论文标题
基于事件日志分析的故障检测和预测的功能选择
Feature Selection for Fault Detection and Prediction based on Event Log Analysis
论文作者
论文摘要
事件日志被广泛用于复杂系统中的异常检测和预测。现有的基于日志的异常检测方法通常包括四个主要步骤：日志收集，日志解析，特征提取和异常检测，其中特征提取步骤提取有用的特征，可通过计数日志事件来进行异常检测。对于一个复杂的系统，例如由大量子系统组成的光刻机器，其日志可能包含数千个不同的事件，从而导致提取的功能丰富。但是，当在子系统级别进行异常检测时，分析所有功能变得昂贵且不必要。为了减轻此问题，我们为基于日志的异常检测和预测开发了一种功能选择方法，从而在很大程度上提高了有效性和效率。
Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the feature extraction step extracts useful features for anomaly detection by counting log events. For a complex system, such as a lithography machine consisting of a large number of subsystems, its log may contain thousands of different events, resulting in abounding extracted features. However, when anomaly detection is performed at the subsystem level, analyzing all features becomes expensive and unnecessary. To mitigate this problem, we develop a feature selection method for log-based anomaly detection and prediction, largely improving the effectiveness and efficiency.
