Azure Core Workload Insights中的高显著性故障检测
High Significant Fault Detection in Azure Core Workload Insights
AI总结：
本文研究Azure Core时间序列数据中的高显著性故障检测，提出自动识别具有高用户感知度和高重建误差的异常（如每小时5-20个）及其关联信息，以便在仪表板上向用户突出显示。
摘要：
Azure Core workload insights包含具有不同度量单位的时间序列数据。由于与数据相关的度量名称、资源区域、维度及其维度值方面出现的故障，在这些时间序列数据中观察到了故障或异常。对于Azure Core而言，一项重要任务是在仪表板上向用户突出显示他们能够轻松感知的故障或异常。报告的异常数量应具有高度显著性且数量有限，例如每小时报告5-20个异常。所报告的异常将具有显著的用户感知度，并且在任何时间序列预测模型中都具有高重建误差。因此，我们的任务是自动识别“高显著性异常”及其相关信息以供用户感知。
英文摘要：
Azure Core workload insights have time-series data with different metric units. Faults or Anomalies are observed in these time-series data owing to faults observed with respect to metric name, resources region, dimensions, and its dimension value associated with the data. For Azure Core, an important task is to highlight faults or anomalies to the user on a dashboard that they can perceive easily. The number of anomalies reported should be highly significant and in a limited number, e.g., 5-20 anomalies reported per hour. The reported anomalies will have significant user perception and high reconstruction error in any time-series forecasting model. Hence, our task is to automatically identify 'high significant anomalies' and their associated information for user perception.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
