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学术讲座通知:Skyline for Similarity Search: New Definitions, Data Structures, and Algorithms

发布时间:2017-05-12 09:33  浏览:1019次

应网络与交换技术国家重点实验室程祥副教授的邀请,美国埃默里大学Li Xiong教授将于5月19日来北京邮电大学作学术报告。欢迎校内广大师生踊跃参加。

讲座题目:Skyline for Similarity Search: New Definitions, Data Structures, and Algorithms

主讲人:Li Xiong(熊莉)教授

主持人:程祥

时间:2017519日(星期10:15—11:45

地点:新科研楼610会议室

Abstract

Skyline queries and k nearest neighbor (kNN) queries are important for many applications involving multi-criteria similarity search or decision making. The skyline of a set of multi-dimensional data objects consists of the Pareto optimal objects (all possible nearest neighbors) that are not worse (not further to the query object) than any other in all attributes. Although skyline queries have been extensively studied, the big data era presents new challenges due to the volume, velocity, variety, varacity of the data as well as the need for harnessing the value and ensuring the privacy and security of the data.  In this talk, I will present a skyline computation framework with new definitions and algorithms addressing these challenges focusing on: 1) a new skyline definition called group-based skyline that extends traditional skyline definition from Pareto optimal objects (all possible nearest neighbors) to Pareto optimal groups (all possible k nearest neighbors), 2) a novel data structure called Skyline Diagram, a counterpart of Voronoi diagram for kNN queries, to facilitate efficient computation of dynamic skyline queries,  and 3) a secure skyline protocol for outsourced computation on encrypted data as well as a parallel implementation that are scalable and guarantee the confidentiality of the data.  I will conclude with a discussion of open problems.

Bio

Li Xiong is a Professor of Computer Science (and Biomedical Informatics) and holds a Winship Distinguished Research Professorship at Emory University. She has a PhD from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from University of Science and Technology of China, all in Computer Science. She and her research group, Assured Information Management and Sharing (AIMS), conduct research that addresses both fundamental and applied questions at the interface of data privacy and security, spatiotemporal data management, and health informatics. Li has published over 100 papers in premier journals and conferences including TKDE, VLDB, ICDE, CCS, and WWW, and has received four best paper awards.  She currently serves as associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE) and  numerous program committees for data management and data security conferences.  She is a recipient of a Google Research Award, IBM Faculty Innovation Award, Cisco Research Award, and Woodrow Wilson Fellowship.  Her research is supported by NSF (National Science Foundation), NIH (National Institute of Health), AFOSR (Air Force Office of Scientific Research), and PCORI (Patient-Centered Outcomes Research Institute).

网络与交换技术国家重点实验室
2017年5月12日