# LogStamp: Automatic Online Log Parsing Based on Sequence Labelling

arXiv: https://arxiv.org/abs/2208.10282 · HTML: https://mlsanity.com/paper/2208.10282/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2022.

## Built on by (most cited first, 6 of 6)

- [Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies](https://mlsanity.com/paper/2308.07610/) (2023)
- [Log Parsing: How Far Can ChatGPT Go?](https://mlsanity.com/paper/2306.01590/) (2023)
- [LogLM: From Task-based to Instruction-based Automated Log Analysis](https://mlsanity.com/paper/2410.09352/) (2024)
- [Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge](https://mlsanity.com/paper/2412.01377/) (2024)
- [Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis](https://mlsanity.com/paper/2409.13561/) (2024)
- [Interactive Log Parsing via Light-weight User Feedback](https://mlsanity.com/paper/2301.12225/) (2023)

## Abstract (arXiv)

Logs are one of the most critical data for service management. It contains rich runtime information for both services and users. Since size of logs are often enormous in size and have free handwritten constructions, a typical log-based analysis needs to parse logs into structured format first. However, we observe that most existing log parsing methods cannot parse logs online, which is essential for online services. In this paper, we present an automatic online log parsing method, name as LogStamp. We extensively evaluate LogStamp on five public datasets to demonstrate the effectiveness of our proposed method. The experiments show that our proposed method can achieve high accuracy with only a small portion of the training set. For example, it can achieve an average accuracy of 0.956 when using only 10% of the data training.