3月28日交叉学科论坛学术报告(商烁 阿卜杜拉国王科技大学)
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报告题目:   Searching Trajectories by Regions of Interest
报告时间:   3月28日下午15:00
报告地点:   计算机学院B403
报告人:     商烁
报告人单位: 阿卜杜拉国王科技大学
 
报告人简介:商烁,沙特阿卜杜拉国王科技大学(KAUST)极限计算中心研究员,博士生导师。2008年本科毕业于北京大学,2012年博士毕业于澳大利亚昆士兰大学。研究方向包括大数据、城市计算、时空数据库、社交媒体分析等。在相关领域发表论文50余篇,含CCF A类论文19篇,SCI论文25篇,EI论文29篇,Google Scholar引用780余次,SCI引用200余次。曾获WISE 2017最佳论文奖。曾担任WWW Journal客座编辑、IEEE Big Data Congress领域主席、APWeb/WAIM 2017大会演示主席、ICDE 2013移动对象分会场主席,VLDB 2019、SIGMOD 2018、ICDE
2018、CIKM 2018、2017、DASFAA 2018、2015、2014程序委员会委员,并担任VLDB Journal、IEEE TKDE、ACM TIST等数据管理和数据挖掘领域顶级期刊的评审专家。
报告摘要:With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.
 
邀请人:交叉学科论坛(季论坛)
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