讲座题目:Personalized Trajectory Matching in Spatial Networks
主讲人:商朔 教授
主持人:林学民 教授
开始时间:2014-12-16 周二 14:00
讲座地址:中北校区数学馆201室
主办单位:软件学院
报告人简介：
Dr. Shuo Shang is currently a Professor of Computer Science at China University of Petroleum-Beijing. He is also an Adjunct Professor at 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). He was a Research Assistant Professor with Department of Computer Science, Aalborg University, and he was 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, respectively, 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 July 2011, July 2012, and November 2013, he spent three months at the King Abdullah University of Science and Technology (KAUST) as a visiting research scientist hosted by Prof. Panos Kalnis. His research interests include efficient query processing in spatio-temporal databases, trajectory search and mining, uncertain data management, personalized recommendation, location based services, and location based social networks. He received the "Excellent Talents of Beijing" Award in 2013 conferred by Beijing Government. 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, KAIS, Geoinformatica, DKE, and IEICE Transactions.Abstract
报告内容摘要：
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.