德州大学达拉斯分校伍伟丽教授学术报告

2016年06月21日 11:06  点击:[]

题目:Small Sensor and Big Data


时间:2016年6月22日(周三)10:00-11:30


地点:综合楼5楼大会议室


摘要:This talk focuses research issues in data engineering, especially in data collection, aggregation, and analysis. For data collection and aggregation, a sequence of research results is included on coverage and connected coverage problems in sensor systems. For data analysis, a research work is presented on the shortest path in big data networks.
 
Sensors have applications in many areas, such as embedded systems in electrical engineering, information systems in industrial engineering and management science, sensor networks in computer science, and military command and control systems in military science. In every system involving sensors, sensor coverage is an important research issue as it directly influences the quality of service and efficiency of a sensor system.
 
The minimum connected sensor cover problem involves two issues, coverage and connectivity. It is also a long-standing open problem whether there exists or not a polynomial-time constant-approximation solution. So far this is still open. However, we made a significant progress by constructing two approximation solutions. One approximation solution has a performance ratio depending on the number of sensors, but independent of the link radius. The second approximation solution has a performance ratio depending on the link radius, but independent of the number of sensors. The existence of these two approximation solutions means that either the link radius has a close relationship with the number of sensors or an efficient constant-approximation solution exists. Since the former unlikely holds, the latter is possibly true. Thus, it suggests that this open problem is most likely to have a positive answer. This work is based on a new finding of a relationship between the minimum connected sensor cover problem and the group Steiner tree problem.


This talk contains a new approach for community detection in social network, which is one of the trendy and fundamental research problems in Big Data. In this work, the community detection problem was investigated based on the novel concept of terminal set. A terminal se is a group of users within which any two users belong to different communities. Although the community detection is hard in general, the terminal set can be very helpful in designing effective community detection algorithms. We first present a 2-approximation algorithm running in polynomial time for the original community detection problem. In the other issue, in order to better support real applications we further consider the case when extra restrictions are imposed on feasible partitions. For such customized community detection problems, we provide two randomized algorithms which are able to find the optimal partition with a high probability. Demonstrated by the experiments performed on benchmark networks the proposed algorithms are able to produce high-quality communities.


报告人简介:
Dr. Weili Wu is a full professor in Department of Computer Science, University of Texas at Dallas. She received her Ph. D. in 2002 and M.S. in 1998 from Department of Computer Science, University of Minnesota, Twin City. She’s a L.N Technical University  alumnus. She received her bachelor in 1998 from Department of Mechanical Engineering, Liao Ning Technical University. Her research mainly deals with the general research area of data communication and data management. Her research focuses on the design and analysis of algorithms for optimization problems that occur in wireless networking environments and various database systems including Big Data, Social Networks. She has published more than 200 research papers in various prestigious journals and conferences such as IEEE Transaction on Knowledge and Data Engineering (TKDE), IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Multimedia (TMM), ACM Transactions on Sensor Networks (TOSN), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE/ACM Transactions on Networking (TON), Journal of Global Optimization (JGO), Journal of Optical Communications and Networking (JOCN), Optimization Letters (OPTL), IEEE Communications Letters (ICL), Journal of Parallel and Distributed Computing (JPDC), Journal of Computational Biology (JCB), Discrete Mathematics (DM), Social Network Analysis and Mining (SNAM), Discrete Applied Mathematics (DAM), IEEE INFOCOM (The Conference on Computer Communications), ACM SIGKDD (International Conference on Knowledge Discovery & Data Mining), International Conference on Distributed Computing Systems (ICDCS), International Conference on Database and Expert Systems Applications (DEXA), SIAM Conference on Data Mining, etc. Dr Wu is associate editors of Discrete Mathematics, Algorithms and Applications, World Scientifics (DMAA), SOP Transactions on Wireless Communications (STOWC), Computational Social Networks, Springer, and International Journal of Bioinformatics Research and Applications (IJBRA). Dr. Wu is a senior member of IEEE.

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