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Today's competitive business climate and the complexity of IT environments dictate efficient and cost effective service delivery and support of IT services. This is largely achieved through automating of routine maintenance procedures including problem detection, determination and resolution. System monitoring provides effective and reliable means for problem detection. Coupled with automated ticket creation, it ensures that a degradation of the vital signs, defined by acceptable thresholds or monitoring conditions, is flagged as a problem candidate and sent to supporting personnel as an incident ticket. This paper describes a novel methodology and a system for minimizing non-actionable tickets while preserving all tickets which require corrective action. Our proposed method defines monitoring conditions and the optimal corresponding delay times based on an off-line analysis of historical alerts and the matching incident tickets. Potential monitoring conditions are built on a set of predictive rules which are automatically generated by a rule-based learning algorithm with coverage, confidence and rule complexity criteria. These conditions and delay times are propagated as configurations into run-time monitoring systems.