时间

2017年3月23日 （周四）上午10点-11点

 

地点

静园六院212会议室

 

摘要

The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and expressions, which points to the importance of understanding and discovering the knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an efficient and elegant way. In this talk we introduce an event cube (EC) model which is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. Specifically, based on essential event elements of 5W1H, the EC model is developed to organize the discovered events from multiple dimensions, to operate on the events at various levels of granularity, so as to facilitate analyzing and mining hidden/inherent relationships among the events effectively. (This work is part of a large collaborative project which involves 4 universities in Hong Kong.)

 

简历

李青，教授，博士生导师，香港城市大学多媒体工程研究中心主任、电脑科学系终身教授，主要研究领域包括多媒体数据管理、概念建模、数据挖掘、社交媒体与Web服务计算等。他在相关领域发表了300多篇的国际会议与期刊文章，是NSFC 海外杰青获得者。曾任ACM Transactions on Internet Technology, IEEE Transactions on Knowledge and Data Engineering 的副编，目前是WWW journal, Data & Knowledge Engineering, Journal of Web Engineering 等期刊的编委。他现任香港万维网科技学会会长、国际万维网信息系统工程(WISE)学会副会长、CCF数据库专委会常务委员、CCF大数据专家委员会委员等，同时在多个国际会议（包括ACM RecSys, DASFAA, ER, ICWL, IEEE U-Media）出任指导委员会委员。