论文标题
LogStamp：基于序列标签的自动在线日志解析
LogStamp: Automatic Online Log Parsing Based on Sequence Labelling
论文作者
论文摘要
日志是服务管理的最关键数据之一。它包含服务和用户的丰富运行时信息。由于日志的大小通常是巨大的，并且具有免费的手写结构，因此首先将基于日志的分析首先分析为结构化格式。但是，我们观察到大多数现有的日志解析方法无法在线解析日志，这对于在线服务至关重要。在本文中，我们提出了一种自动在线日志解析方法，名称为LogStamp。我们广泛评估了五个公共数据集上的LogStamp，以证明我们提出的方法的有效性。实验表明，只有一小部分训练集，我们提出的方法就可以达到高精度。例如，当仅使用10％的数据培训时，它的平均准确度可以达到0.956。
Logs are one of the most critical data for service management. It contains rich runtime information for both services and users. Since size of logs are often enormous in size and have free handwritten constructions, a typical log-based analysis needs to parse logs into structured format first. However, we observe that most existing log parsing methods cannot parse logs online, which is essential for online services. In this paper, we present an automatic online log parsing method, name as LogStamp. We extensively evaluate LogStamp on five public datasets to demonstrate the effectiveness of our proposed method. The experiments show that our proposed method can achieve high accuracy with only a small portion of the training set. For example, it can achieve an average accuracy of 0.956 when using only 10% of the data training.
