论文标题
基于文档的语义意识日志解析
Documentation based Semantic-Aware Log Parsing
论文作者
论文摘要
随着深度学习技术的最新进展，将机器学习应用到数据数据中存在迅速增长的兴趣。作为日志分析的基本部分，将原始日志转换为结构化事件的准确日志解析对于随后的机器学习和数据挖掘任务至关重要。先前的方法要么分析解析的源代码，要么是数据驱动的，例如文本群集。他们在很大程度上忽略了利用另一个可用的可用和宝贵的资源，即软件文档，可为消息提供详细的解释，以提高准确性。在本文中，我们提出了一种方法和系统框架，以使用文档知识进行日志解析。有了参数值识别，它不仅可以提高已记录消息的解析精度，而且可以提高无证件消息的解析精度。此外，它可以发现通过共享参数建立的事件模板之间的链接并指示事件上下文的相关性。
With the recent advances of deep learning techniques, there are rapidly growing interests in applying machine learning to log data. As a fundamental part of log analytics, accurate log parsing that transforms raw logs to structured events is critical for subsequent machine learning and data mining tasks. Previous approaches either analyze the source code for parsing or are data-driven such as text clustering. They largely neglect to exploit another widely available and valuable resource, software documentation that provides detailed explanations for the messages, to improve accuracy. In this paper, we propose an approach and system framework to use documentation knowledge for log parsing. With parameter value identification, it not only can improve the parsing accuracy for documented messages but also for undocumented messages. In addition, it can discover the linkages between event templates that are established by sharing parameters and indicate the correlation of the event context.
