ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (2): 284-294.doi: 10.7544/issn1000-1239.2017.20160850

Special Issue: 2017科学大数据管理专题

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Crowdsourcing-Based Scientific Data Processing

Zhao Jianghua1,2, Mu Shuting3, Wang Xuezhi1, Lin Qinghui1, Zhang Xi3, Zhou Yuanchun1   

  1. 1(Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190);2(University of Chinese Academy of Sciences, Beijing 100049);3(College of Management and Economics, Tianjin University, Tianjin 300072)
  • Online:2017-02-01

Abstract: The ultimate goal of acquiring scientific data is to extract useful knowledge from the data according to specific needs and apply the knowledge to specific areas to help decision makers make decisions. As the volume of scientific data becomes larger, and the structure becomes more complex, such as semi or unstructured data, it is difficult to automatically process these data by computers. By incorporating human computing power in data processing, crowdsourcing has become one of the solutions for big scientific data processing. By analyzing the characteristics of crowdsourcing scientific data processing tasks to citizens, this paper studies three aspects, which are talent selection mechanism, task execution mode, and result assessment strategy. Then a series of crowdsourcing-based remote sensing imagery interpretation experiments are carried out. Results show that not only scientific data can be processed through crowdsourcing paradigm, but also by designing reasonable procedure, high-quality data can be obtained.

Key words: crowdsourcing, scientific big data, data processing, talent selection, quality assessment

CLC Number: