Asking the Right Questions in Crowd Data Sourcing

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How researchers see this paper

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A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “ASKIT! [51] uses this assumption and focuses on the problem of determining which questions should be directed to which users, in order to minimize the uncertainty of the collected data.”
Truth Inference in Crowdsourcing: Is the Problem Solved? (2017, background): “Recently, there are some works [60, 19, 34, 7] that study how to assign tasks to appropriate workers in an online manner, called Online Task Assignment.”
Crowdsourced Data Management: A Survey (2016, methodology): “The problem is defined as: Given n tasks, when a worker comes, which k tasks (k is predefined) should be assigned to the coming worker? When a worker comes, [18], [74] compute an uncertainty”
Foundations of Crowd Data Sourcing (2015, background): “The first is [4], which makes the closed-world assumption that there is a known set of missing values.”
Cleaning Crowdsourced Labels Using Oracles For Statistical Classification (2018, background): “There are three research topics in crowdsourcing related to our work: task assignment [4,6,13,17,26,31,48], truth inference [12,16,19,33,47], and active learning (from crowds) [18, 24,27,45].”