Poster: Towards Federated LLM-Powered CEP Rule Generation and Refinement

In traditional event processing systems, patterns representing situations of interest are typically defined by domain experts or learned from historical data, making rule generation reactive, time-consuming, and susceptible to human error. This paper proposes integrating large language models (LLMs) to automate and accelerate query translation and rule generation into event-based systems. Also, we introduce a federated learning schema to refine the initially generated rules by examining them over distributed event streams, ensuring greater accuracy and adaptability. Preliminary results demonstrate the potential of LLMs as a key component in proactively expediting the autonomous rule-generation process. Moreover, our findings suggest that employing customized prompt engineering techniques can further enhance the quality of the generated rules.