Active database systems (ADBS) can, automatically, react to the occurrence of predefined events 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. Rule scheduler selects a rule to execute in each time through the rules, which are ready for execution. We have already evaluated and compared the existing rule scheduling approaches in a laboratorial environment based on three tier architecture. Five evaluation criteria were, formally, recognized and defined for evaluation and comparison of rule scheduling approaches including: Average Response Time, Response Time Standard Deviation, Throughput, Time Overhead per Transaction and CPU Utilization. At last, we introduced the most effective approach. In this paper, we propose a new approach to improve the rule scheduling based on improvement of Rule Execution Probability Estimation. Then, we compare it with the most effective existing approach in the framework mentioned. Results of experiments show that the new method improves the rule scheduling.

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