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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Quality Control of Massive Data for Crowdsourcing in Location-Based Services
verfasst von : Gang Zhang, Haopeng Chen
Erschienen in: Algorithms and Architectures for Parallel Processing
Verlag: Springer International Publishing
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Crowdsourcing has become a prospective paradigm for commercial purposes in the past decade, since it is based on a simple but powerful concept that virtually anyone has the potential to plug in valuable information, which brings a lot of benefits such as low cost and high immediacy, particularly in some location-based services (LBS). On the other side, there also exist many problems need to be solved in crowdsourcing. For example, the quality control for crowdsourcing systems has been identified as a significant challenge, which includes how to handle massive data more efficiently, how to discriminate poor quality content in workers’ submission and so on. In this paper, we put forward an approach to control the crowdsourcing quality by evaluating workers’ performance according to their submitted contents. Our experiments have demonstrated the effectiveness and efficiency of the approach.