新型高性能计算资源模型和任务模型

近期，实验室复杂系统与高性能计算团队在移动大数据处理与分析、地表环境数据以及对地观测数据处理的高性能并行处理方面取得重要成果，针对数据密集型计算特征，研究了并行计算资源模型和任务模型的研究；将时间序列自适应分解算法（如经验模态分解算法）与数据挖掘算法结合，从空天一体的数据中提取知识，应用于生产实际。相关成果已经发表了多篇SCI检索论文。

（1）Yunliang Chen, Lizhe Wang, Fangyuan Li, Bo Du, Kim-Kwang Raymond Choo, Houcine Hassan, Wenjian Qin:Air quality data clustering using EPLS method. Information Fusion 36: 225-232 (2017) （T1）

（2）Yunliang Chen, Xiaodao Chen, Wangyang Liu, Yuchen Zhou, Albert. ZomayaShiyan Hu, Stochastic scheduling for variation-aware virtual machine placement in a cloud computing CPS, Future Generation Computer Systems, DOI: 10.1016/j.future.2017.09.024 (2017) (T2)

（3）Ze Deng, Wei Han, Lizhe Wang, Rajiv Ranjan, Albert Y. Zomaya, Wei Jie: An efficient online direction-preserving compression approach for trajectory streaming data. Future Generation Comp. Syst. 68: 150-162 (2017)（T2）

（4）Yunliang Chen, Fangyuan Li, Jia Chen, Bo Du, Kim-Kwang Raymond Choo, Houcine Hassan:EPLS: A novel feature extraction method for migration data clustering. J. Parallel Distrib. Comput. 103: 96-103 (2017) （CCF-B）

（5）Yunliang Chen, Fangyuan Li, Ze Deng, Xiaodao Chen, Jijun He: PM2.5 forecasting with hybrid LSE model-based approach. Softw., Pract. Exper. 47(3): 379-390 (2017) (CCF-B)

（6）Xiaodao Chen, Xiaohui Huang, Yang Xiang, Dongmei Zhang, Rajiv Ranjan, Chen Liao:A CPS framework based perturbation constrained buffer planning approach in VLSI design. J. Parallel Distrib. Comput. 103: 3-10 (2017) （CCF-B）