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A Crowdsourcing Worker Quality Evaluation Algorithm on MapReduce for Big Data Applications

机译：基于MapReduce的大数据应用众包工人质量评估算法

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摘要

Crowdsourcing is a new emerging distributed computing and business model on the backdrop of Internet blossoming. With the development of crowdsourcing systems, the data size of crowdsourcers, contractors and tasks grows rapidly. The worker quality evaluation based on big data analysis technology has become a critical challenge. This paper first proposes a general worker quality evaluation algorithm that is applied to any critical tasks such as tagging, matching, filtering, categorization and many other emerging applications, without wasting resources. Second, we realize the evaluation algorithm in the Hadoop platform using the MapReduce parallel programming model. Finally, to effectively verify the accuracy and the effectiveness of the algorithm in a wide variety of big data scenarios, we 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 is suitable for large-scale worker quality evaluations in a big data environment.
机译：众包是在互联网蓬勃发展的背景下一种新兴的分布式计算和商业模式。随着众包系统的发展，众包方，承包商和任务的数据量迅速增长。基于大数据分析技术的工人质量评估已成为一项严峻的挑战。本文首先提出了一种通用的工人质量评估算法，该算法可应用于任何关键任务，例如标记，匹配，过滤，分类和许多其他新兴应用，而不会浪费资源。其次，我们使用MapReduce并行编程模型在Hadoop平台上实现评估算法。最后，为了有效地验证算法在各种大数据场景中的准确性和有效性，我们进行了一系列实验。实验结果表明，该算法是准确有效的。它具有较高的计算性能和水平可伸缩性。它适用于大数据环境中的大规模工人质量评估。

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