UniParser：一个统一的异构日志数据解析器
UniParser: A Unified Log Parser for Heterogeneous Log Data
摘要：
它为工程师诊断大规模在线服务系统中的故障提供第一手信息。日志解析将半结构化的原始日志信息转换为结构化的数据，是实现基于日志的异常检测和诊断等自动化日志分析的前提。现有的日志解析器都遵循将公共部分提取为模板，动态部分提取为参数的基本思想。然而，这些日志解析方法，往往忽略了日志消息的语义意义。此外，各种日志源之间的高度多样性也构成了跨不同系统的日志解析的通用化的障碍。在本文中，我们提出了UniParser捕获常见的日志行为从异构日志数据。UniParser利用令牌编码器模块和上下文编码器模块从日志令牌及其相邻上下文中学习模式。上下文相似性模块是专门设计来模拟学习模式的共性。我们已经在16个公共日志数据集上进行了大量的实验，我们的结果表明，UniParser大大优于最先进的日志解析器。
英文摘要：
Logs provide first-hand information for engineers to diagnose failures in large-scale online service systems. Log parsing, which transforms semi-structured raw log messages into structured data, is a prerequisite of automated log analysis such as log-based anomaly detection and diagnosis. Almost all existing log parsers follow the general idea of extracting the common part as templates and the dynamic part as parameters. However, these log parsing methods, often neglect the semantic meaning of log messages. Furthermore, high diversity among various log sources also poses an obstacle in the generalization of log parsing across different systems. In this paper, we propose UniParser to capture the common logging behaviours from heterogeneous log data. UniParser utilizes a Token Encoder module and a Context Encoder module to learn the patterns from the log token and its neighbouring context. A Context Similarity module is specially designed to model the commonalities of learned patterns. We have performed extensive experiments on 16 public log datasets and our results show that UniParser outperperforms state-of-the-art log parsers by a large margin.
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。
