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. Rule scheduler selects a rule to execute (evaluate) its action (condition) section in each time through the rules, which are ready for execution (evaluation). We have already evaluated and compared the existing rule scheduling approaches in a laboratorial environment based on three-tier architecture. Five evaluation criteria were recognized and defined formally for evaluation and comparison of rule scheduling approaches including: Average Response Time, Response Time Variance, Throughput, Time Overhead per Transaction and CPU Utilization. At last, we introduced the most effective approach. In this paper, we first, design and implement the before mentioned laboratorial environment again to over and simulate the behavior of ADBS more exactly and completely, then propose a new approach to improve the rule scheduling process based on improvement of triggered rule scheduling using learning automaton. Then, we compare it with the most effective existing approach in the mentioned framework. Results of experiments show that the new method improves the rule scheduling.