A Methodology for Proactively Diagnosing Production Oracle DBMSs Based on Correlation Analysis of Trace Files and Alert.Log to Predict Failures and Prevent Unplanned Downtime

The study describes the methodological features of proactive diagnostics for high-load Oracle DBMS, based on correlational analysis of the contents of the alert.log journal and trace files generated by background processes. The significance of the approach is driven by the substantial increase in the cost of unplanned downtime in 2024–2025: in the financial sector, losses reach approximately 5 million US dollars per hour of unavailability. As a conceptual foundation, an algorithmic method is proposed for identifying latent relationships between background process activity and subsequent critical failures, aimed at detecting non-obvious causal–temporal dependencies that remain outside the scope of traditional monitoring. The proposed methodology is based on correlation analysis of alert.log and trace files, supplemented by temporal modeling of events and predictive analytics methods. It enables detection of performance degradation, resource leaks, system anomalies, and logical errors before an incident occurs, thereby significantly reducing the likelihood of unplanned downtime. The methodological framework integrates latent semantic indexing tools for processing unstructured textual corpora and statistical modeling based on the Jensen–Shannon divergence to quantitatively assess changes in event distributions and for forecasting failure probability. Such a synthesis enables a transition from simple anomaly fixation to a probabilistic interpretation of system-state degradation based on weak signals contained in diagnostic traces. Particular attention is given to describing interactions between redo and ARCH processes, the operational specifics of standby mechanisms, as well as the role of RMAN as a means of preventing data loss through proper organization of backup and recovery. The methodology is formalized as a cyclic diagnostic loop: Log Collection ? Normalization ? Correlation ? Anomaly Detection ? Classification ? Prediction ? Prevention/Remediation. The formulated results establish a theoretical basis and applied guidelines for evolving from reactive monitoring to autonomous management of data infrastructure, reducing the dependence of the availability of an organization’s critical services on the human factor and operational errors.