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CDAS: A Crowdsourcing Data Analytics System
| Citations: | 36 - 3 self |
BibTeX
@MISC{Liu_cdas:a,
author = {Xuan Liu and Beng Chin Ooi and Yanyan Shen and Meihui Zhang},
title = {CDAS: A Crowdsourcing Data Analytics System},
year = {}
}
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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 guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers ’ historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the qualitysensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality. 1.
Keyphrases
crowdsourcing data analytics system query engine twitter sentiment analytics job online strategy quality-sensitive model state-of-the-art technology good supplement satisfactory accuracy crowdsourcing job processing cost current technology human-oriented task image annotation technique image tagging human-assisted method real twitter natural language processing quality-sensitive answering model computer-oriented task state-of-the-art classification human task estimated accuracy required query answer quality many computer task complex problem analytics job core part human participation human worker historical performance complex job qualitysensitive model flickr data
