博彩平台

报告题目：I/O-Efficient Triangle Enumeration

报告人：Yufei Tao（陶宇飞）教授（香港中文大学）

时间：12月8日（星期一）14:00

地点：天赐庄校区理工楼321室

报告摘要：This talk will discuss the triangle enumeration problem in the external memory model (a.k.a., the disk model). In this problem, the input is an undirected graph G which does not fit in memory (and hence, is stored in the disk). The objective is to witness every size-3 clique of G in memory

once. This problem is a fundamental problem in graph theory, and has

numerous applications in a great variety of contexts. We will discuss the

recent development of I/O-efficient algorithms for solving this problem,

and a theoretical lower bound that proves the (near) optimality of the

state-of-the-art algorithm.

报告人简介：Yufei Tao is a full Professor in the Department of Computer Science and Engineering, Chinese University of Hong Kong (CUHK). Before joining CUHK in 2006, he was a Visiting Scientist at the Carnegie Mellon University during 2002-2003, and an Assistant Professor at the City University of Hong Kong during 2003-2006. From 2011 to 2013, he was simultaneously a Visiting Professor, under the World Class University program of the Korean government, in the Division of Web Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Korea. He obtained his PhD degree from Hong Kong University of Science and Technology (HKUST) in 2002. He received the best paper award at SIGMOD 2013, and a Hong Kong Young Scientist Award in 2002. He is currently an associate editor of ACM Transactions on Database Systems(TODS). He served as an associate editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) from 2012 to 2014. He served as a PC co-chair

of International Conference on Data Engineering (ICDE) 2014, and of

International Symposium on Spatial and Temporal Databases (SSTD) 2011.Yufei has worked extensively on indexing and query optimization in spatial and temporal databases. His current research aims to leverage the theory of data structures and computational geometry to develop practical

algorithms with non-trivial theoretical guarantees, particularly in

dealing with massive datasets that do not fit in memory.