  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.
