Active systems are crucial for handling dynamic events in various domains, including business processes. The first work introduces an intelligent method using integer encoding for log pre-processing, Bat Optimization for feature selection, and Deep Convolutional Neural Networks for abnormal event detection, though CNNs lack spatial consistency. To address this, the second work implements Eclat-based Association Rule Mining (EARM) for detecting and prioritizing abnormal events, but it generates excessive candidate sets and requires extensive database scanning. The third work enhances incident prediction in aerospace systems by integrating Animal Migration Optimization (AMO) with Association Rule Mining (ARM), where Apriori generates association rules, and AMO refines them by eliminating low-utility rules. One-hot encoding is applied for numeric conversion, ensuring efficient event derivation. This structured approach optimizes computational efficiency while improving event detection accuracy and prioritization.