The demand for high-performance anomaly detection techniques of IoT data becomes urgent, especially in industry field. The anomaly identification and explanation in time series data is one essential task in IoT data mining. Since that the existing anomaly detection techniques focus on the identification of anomalies, the explanation of anomalies is not well-solved. We address the anomaly explanation problem for multivariate IoT data and propose a 3-step self-contained method in this paper. We formalize and utilize the domain knowledge in our method, and identify the anomalies by the violation of constraints. We propose set-cover-based anomaly explanation algorithms to discover the anomaly events reflected by violation features, and further develop knowledge update algorithms to improve the original knowledge set. Experimental results on real datasets from large-scale IoT systems verify that our method computes high-quality explanation solutions of anomalies. Our work provides a guide to navigate the explicable anomaly detection in both IoT fault diagnosis and temporal data cleaning.

中文翻译：

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探索数据和知识的多元工业数据组合异常解释

物联网数据高性能异常检测技术的需求变得迫在眉睫，尤其是在工业领域。时间序列数据中的异常识别和解释是物联网数据挖掘中的一项基本任务。由于现有的异常检测技术着重于异常的识别，因此对异常的解释还不能很好地解决。我们解决了多元物联网数据的异常解释问题，并提出了一种三步自包含的方法。我们在我们的方法中形式化和利用领域知识，并通过违反约束条件来识别异常。我们提出了一种基于集合覆盖的异常解释算法，以发现违规特征所反映的异常事件，并进一步开发知识更新算法来改进原始知识集。来自大规模物联网系统的真实数据集的实验结果证明，我们的方法能够计算出高质量的异常解释解。我们的工作提供了在物联网故障诊断和临时数据清除中导航可发现异常检测的指南。