LogBabylon: A Unified Framework for Cross-Log File Integration and Analysis

TL;DR

LogBabylon tackles the fragmentation of logs from diverse systems by unifying data through a framework that combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG). It introduces a three-stage pipeline—classification, consolidation, and interpretation—where an efficient prefix parse-tree guides token-level clustering, while RAG supplies external context to the LLM for richer analysis. Key contributions include (i) LLM-powered log template extraction with variable-aware prompting and in-context learning, (ii) end-to-end log consolidation using a vector database, and (iii) human-readable outputs and robust evaluation on Loghub-2k and LogPub datasets, showing superior accuracy and generalization. The approach enables scalable, real-time-like reasoning over heterogeneous log data, supporting proactive incident response, performance optimization, and security assurance. The findings suggest that integrating LLMs with external knowledge bases can significantly enhance cross-log analysis and anomaly detection in dynamic computing environments, with practical impact on operational efficiency and reliability. $y = f(x, z)$ summarizes how LogBabylon combines a new log entry $x$ with retrieved context $z$ to generate interpretable outputs.

Abstract

Logs 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 invaluable tool for continuous monitoring, performance optimization, and security assurance in dynamic computing environments.

Figures (3)

• Figure 1: LogBabylon Architecture
• Figure 2: An example of the data structures in our method
• Figure 3: LLM Semantic Analysis in LogBabylon