Visual analytics for an optimized and quality improved crowdsourcing process
|has title::Visualization of disagreement-based quality metrics of crowdsourcing data|
|Master:||project within::AI and Communication|
|Student name:||student name::Cristea Tatiana|
In this research we propose a visual-analytics model in the context of Crowd-Watson: a framework that uses Crowdsourcing methods to generate gold standard training and evaluation data for machine learning purposes. The proposed visual-analytic model aims to optimize the Crowdsourcing process and improve its quality by efficiently dealing with large and noisy amounts of data obtained as result of the Crowdsourcing process. Requirements for the dynamic, scalable and interactive visualizations were extracted from literature and through interviews with users of the framework.