Semantic-Aware Log Understanding and Analysis

The exponential growth in system complexity and the corresponding surge in log data volume necessitate advanced log analysis techniques for efficient system management and anomaly detection. Traditional log understanding and analysis methods often fail to capture the rich semantic context inherent in log messages, leading to suboptimal monitoring and diagnostic capabilities. This paper aims to bridge the semantic gap by integrating cutting-edge semantic technologies into the log analysis pipeline. We leverage natural language processing, information retrieval, and large language models to enrich log data with semantic information, facilitating a deeper understanding of log messages. Our methodology enhances anomaly detection accuracy by utilizing hierarchical contextual information and pre-training technology, and refining log-based QA processes by log retrieval and log reader. Preliminary results demonstrate a significant improvement in identifying and diagnosing system anomalies, as well as in the automated answering log questions. This research not only presents a breakthrough in log data analysis but also sets the stage for future advancements in intelligent system monitoring and proactive fault resolution. Through this semantic-aware approach, we envision a new paradigm in log analysis that transcends traditional machine learning methods, offering a more robust and intuitive understanding of system behaviors and states.