亚盈体育电竞(中国)有限公司官网

吴全旺

时间：2017-06-28 浏览量：次

1基本信息

姓名：吴全旺

性别：男

职称：副研究员(博导，硕导)

办公地点：主教1605

E-mail：wqw@cqu.edu.cn

研究方向：云计算，计算智能，数据挖掘

招生信息：年度招收博士1名，硕士3名。

2个人简介：

博士，副研究员，博士生导师，硕士生导师；中国计算机学会（CCF）会员、IEEE会员。从2003年到2013年于重庆大学依次获得学士、硕士、博士学位；2014年到2015年于日本National Institute of Informatics担任Special Researcher；2016年起于亚盈体育电竞(中国)有限公司官网任职。主要研究方向包括云计算、计算智能、数据挖掘，在IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Services Computing等SCI期刊和国际会议上发表学术论文30余篇。主持国家重大研发计划子课题、国家自然科学基金青年项目、中国博士后科学基金面上项目和特别资助、重庆市博士后科研项目特别资助、中央高校基本科研业务专项等科研项目。

3学术成果

主要获奖

[1]2017ACM中国学术新星重庆分会奖

[2]2018吴文俊人工智能科技进步一等奖(证书编号：KJJB-2018-1-C-R12)：智慧金融中的集成生物识别关键技术及应用

[3]2017重庆市自然科学奖三等奖(证书编号：2017-Z-3-08-R04)：高维数据挖掘算法及可信服务计算预测模型研究

主要期刊论文发表

[1]Q. Wu, M. Zhou, Q. Zhu, Y. Xia, J. Wen. MOELS: Multiobjective Evolutionary List Scheduling for Cloud Workflows,IEEE Transactions on Automation Science and Engineering, 2019, in press, DOI: 10.1109/TASE.2019.2918691(JCR 1区, CCF-B).

[2]D. Cheng, Q. Zhu, J. Huang,Q. Wu, L. Yang. Clustering with local density peaks-based minimum spanning tree,IEEE Transactions on Knowledge and Data Engineering, 2019, in press, DOI: 10.1109/TKDE.2019.2930056 (JCR 1区, CCF-A)

[3]Q. Wu, F. Ishikawa, Q. Zhu, Y. Xia. Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters,IEEE Transactions on Services Computing, 2019, 12(4):550 - 563(JCR 1区, CCF-B).

[4]D. Cheng, Q. Zhu, J. Huang,Q. Wu, L. Yang. A novel cluster validity index based on local cores,IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(4):985 - 999 (JCR 1区, CCF-B)

[5]L. Yang, Q. Zhu, J. Huang, D. Cheng,Q. Wu, X. Hong. Constraint nearest neighbor for instance reduction,Soft Computing, 2019, 23: 13235–13245(JCR 1区, CCF-C).

[6]J. Li, Q. Zhu,Q. Wu. A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor,Knowledge-Based Systems, 2019, 184: 104895(JCR 1区, CCF-C).

[7]D. Cheng, Q. Zhu, J. Huang,Q. Wu, L. Yang. A local cores-based hierarchical clustering algorithm for data sets with complex structures,Neural Computing and Applications, 2019, 31(11): 8051-8068 (JCR 1区)

[8]D. Cheng, Q. Zhu, J. Huang,Q. Wu, L. Yang. A hierarchical clustering algorithm based on noise removal,International Journal of Machine Learning and Cybernetics, 2019, 10(7): 1591–1602 (JCR 2区)

[9]Y. Wang, J. Wen,Q. Wu, L. Guo, B. Tao. A dynamic cloud service selection model based on trust and SLA in cloud computing,International Journal of Grid and Utility Computing, 2019, 10(4): 334-343.

[10]Q. Wu, M. Zhou, Q. Zhu, Y. Xia. VCG Auction-Based Dynamic Pricing for Multigranularity Service Composition,IEEE Transactions on Automation Science and Engineering, 2018, 15(2): 796-805(JCR 1区, CCF-B).

[11]J. Lu, Q. Zhu,Q. Wu. A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain.Engineering Applications of Artificial Intelligence, 2018, 72: 213-227(JCR 1区, CCF-C).

[12]L. Yang, Q. Zhu, J. Huang, D. Cheng,Q. Wu, X. Hong. Natural neighborhood graph-based instance reduction algorithm without parameters,Applied Soft Computing, 2018, 70: 279-287 (JCR 1区)

[13]Q. Wu, F. Ishikawa, Q. Zhu, Y. Xia, J. Wen. Deadline-constrained Cost Optimization for Workflow Applications in Clouds,IEEE Transactions on Parallel and Distributed Systems, 2017, 28(12): 3401–3412 (JCR 1区, CCF-A).

