Intelligent Solutions for Retroactive Anomaly Detection and Resolution with Log File Systems

Authors

Derek G. Rogers, Southern Methodist UniversityFollow
Chanvo Nguyen, Southern Methodist UniversityFollow
Abhay Sharma, Southern Methodist UniversityFollow

Abstract

This paper explores the intricate challenges log files pose from data science and machine learning perspectives. Drawing inspiration from existing methods, LAnoBERT, PULL, LLMs, and the breadth of recent research, this paper aims to push the boundaries of machine learning for log file systems. Our study comprehensively examines the unique challenges presented in our problem setup, delineates the limitations of existing methods, and introduces innovative solutions. These contributions are organized to offer valuable insights, predictions, and actionable recommendations tailored for Microsoft's engineers working on log data analysis.

Recommended Citation

Rogers, Derek G.; Nguyen, Chanvo; and Sharma, Abhay () "Intelligent Solutions for Retroactive Anomaly Detection and Resolution with Log File Systems," SMU Data Science Review: Vol. 8: No. 1, Article 10.
Available at: https://scholar.smu.edu/datasciencereview/vol8/iss1/10

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License