Title :
Data-driven crowdsourcing: Management, mining, and applications
Author :
Lei Chen ; Dongwon Lee ; Milo, Tova
Author_Institution :
HKUST, Kowloon, China
Abstract :
In this 3-hour tutorial, we present the landscape of recent developments in data management and mining research, and survey a selected set of state-of-the-art works that significantly extended existing database reserach in order to incorporate and exploit the novel notion of “crowdsourcing” in a creative fashion. In particular, three speakers take turns to present the topics of human-powered database operations, crowdsourced data mining, and the application of crowdsourcing in social media, respectively.
Keywords :
data mining; social networking (online); crowdsourced data mining; data management; data-driven crowdsourcing; human-powered database operations; social media; Crowdsourcing; Data mining; Databases; Media; Sociology; Statistics; Tutorials;
Conference_Titel :
Data Engineering (ICDE), 2015 IEEE 31st International Conference on
Conference_Location :
Seoul
DOI :
10.1109/ICDE.2015.7113418