LogQA: Question Answering in Unstructured Logs

03/21/2023
by   Shaohan Huang, et al.
0

Modern systems produce a large volume of logs to record run-time status and events. System operators use these raw logs to track a system in order to obtain some useful information to diagnose system anomalies. One of the most important problems in this area is to help operators find the answers to log-based questions efficiently and user-friendly. In this work, we propose LogQA, which aims at answering log-based questions in the form of natural language based on large-scale unstructured log corpora. Our system presents the answer to a question directly instead of returning a list of relevant snippets, thus offering better user-friendliness and efficiency. LogQA represents the first approach to solve question answering in lod domain. LogQA has two key components: Log Retriever and Log Reader. Log Retriever aims at retrieving relevant logs w.r.t. a given question, while Log Reader is responsible for inferring the final answer. Given the lack of a public dataset for log questing answering, we manually labelled a QA dataset of three open-source log corpus and will make them publicly available. We evaluated our proposed model on these datasets by comparing its performance with 6 other baseline methods. Our experimental results demonstrate that LogQA has outperformed other baseline methods.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset