A Crowdsourcing Worker Quality Evaluation Algorithm on Mapreduce for Big Data Applications - 2016
Crowdsourcing may be a new rising distributed computing and business model on the backdrop of Internet blossoming. With the development of crowdsourcing systems, the info size of crowdsourcers, contractors and tasks grows rapidly. The employee quality evaluation based mostly on huge data analysis technology has become a important challenge. This paper first proposes a general worker quality analysis algorithm that is applied to any crucial tasks like tagging, matching, filtering, categorization and many different rising applications, without wasting resources. Second, we have a tendency to notice the evaluation algorithm within the Hadoop platform using the MapReduce parallel programming model. Finally, to effectively verify the accuracy and also the effectiveness of the algorithm during a large choice of massive information scenarios, we have a tendency to conduct a series of experiments. The experimental results demonstrate that the proposed algorithm is accurate and effective. It has high computing performance and horizontal scalability. And it's appropriate for large-scale employee quality evaluations in a massive data surroundings.
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