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Date
Publisher
Springer Nature
Abstract
This paper presents RL4CEP, a reinforcement learning (RL) approach to dynamically update complex event processing (CEP)
rules. RL4CEP uses Double Deep Q-Networks to update the threshold values used by CEP rules. It is implemented using
Apache Flink as a CEP engine and Apache Kafka for message distribution. RL4CEP is a generic approach for scenarios
in which CEP rules need to be updated dynamically. In this paper, we use RL4CEP in a financial trading use case. Our
experimental results based on three financial trading rules and eight financial datasets demonstrate the merits of RL4CEP in
improving the overall profit, when compared to baseline and state-of-the-art approaches, with a reasonable consumption of
resources, i.e., RAM and CPU. Finally, our experiments indicate that RL4CEP is executed quite fast compared to traditional
CEP engines processing static rules.
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License
Except where otherwised noted, this item's license is described as Attribution 4.0 International