[14]J. Huang, Q. Zhu, L. Yang, D. Cheng,Q. Wu. QCC: a novel clustering algorithm based on Quasi-Cluster Centers,Machine Learning, 2017, 106(3): 337–357(JCR 2区, CCF-B).

[15]D. Cheng, Q. Zhu, J. Huang, L. Yang,Q. Wu. Natural Neighbor-based Clustering Algorithm with Local Representatives,Knowledge-Based Systems, 2017, 123:238-253(JCR 1区, CCF-C).

[16]J. Huang, Q. Zhu, L. Yang, D. Cheng,Q. Wu. A Novel Outlier Cluster Detection Algorithm without Top-n Parameter,Knowledge-Based Systems, 2017, 121: 32–40(JCR 1区, CCF-C).

[17]W. Zheng, Y. Wang, Y. Xia,Q. Wu, et al. On dynamic performance estimation of fault-prone Infrastructure-as-a-Service clouds.International Journal of Distributed Sensor Networks, 2017, 13(7).

[18]W. Li, Y. Wang, Y. Wang, Y. Xia, X. Luo,Q. Wu. An Energy-Aware and Under-SLA-Constraints VM Consolidation Strategy Based on the Optimal Matching Method.International Journal of Web Services Research, 2017, 14(4): 75-89.

[19]Q. Wu, F. Ishikawa, Q. Zhu, D. Shin. QoS-aware Multi-granularity Service Composition: Modeling and Optimization,IEEE Transactions on Systems Man Cybernetics-Systems, 2016, 46(11): 1565-1577(JCR 2区).

[20]Q.Wu,Q. Zhu, P. Li. A Neural Network based Reputation Bootstrapping Approach for Service Selection,Enterprise Information Systems, 2015, 9(7): 768-784(JCR 1区).

[21]Q.Wu,Q. Zhu, X. Jian, F. Ishikawa. SLA-aware composite cloud service provisioning: a broker-based approach,Journal of Systems and Software, 2014, 96: 194–201(JCR 2区, CCF-B).

[22]Q.Wu,Q. Zhu, M. Zhou. A correlation-driven optimal service selection approach for virtual enterprise establishment.Journal of Intelligent Manufacturing, 2014, 25(6): 1441-1453(JCR 2区).

[23]Q.Wu,Q. Zhu, X. Jian. A robust decentralized reputation management system for service selection.Optik, 2014, 125(11): 2692–2701.

[24]Q.Wu,Q. Zhu.Transactional and QoS-aware dynamic service compositionbased onAnt Colony Optimization.Future Generation Computer Systems,2013, 29(5): 1112-1119(JCR 1区, CCF-C).

[25]Q.Wu,Q. Zhu,P.Li.Acaching mechanism for QoS-aware service composition.Journal ofWeb Engineering,2012, 11(2): 119-130(CCF-C).

主要会议论文发表

[1]D. Cheng, Q. Zhu,Q. Wu. A local cores-based hierarchical clustering algorithm for data sets with complex structures,IEEE 42nd Annual Computer Software and Applications Conference(COMPSAC), Tokyo, pp. 410-419, 2018 (CCF-C)

[2]Y. Wang, J. Jiang, Y. Xia,Q. Wu, X. Luo, Q. Zhu. A Multi-stage Dynamic Game-Theoretic Approach for Multi-Workflow Scheduling on Heterogeneous Virtual Machines from Multiple Infrastructure-as-a-Service Clouds,International Conference on Services Computing(SCC), Cham, pp. 137-152, 2018 (CCF-C).

[3]Q.Wu, and F. Ishikawa. Heterogeneous virtual machine consolidation using an improved grouping genetic algorithm,17th IEEE International Conference on High Performance Computing and Communications(HPCC), New York, pp. 397-404, 2015(CCF-C).

[4]Q.Wu, and F. Ishikawa. Towards Service Skyline for Multi-granularity Service Composition,Proceedings of the 2014 International Workshop on Web Intelligence and Smart Sensing(IWWISS), Saint Etienne, 2014.

[5]Q.Wu,Q. Zhu, X. Jian. QoS-aware multi-granularity service composition based on generalized component services,10th International Conference on Service Oriented Computing(ICSOC), Berlin, pp. 446–455, 2013(CCF-B).