Active database systems (ADBS) can react to the occurrence of some predefined events automatically. Reactive behavior of ADBS is organized by a collection of active rules. One of the most important modules of ADBS is the rule manager. The main responsibility of the rule manager is triggering, buffering, firing and selecting (scheduling) rules. Rule scheduling method has considerable impact on performance and efficiency of ADBS. In this paper, we measure evaluation parameters in benchmarking rule scheduling methods in ADBS. We first investigate existing rule scheduling methods and then introduce a framework to compare and evaluate these methods. In this framework, five evaluation criteria have been proposed: Average Response Time, Response Time Variance, Throughput, Time Overhead per Transaction and CPU Utilization. Existing methods have been evaluated by using this framework and the method which has the most positive impact on performance and efficiency of ADBS has been selected by analyzing the weaknesses and strengths of existing method. (This method has been named Ex-SJFPRO-V.1.8). We conclude from results of experiments that using learning techniques in Ex-SJFPRO-V.1.8 improve the performance and efficiency of Active Database Management System.

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