CDAS: A Crowdsourcing Data Analytics System
Xuan Liu, Meiyu Lu, Beng Chin Ooi, Yanyan Shen, Sai Wu, Meihui Zhang

TL;DR
The paper presents CDAS, a crowdsourcing data analytics system with a quality-sensitive model that improves accuracy in complex tasks like sentiment analysis and image tagging by effectively leveraging human input.
Contribution
Introduction of a novel quality-sensitive answering model integrated into a crowdsourcing system for better accuracy in complex data analytics tasks.
Findings
Human-assisted methods outperform state-of-the-art classification techniques.
The quality-sensitive model effectively guides crowdsourcing for desired accuracy.
Deployment on real datasets demonstrates improved results.
Abstract
Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Data Stream Mining Techniques · Privacy-Preserving Technologies in Data
