Workstation capacity tuning using reinforcement learning
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Introduction to the ICCS 2007 Workshop on Dynamic Data Driven Applications Systems
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Heuristic search based exploration in reinforcement learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Reinforcement learning based resource allocation in business process management
Data & Knowledge Engineering
Proceedings of the 2011 ACM Symposium on Applied Computing
On dynamic data-driven selection of sensor streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
ACM SIGAPP Applied Computing Review
A survey of context data distribution for mobile ubiquitous systems
ACM Computing Surveys (CSUR)
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In this paper, we propose 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 is the introduction of a reinforcement Q-Learning approach including search strategies to determine how to drill and drive a series of highly dependent data in order to increase prediction accuracy and efficiency. 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, so that search convergence can be improved. We show 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 30.48%.