International Journal of Science, Engineering and TechnologyAuthors: Dr. Jonathan Mercer, Emily Richardson, Dr. Nathaniel Brooks, Olivia Bennett, Ethan Clarke, Jeji Krishnan
Abstract: Modern enterprise messaging and distributed application environments generate massive volumes of operational data in the form of thread dumps, mailbox logs, runtime traces, and system diagnostics. Analyzing these heterogeneous data sources manually is time-consuming, error-prone, and often insufficient for identifying hidden operational anomalies, performance bottlenecks, and service degradation patterns. This research proposes an AI-driven operational signature extraction framework that leverages deep neural models to automatically learn, classify, and interpret operational behaviors from thread dumps and messaging system logs. The proposed approach integrates log parsing, feature engineering, sequence modeling, and anomaly detection techniques to identify recurring runtime signatures associated with deadlocks, thread contention, latency spikes, mailbox congestion, and system instability. By applying deep learning architectures such as recurrent neural networks and transformer-based models, the framework enables intelligent correlation of runtime events across distributed systems and improves diagnostic accuracy in complex operational environments. Experimental evaluation demonstrates that the proposed model significantly enhances anomaly detection efficiency, reduces manual troubleshooting effort, and accelerates root cause identification compared to traditional rule-based monitoring approaches. The study highlights the potential of AI-powered operational analytics in strengthening enterprise observability, predictive maintenance, and automated support engineering for large-scale messaging infrastructures.
DOI: http://doi.org/10.5281/zenodo.20265301
