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Spatial Crowdsourcing, Opportunities and Challenges
Spatial Crowdsourcing, Opportunities and Challenges
教师介绍
Associate Professor ,Department of Computer Science and Engineering,HongKong University of Science and Technology Lei Chen received his BS degree in Computer Science at Tian Jin University ,P.R.China(BS 94), and an MA degree in computer science at Asian Institute of Technology (AIT) Asian Institute of Technology (MS 97). He received a Ph.D. degree in Computer Science at University of Waterloo.Research Interests:Crowdsourcing-based Data Processing, Uncertain and Probabilistic databases, Web data management, Multimedia and Time series databases, Privacy.

本讲教师:陈雷
所属学科:工科
人  气:5408

课程介绍
As one of the successful forms of using Wisdom of Crowd, crowdsourcing, has been widely used for many human intrinsic tasks, such as image labeling, natural language understanding, market predication and opinion mining. Meanwhile, with advances in pervasive technology, mobile devices, such as mobile phones, tablets, and PDA, have become extremely popular. These mobile devices can work as sensors to collect various types of data, such as pictures, videos and texts. Therefore, in crowdsourcing, a requester can unitize power of mobile devices and their location information to ask for resources related a specific location, the mobile users who would like to take the task will travel to that place and get the data (videos, audios, or pictures) and then send the data to the requester. This type of crowdsourcing is called spatial crowdsourcing. Due to the rapid growth of mobile device uses and amazing functionality provided by mobile devices, spatial crowdsourcing will become more popular than general crowdsourcing, such as Amazon Turk and Crowdflower.
In this talk, I will first briefly review the history of crowdsourcing and discuss the key issues related to crowdsourcing. Then, I will demonstrate the power of spatial crowdsourcing with our recent developed software, gMission. Finally, I will highlight challenges and research opportunities about spatial crowdsourcing over Big Data.
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