Determination of Event Patterns for Complex Event Processing Using Fuzzy Unordered Rule Induction Algorithm with Multi-objective Evolutionary Feature Subset Selection
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Complex Event Processing (CEP) is an emerging technology to process streaming data and to generate response actions in real time. CEP systems treat all sensor data as primitive events and attempt to detect semantically high level events and related actions by matching them using event patterns. These event patterns are the rules which combine primitive events according to temporal, logical, or spatial correlations among them. Although event patterns (decision rules) can be provided by experts in simplistic scenarios, the huge amount of sensor data makes this unfeasible. The main purpose of the underlying paper is replacing manual identification of event patterns. Considering the uncertainty related to the sensor data, Fuzzy Unordered Rule Induction Algorithm (FURIA) was implemented to identify event patterns after selecting the relevant feature subset using Elitist Pareto-based Multi-Objective Evolutionary Algorithm for Diversity Reinforcement (ENORA). The results were compared to the alternative machine learning approaches.