Reinforcement learning-based prediction approach for distributed Dynamic Data-Driven Application Systems

With the current advances in cloud and distributed system technology, data have become ubiquitous and their dynamics has increased. It is an extreme challenge to find the interdependencies among distributed data in order to dynamically manage and predict the trend within large amounts of data sources. This paper proposes a new distributed dynamic data-driven model and strategy to direct and evaluate the interlinked data sets in Dynamic Data-Driven Application Systems (DDDAS). The underlying technique involves the introduction of a reinforcement Q-Learning approach which includes search strategies to determine how to drill and drive a series of highly dependent data in order to enhance prediction accuracy and efficiency. It can tackle dynamic data issues in a real-time, dynamic and resource-bounded environment. The proposed framework is a comprehensive skeleton for modeling complex, flexible and dynamic tasks in a distributed environment for solving DDDAS problems. In simulation, the new model utilizes individual sensors, distributed databases and predictors in Dynamic Data Stream Nodes with multiple dimensional variables which can be instantiated to explore the search space, thereby improving the search convergence. This study shows the effectiveness and applicability of using the technique in the analysis of typhoon rainfall data. The result shows that the proposed approach performed better than traditional linear regression approaches, reducing the error rate by 36.34 %.