Rule Scheduling in Active Database using Learning Automata

Active database systems (ADBS) can react to the occurrence of predefined events automatically by definition a collection of active rules. One of the most important modules of ADBS is the rule scheduler, which has considerable impact on performance and efficiency of ADBS. The job of rule scheduler is the selection of a rule for execution from the set of ready for execution rules. In this paper, we propose a new approach based on learning automata to improve the rule scheduling performance in terms of average response time, response time variance, and throughput. Learning automata have been used to obtain better estimations for rule execution probabilities. The results of experimentations show that the performance of the proposed method outperforms the most effective existing rule scheduling method.