Name
Abstract | ||
|---|---|---|
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%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2480362.2480511 | SAC |
Keywords | Field | DocType |
typhoon rainfall data,dependent data,search strategy,dynamic data stream nodes,dynamic data,search convergence,new model,dynamic data driven application,interlinked data set,search space,reinforcement learning,q learning | Convergence (routing),Data mining,Data set,Computer science,Word error rate,Q-learning,Dynamic data,Artificial intelligence,Distributed database,Machine learning,Reinforcement learning,Linear regression | Conference |
Citations | PageRank | References |
2 | 0.37 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Szu-Yin Lin | 1 | 88 | 12.24 |
| Kuo-Ming Chao | 2 | 1123 | 130.82 |
| Chi-Chun Lo | 3 | 593 | 54.99 |
| Nick Godwin | 4 | 110 | 12.52 |