| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 103 === The traditional data analysis and prediction method assumes that data distribution is stable. Therefore, it can predict unlabeled data precisely by analyzing the historical data. However, in today’s big-data environment, which is changing frequently, the traditional approach can no longer be effective; it cannot handle concept drift in a Dynamic Data Driven Application System (DDDAS). This thesis proposes a parallel detection and prediction method for concept drift in DDDAS. The proposed method can detect changing data and then feedback to the prediction model for better subsequent predictions. Furthermore, this method computes a global prediction by aggregating local predictions. Therefore, prediction accuracy is increased and computation time is decreased. In simulation, Map-Reduce is used for parallel processing. Two cases are tested. Results show that prediction accuracy is raised by 14% and 35% for these two cases, respectively. The execution time is improved by almost 45% and 29%, respectively.
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