Breaking Text Limits: Exploring Frontier Technologies and Challenges in Parsing Massive Log Files with GPT

Large-scale log files are vital for capturing critical information about system operations, network interactions, and user activities, making them essential in areas such as enterprise management, research, and cybersecurity. However, as data volume grows exponentially, traditional text processing methods struggle to efficiently parse these extensive logs, hindering accurate information extraction and analysis. This study focuses on leveraging existing natural language processing techniques, including regular expressions, keyword extraction, and text clustering, to tackle the challenges of parsing large log files. We examine the specific issues posed by such data and introduce a comprehensive approach that incorporates log preprocessing with regular expressions, followed by key information extraction via advanced algorithms, and segmentation through text clustering methods. Our experimental results demonstrate an accuracy rate of 85%, highlighting the effectiveness and potential of our proposed solution for addressing the increasing complexity of log file analysis.