Interactive Log Parsing via Light-weight User Feedback
This work addresses the need for interactive log parsing to enhance diagnosis and troubleshooting in large-scale Web applications, offering a novel but incremental improvement over existing methods.
The paper tackles the problem of template mining for log analysis in Web applications by introducing a human-in-the-loop framework with light-weight user feedback, resulting in provably correct algorithms that improve accuracy across multiple benchmarks.
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.