"Logram: 高效日志解析的 n-Gram 方法"

Logram: Efficient Log Parsing Using n-Gram Dictionaries is a research paper that focuses on the challenge of automated log analysis in software systems. Logs serve as valuable sources of runtime information, aiding practitioners in understanding system behaviors and diagnosing failures. However, due to the large size of logs, manual analysis is often impractical, necessitating automated solutions. The paper introduces Logram, a novel approach to log parsing that utilizes n-Gram dictionaries to enhance efficiency. By leveraging n-Grams, Logram is able to effectively convert unstructured log data into a structured format, enabling more streamlined analysis and extraction of valuable insights. This approach not only accelerates the log parsing process but also improves the accuracy and reliability of the results obtained. The significance of Logram lies in its ability to address the inherent challenges of log analysis in software systems. By integrating n-Gram dictionaries, Logram offers a more efficient and accurate solution for parsing logs, ultimately enhancing the operational and maintenance efforts of practitioners. The findings of this research paper provide valuable insights into the potential of n-Gram dictionaries in log parsing, offering a promising avenue for further research and development in this area. In conclusion, Logram: Efficient Log Parsing Using n-Gram Dictionaries presents a cutting-edge approach to automated log analysis, showcasing the importance of leveraging innovative techniques to improve the efficiency and effectiveness of log parsing in software systems. This research paper highlights the potential of n-Gram dictionaries in addressing the challenges of log analysis, paving the way for advancements in this critical aspect of software operation and maintenance.