Crowd-Powered Data Management
Recorded 07 December 2016 in Lausanne, Vaud, Switzerland
Event: IC Colloquia - EPFL IC School Colloquia
Modern data analysis combines general knowledge stored in databases with individual knowledge obtained from the crowd, capturing people habits and preferences. To account for such mixed knowledge, along with user interaction and optimization issues, data management platforms must employ a complex process of reasoning, automatic crowd-task generation and result analysis.
In this talk, I will introduce the notion of crowd mining and describe a generic architecture for crowd mining applications. This architecture allows us to examine and compare the components of existing crowdsourcing systems and point out extensions required by crowd mining. It also highlights new research challenges and potential reuse of existing techniques/components. I will exemplify this for the OASSIS project, a system developed in Tel Aviv University, and for other prominent crowdsourcing frameworks.
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