@inproceedings{lu2025logbabylon,
title={Logbabylon: A unified framework for cross-log file integration and analysis},
author={Lu, Yang and Karanjai, Rabimba and Alsagheer, Dana and Kasichainula, Keshav and Xu, Lei and Shi, Weidong and Huang, Shou-Hsuan Stephen},
booktitle={Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing},
pages={1953--1960},
year={2025}
}Copied to clipboardLogs are critical resources that record events, activities, or messages produced by software applications, operating systems, servers, and network devices. However, consolidating the heterogeneous logs and cross-referencing them is challenging and complicated. Manually analyzing the log data is time-consuming and prone to errors. LogBabylon is a centralized log data consolidating solution that leverages Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) technology. LogBabylon interprets the log data in a human-readable way and adds insight analysis of the system performance and anomaly alerts. It provides a paramount view of the system landscape, enabling proactive management and rapid incident response. LogBabylon consolidates diverse log sources and enhances the extracted information’s accuracy and relevancy. This facilitates a deeper understanding of log data, supporting more effective decision-making and operational efficiency. Furthermore, LogBabylon streamlines the log analysis process, significantly reducing the time and effort required to interpret complex datasets. Its capabilities extend to generating context-aware insights, offering an inva