Authors: Dr. Andrew Collins, Dr. Melissa Grant, Rahul Verma, Dr. Kevin Mitchell, Sophia Nguyen, Jeji Krishnan
Abstract: Enterprise email and collaboration platforms such as Zimbra generate large volumes of system logs that capture critical information about server operations, user activities, and fault conditions. However, manual analysis of these logs is time-consuming, error-prone, and often insufficient for identifying complex failure patterns in distributed environments. This paper presents an AI-assisted log analysis framework designed to enhance diagnostics for Zimbra-based enterprise systems, with a specific focus on intelligent processing of zmdiaglog outputs. The proposed approach leverages machine learning techniques, including pattern recognition, anomaly detection, and natural language processing, to automatically interpret log data and identify underlying issues. By incorporating domain-specific knowledge of Zimbra architecture and log semantics, the system maps raw log entries to meaningful diagnostic insights, enabling faster root cause analysis and improved system observability. The framework also integrates automated classification of errors, correlation of multi-source logs, and predictive analytics to detect potential failures before they impact system performance. Experimental evaluation demonstrates significant improvements in diagnostic accuracy, reduction in analysis time, and enhanced operational efficiency compared to traditional rule-based methods. The results highlight the effectiveness of AI-driven log intelligence in improving reliability, maintainability, and scalability of enterprise email and collaboration platforms. This research contributes a practical and scalable solution for modern system diagnostics and provides a foundation for future advancements in AI-powered observability and automated troubleshooting.
DOI: http://doi.org/10.5281/zenodo.20157163
