报告日期及时间：2016-01-06 周三上午10点
报告地点：B403
报告人：商硕教授
报告人单位：中国石油大学（北京）
报告人简介：Dr. Shuo Shang is a Professor of Computer Science at China University of Petroleum-Beijing. He is an Assistant Director of Beijing Key Laboratory of Big Data Management and Analysis Methods, and an Adjunct Professor at MOE Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China. He is a member of China Computer Federation Technical Committee on Databases(CCF-TCDB) and a member of China Association of Geographic Information Society Technical Committee on Theory and Methodology. He was a Research Assistant Professor with Department of Computer Science, Aalborg University, and he was also a faculty member of the Center for Data-intensive Systems (Daisy), Aalborg University. He obtained his B.Sc. from Peking University in 2008, and Ph.D. from The University of Queensland in 2012, both in Computer Science. During September to December 2011, he spent three months at Aarhus University as a visiting scholar hosted by Prof. Chiristian S. Jensen. During 2011 to 2015, he also holds positions of visiting research scientist and visiting professor at the Nanyang Technological University (NTU), Nagoya University, Aalborg University, Queens University Belfast, and University of Utah. His research interests include efficient query processing in spatio-temporal databases, spatial trajectory computing, and location based social media. He was the winner of "Beijing New Star in Science and Technology" (Beijing Nova Program) of Year 2016. He received the "Excellent Talents of Beijing" Award of Year 2014 conferred by Beijing Government, and received the "Distinguished Young Researcher" Award of Year 2013 conferred by China University of Petroleum-Beijing. He was on Session Chair (session of moving objects) of ICDE 2013. He is on the reviewer board of several top database/data mining journals such as IEEE TKDE, The VLDB Journal, ACM TIST, ACM TSAS, KAIS, Geoinformatica, DKE, WWWJ, JCST, and IEICE Transactions.
报告摘要：
With the increasing availability of moving-object tracking data, trajectory search and matching is increasingly important. We propose and investigate a novel problem called Personalized Trajectory Matching (PTM). In contrast to conventional trajectory similarity search by spatial distance only, PTM takes into account the significance of each sample point in a query trajectory. A PTM query takes a trajectory with user specified weights for each sample point in the trajectory as its argument. It returns the trajectory in an argument data set with the highest similarity to the query trajectory. We believe that this type of query may bring significant benefits to users in many popular applications such as route planning, carpooling, friend recommendation, traffic analysis, urban computing, and location based services in general.
PTM query processing faces two challenges: how to prune the search space during the query processing and how to schedule multiple so-called expansion centers effectively. To address these challenges, a novel two-phase search algorithm is proposed that carefully selects a set of expansion centers from the query trajectory and exploits upper and lower bounds to prune the search space in the spatial and temporal domains. An efficiency study reveals that the algorithm explores the minimum search space in both domains. Second, a heuristic search strategy based on priority ranking is developed to schedule the multiple expansion centers, which can further prune the search space and enhance the query efficiency. The performance of the PTM query is studied in extensive experiments based on real and synthetic trajectory data sets.
邀请人：彭煜玮
