Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubleshooting of Web applications but yet previous template mining algorithms fails to support it. We formulate three types of light-weight user feedbacks and based on them we design three atomic human-in-the-loop template mining algorithms. We derive mild conditions under which the outputs of our proposed algorithms are provably correct. We also derive upper bounds on the computational complexity and query complexity of each algorithm. We demonstrate the versatility of our proposed algorithms by combining them to improve the template mining accuracy of five representative algorithms over sixteen widely used benchmark datasets.

翻译：模板挖掘是支持日志分析的基础任务之一，它有助于大规模Web应用的诊断与故障排查。本文开发了一种人在回路的模板挖掘框架，以支持交互式日志分析——这在Web应用的实际诊断或故障排查中非常必要，但以往的模板挖掘算法无法满足这一需求。我们定义了三种轻量级用户反馈类型，并基于这些反馈设计了三种原子级的、人在回路的模板挖掘算法。我们推导出了在所提算法输出可证明正确时的温和条件，同时还推导出了每种算法的计算复杂度和查询复杂度的上界。我们通过将所提算法进行组合，在十六个广泛使用的基准数据集上提高了五种代表性算法的模板挖掘精度，从而展示了所提算法的通用性。