INTEGRATED METHODOLOGY FOR ENHANCING WEB APPLICATION MONITORING: Predictive Analytics for Error Reduction and Accelerated Diagnostics

The monograph introduces and substantiates an Integrated Predictive Analytics Methodology (IPAM) designed to enhance the efficiency of web-application monitoring by anticipating failures, reducing error rates, and accelerating diagnostic processes. The study’s relevance stems from the increasing complexity of modern web systems and the heightened demands for their reliability and continuous availability. It examines prevailing monitoring practices and reveals their main constraints — chiefly the reactive character of observability tools and the data overload produced by excessive signal noise. IPAM marries the collection of heterogeneous telemetry (log files, metrics, and traces) with machinelearning algorithms for prognostic anomaly detection, focusing on proactive problem identification and automated diagnostics that are expected to cut downtime and boost DevOps productivity. The monograph follows a scholarly style, providing a current literature review, a detailed description of IPAM, an account of the predictive module’s implementation, and a comparative analysis against existing solutions. The results are theoretical and methodological: they emphasize the conceptual novelty of the approach, while conclusions rest on an analytical synthesis of the literature. Overall, the work contributes to web-application performance management by offering a proactive monitoring strategy that strengthens reliability and shortens incident-response times. View Less