Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective

Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can be obtained on-demand so that the wisdom of crowds is easily...

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Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica Vol. 9; no. 5; pp. 749 - 762
Main Author: Zhang, Jing
Format: Journal Article
Language:English
Published: Piscataway Chinese Association of Automation (CAA) 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Subjects:
Big Data
Costs
Crowdsourcing
data fusion
Data models
Lead
Learning
learning from crowds
learning paradigms
learning with uncertainty
Systematics
Training
Uncertainty
Crowdsourcing
data fusion
learning from crowds
learning paradigms
learning with uncertainty
ISSN:2329-9266, 2329-9274
Online Access:Get full text
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Summary:Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process. During the past thirteen years, researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds. This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data, models, and learning processes. In addition to reviewing existing important work, the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work, which will light up the way for new researchers and encourage them to pursue new contributions.
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ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2022.105434