A fuzzy Petri net model for intelligent databases

Knowledge intensive applications require an intelligent environment, which can perform deductions in response to user queries or events that occur inside or outside of the applications. For that, we propose a fuzzy Petri net (FPN) model to represent knowledge and the behavior of an intelligent object-oriented database environment, which integrates fuzzy, active and deductive rules with database objects. The behavior of a system can be unpredictable due to the rules triggering or untriggering each other (non-termination). Intermediate and final database states may also differ according to the order of rule executions (non-confluence). In order to foresee and solve problematic behavior patterns, we employ a static rule analysis on the FPN structure that provides easy checking of the termination property without requiring any extra construct. In addition, with our proposed fuzzy inference algorithm, we guarantee confluent rule executions. The techniques and solutions provided in this study can be used in various complex systems, such as weather forecasting applications, environmental information systems, defense applications.