祝贺研究组成员刘鑫、张国荣和张陶然获得硕士学位。感谢他们在研究室期间的努力工作。祝他们前程似锦！

祝贺研究组成员樊峰峰获得博士学位。感谢他在研究室期间的努力工作。祝他前程似锦！

日期: 星期一，2018年10月22日
时间: 14:30-17:30 pm
地点: 计算机学院楼320会议室
主讲人: 鲍芝峰教授 [INFO]

Talk 1: Trajectory-driven Influential Billboard Placement
In this talk I will present our recent work on "Trajectory-driven Influential Billboard Placement" which is one of the Best Papers of KDD 2018 (Best Paper Award Nomination). In this paper we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T and a budget L, find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1 − 1/e) approximation ratio. However, the enumeration should be very costly when |U| is large. By exploiting the locality property of billboards’ influence, we propose a partition- based framework PartSel. PartSel partitions U into a set of small clusters, computes the locally influential billboards for each cluster, and merges them to generate the global solution. Since the local solutions can be obtained much more efficient than the global one, PartSel should reduce the computation cost greatly; meanwhile it achieves a non-trivial approximation ratio guarantee. Then we propose a LazyProbe method to further prune billboards with low marginal influence, while achieving the same approximation ratio as PartSel. Experiments on real datasets verify the efficiency and effectiveness of our methods.

Talk 2: A Visual Exploration of the Spatial Data
In this talk, I will first introduce the system called HomeSeeker we build for an interactive and visualized exploration of the location-centered real estate data in Australia for the last ten years, as well as the research problems and techniques behind the system. In particular, we will talk about the query processing to facilitate buyers to find a desired property and for sellers to find the best time to enter the market to sell the house.

Bio:
Zhifeng Bao is an Associate Professor in Computer Science, RMIT (Royal Melbourne Institute of Technology) university and an Adjunct Fellow at University of Melbourne, Australia. He received his PhD from the CS Dept at NUS in 2011. Zhifeng was the only recipient of the Best PhD Thesis Award in School of Computing and was the winner of the Singapore IDA (Infocomm Development Authority) gold medal. Zhifeng was a winner of the Google Faculty Research Award 2015. His research interests include data visualization, spatial data analytics and data integration. He served the PC Co-chair of DASFAA17, ER18, APWEB16, WSDM19 Cup, etc, and served the PC member of top conferences such as VLDB17-18, SIGMOD18, SIGIR15-18, ICDE16-19, IJCAI16. Zhifeng has received four best paper awards such as DASFAA17, ADC16, and six best paper nomination such as KDD 2018, IEEE ICDE 2009, CIKM 2014. Since 2015 he has secured more than 1 million AUD funding as the chief investigator from Australasian Research Council, CSIRO and Google.

日期: 星期二，2018年6月5日
时间: 10:00 am
地点: 计算机学院楼105会议室
主讲人: 牛笛教授 [个人主页]
主题: 故事森林：基于AI和自然语言处理的大规模新闻梳理系统

Di will describe his recent experience of implementing a news content organization system in collaboration with Tencent that can discover hot events from vast streams of breaking news and connect events into stories for easy viewing. Our real-world system has distinct requirements in contrast to previous studies on document topic modeling and detection, in that 1) an "event" does not only contain articles of a similar topic, but is a cluster of documents that report exactly the same physical news event; 2) we must evolve news stories in a logical and online manner. In solving these challenges, he proposes Story Forest, a state-of-the-art news content organization system based on artificial intelligence and natural language processing. He will briefly describe the key enabling technologies in Story Forest, including text matching and identifying the relationship between text objects, e.g., whether they talk about the same event or whether one article is a follow-up of another, based on deep learning. His system has been deployed in Tencent QQ Browser mobile app.

个人简介:
Dr. Di Niu (牛笛) is currently a tenured Associate Professor in the Department of Electrical and Computer Engineering at the University of Alberta, Edmonton, Canada, specialized in the interdisciplinary area of distributed systems and optimization, machine learning, data science, data mining, and large-scale data analytics. He received the B.Eng. from Sun Yat-sen University in 2005 and the MSc and PhD degrees from the University of Toronto in 2009 and 2013, respectively.
He has coauthored more than 50 papers in top conferences and journals, including SIGKDD, the Web Conference (WWW), AAAI, INFOCOM, CIKM, ICDM, Multimedia, SIGMETRICS, IEEE/ACM Transactions on Networking, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Multimedia, ACM TOMPECS, etc. He was the winner of the Extraordinary Award of the CCF-Tencent Rhino Bird Open Grant 2016-2017 (with project results ranked No. 1 out of 18 award holders) due to his invention of the Story Forest system for news article understanding and hot event extraction at scale, which has been deployed in Tencent QQBrowser mobile app.

On April 1st, we were humbled by the visit of professor Shuai Ma, head of the Data Management and Analysis Group in Beihang University and professor HongZhi Wang from Harbin Institute of Technology. Both of our guests gave very informative talks about Big Data Processing Technologies which were followed by a compelling discussion about various interesting topics. The professors were keen to answer all the questions that have been raised by the audiance. We look forward to seeing them again.

祝贺研究组成员耿培、代凯、李艺毅获得硕士学位；王卓获得博士学位。感谢他们在研究室期间的努力工作。祝他们前程似锦！