# Interactive Log Parsing via Light-weight User Feedback

arXiv: https://arxiv.org/abs/2301.12225 · HTML: https://mlsanity.com/paper/2301.12225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 28 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Tools and Benchmarks for Automated Log Parsing](https://mlsanity.com/paper/1811.03509/) (2018)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [UniParser: A Unified Log Parser for Heterogeneous Log Data](https://mlsanity.com/paper/2202.06569/) (2022)
- [Self-Supervised Log Parsing](https://mlsanity.com/paper/2003.07905/) (2020)
- [SemParser: A Semantic Parser for Log Analysis](https://mlsanity.com/paper/2112.12636/) (2021)
- [UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks](https://mlsanity.com/paper/2112.03159/) (2021)
- [Logram: Efficient Log Parsing Using n-Gram Dictionaries](https://mlsanity.com/paper/2001.03038/) (2020)
- [LogStamp: Automatic Online Log Parsing Based on Sequence Labelling](https://mlsanity.com/paper/2208.10282/) (2022)
- [FlexParser -- the adaptive log file parser for continuous results in a changing world](https://mlsanity.com/paper/2106.03170/) (2021)
- [AWSOM-LP: An Effective Log Parsing Technique Using Pattern Recognition and Frequency Analysis](https://mlsanity.com/paper/2110.15473/) (2021)
- [vue4logs -- Automatic Structuring of Heterogeneous Computer System Logs](https://mlsanity.com/paper/2202.07504/) (2022)

## Abstract (arXiv)

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.