A generic framework for deriving and processing uncertain events in rule-based systems

In recent years, there has been an increased need for the use of rule-based systems, systems required to act automatically based on events, or changes in the environment. Some events may be generated externally while others must be inferred by the system based on the other events. This event inference is inherently uncertain which is due to uncertain information sources and uncertain event occurrence. To design a solution provides us with the challenges of scalability of incoming events and inaccuracy of associated probabilities. Thus we provide a generic framework enabling such uncertain event inference in rule-based systems based on Probabilistic event model for events information, Monte Carlo sampling algorithm for approximation of probability of the events which uses selectability mechanism for improving the efficiency and performance of the overall system. The overall system works based on the prediction-correction paradigm. We also propose to use the Prioritization algorithm to prioritize client requests based on their needs and provide them with the best service.