报告题目：Spatial Crowdsourcing over Big Data: Opportunities and Challenges
报告人： Prof. Lei Chen (Hong Kong University of Science and Technology)
Lei Chen received the BS degree in computer science and engineering from Tianjin University, Tianjin, China, in 1994, the MA degree from Asian Institute of Technology, Bangkok, Thailand, in 1997, and the PhD degree in computer science from the University of Waterloo, Canada, in 2005. He is currently a full professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. His research interests include crowdsourcing over social media, social media analysis, probabilistic and uncertain databases, and privacy-preserved data publishing. The system developed by his team won the excellent demonstration award in VLDB 2014. He got the SIGMOD Test-of-Time Award in 2015. He is PC Track chairs for SIGMOD 2014, VLDB 2014, ICDE 2012, CIKM 2012, SIGMM 2011. He has served as PC members for SIGMOD, VLDB, ICDE, SIGMM, and WWW. Currently, he serves as Editor-in-Chief of VLDB Journal and an associate editor-in-chief of IEEE Transaction on Data and Knowledge Engineering. He is a member of the VLDB endowment.
Crowdsourcing is a new computing paradigm where humans are enrolled actively to participate into the procedure of computing, especially for the tasks that are intrinsically easier for human than for computers. Not surprisingly, with the development of mobile Internet, the magic power of crowdsourcing is now expanding to physical world, where each user is treated as a mobile computing unit that can be activated and guided for certain tasks. Such practice is in general termed as spatial crowdsourcing, featuring task dispatching and dynamic pricing as its core technical niches. Therefore, it serves as the fundamental prototype of a cluster of industrial applications like Citizen Sensing (Waze), P2P ride-sharing(Uber), Real-time O2O service (Instacart, Postmates) and so on. In this talk, I will first briefly review the history of crowdsourcing and discuss the key issues related to crowdsourcing. After that, I will also introduce the theoretical and practical development of our spatial crowdsourcing project, G-mission. Finally, some interesting future works on G-mission will be highlighted.