Problem
Research questions and friction points this paper is trying to address.
log cleaning
downstream tasks
irrelevant messages
redundant logs
log analysis
Cleaning Logs for Downstream Tasks (Registered Report)
🤖 AI Summary
This work addresses the pervasive issue of redundant and isolated messages in system logs, which hinder downstream tasks such as model reasoning and anomaly detection. To tackle this challenge, the authors propose LogPurifier—the first task-agnostic log cleansing framework—that systematically purifies logs by extracting log templates and modeling their dependencies to accurately identify and remove messages irrelevant to system functional behavior. By doing so, LogPurifier enables effective log sanitization applicable across diverse analytical scenarios. Experimental results demonstrate that LogPurifier substantially improves both accuracy and efficiency in various downstream tasks, thereby validating its effectiveness and generalizability.
📝 Abstract
Background: Software systems generate logs during execution to record critical events and runtime information for troubleshooting and monitoring. However, in practice, logs often contain significant amounts of redundant and irrelevant information, which can negatively impact the performance of downstream analysis tasks, such as model inference and anomaly detection. Objective: The objective of this study is to clean log data by identifying and removing free-standing messages -- messages that are not relevant to the execution behaviors of interest and are interleaved with messages capturing the system's functional behavior. Method: To address this objective, we propose LogPurifier, a task-agnostic log-cleaning approach based on dependency relationships between log message templates. The paper presents a plan for an empirical evaluation using a controlled experimental design to assess the impact of LogPurifier on the effectiveness and efficiency of two downstream tasks: model inference and anomaly detection.
Innovation
Methods, ideas, or system contributions that make the work stand out.
log cleaning
dependency relationships
free-standing messages
task-agnostic
log message templates
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