern prior to a critical event; nor is there clean simple language to describe the pattern leading to the critical event. People often only know it after the fact -- e.g., when something undesirable happens. We propose a novel approach based on relational machine learning and representation learning. We propose and learn probabilistic state machine patterns, which are used to monitor and predict the imminence of critical events. Our experiments demonstrate the efficiency and effectiveness of our approach, as well as its clear superiority over the closest previous approaches such as IL-Miner and LSTM based early prediction.