Automatic learning of predictive rules for complex event processing: doctoral symposium

The inference mechanisms of CEP engines are completely guided by rules, which are specified manually by domain experts. We argue that this user-based rule specification is a limiting factor, as it requires the experts to have technical knowledge about the CEP language they want to use, it restricts the usage of CEP to merely the detection of straightforward situations, and it restrains its propagation to more advanced fields that require prediction and proactivity. Therefore, we introduce autoCEP as a data mining-based approach that automatically learns predictive CEP rules from historical traces. More precisely, we include our novel method that is capable of learning rules and handling events coming from one source, and then we elaborate our vision on how to extend autoCEP to deal with simultaneous events coming from multiple sources.