用NLP技术分析.NET日志，自动发现异常和重复模式。
NLP-Based .NET CLR Event Logs Analyzer
- 基于BERT改进分词，专用于解析.NET事件日志
- 日志压缩与异常检测准确率高，提升系统稳定性
- 适合软件运维与开发人员快速排查系统问题
本文提出一种基于自然语言处理（NLP）的.NET CLR事件日志分析工具。针对软件系统监控与优化的需求，我们设计了一种基于BERT的架构，并引入定制化的分词方法以适配事件日志特性。该工具基于Python、相关库及SQLite数据库构建，既可用于学术实验，也可解决工业界实际问题。实验表明，该方法在日志序列压缩、重复模式识别和异常检测方面表现优异，模型在异常检测任务中展现出高准确率，验证了NLP技术在提升软件系统可靠性与稳定性方面的潜力。
原文摘要 · Abstract (English)
In this paper, we present a tool for analyzing .NET CLR event logs based on a novel method inspired by Natural Language Processing (NLP) approach. Our research addresses the growing need for effective monitoring and optimization of software systems through detailed event log analysis. We utilize a BERT-based architecture with an enhanced tokenization process customized to event logs. The tool, developed using Python, its libraries, and an SQLite database, allows both conducting experiments for academic purposes and efficiently solving industry-emerging tasks. Our experiments demonstrate the efficacy of our approach in compressing event sequences, detecting recurring patterns, and identifying anomalies. The trained model shows promising results, with a high accuracy rate in anomaly detection, which demonstrates the potential of NLP methods to improve the reliability and stability of software systems.
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