Crowdsourcing-Based Scientific Data Processing

Zhao Jianghua; Mu Shuting; Wang Xuezhi; Lin Qinghui; Zhang Xi; Zhou Yuanchun

doi:10.7544/issn1000-1239.2017.20160850

Journal of Computer Research and Development > 2017 > 54(2): 284-294. > DOI: 10.7544/issn1000-1239.2017.20160850

Zhao Jianghua, Mu Shuting, Wang Xuezhi, Lin Qinghui, Zhang Xi, Zhou Yuanchun. Crowdsourcing-Based Scientific Data Processing[J]. Journal of Computer Research and Development, 2017, 54(2): 284-294. DOI: 10.7544/issn1000-1239.2017.20160850

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Crowdsourcing-Based Scientific Data Processing

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)

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Published Date: January 31, 2017

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Abstract

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.
- crowdsourcing,
- scientific big data,
- data processing,
- talent selection,
- quality assessment

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[2]	Wang Hang, Tian Shengzhao, Tang Qing, Chen Duanbing. Few-Shot Image Classification Based on Multi-Scale Label Propagation[J]. Journal of Computer Research and Development, 2022, 59(7): 1486-1495. DOI: 10.7544/issn1000-1239.20210376
[3]	Cao Jiuxin, Gao Qingqing, Xia Rongqing, Liu Weijia, Zhu Xuelin, Liu Bo. Information Propagation Prediction and Specific Information Suppression in Social Networks[J]. Journal of Computer Research and Development, 2021, 58(7): 1490-1503. DOI: 10.7544/issn1000-1239.2021.20200809
[4]	Hu Dou, Wei Lingwei, Zhou Wei, Huai Xiaoyong, Han Jizhong, Hu Songlin. A Rumor Detection Approach Based on Multi-Relational Propagation Tree[J]. Journal of Computer Research and Development, 2021, 58(7): 1395-1411. DOI: 10.7544/issn1000-1239.2021.20200810
[5]	Du Ming, Yang Yun, Zhou Junfeng, Chen Ziyang, Yang Anping. Efficient Methods for Label-Constraint Reachability Query[J]. Journal of Computer Research and Development, 2020, 57(9): 1949-1960. DOI: 10.7544/issn1000-1239.2020.20190569
[6]	Zheng Wenping, Che Chenhao, Qian Yuhua, Wang Jie. A Two-Stage Community Detection Algorithm Based on Label Propagation[J]. Journal of Computer Research and Development, 2018, 55(9): 1959-1971. DOI: 10.7544/issn1000-1239.2018.20180277
[7]	Song Pan, Jing Liping. Exploiting Label Relationships in Multi-Label Classification with Neural Networks[J]. Journal of Computer Research and Development, 2018, 55(8): 1751-1759. DOI: 10.7544/issn1000-1239.2018.20180362
[8]	Ma Gang, Du Yuge, An Bo, Zhang Bo, Wang Wei, Shi Zhongzhi. Risk Evaluation of Complex Information System Based on Threat Propagation Sampling[J]. Journal of Computer Research and Development, 2015, 52(7): 1642-1659. DOI: 10.7544/issn1000-1239.2015.20140184
[9]	Zhu Xiang, Jia Yan, Nie Yuanping, Qu Ming. Event Propagation Analysis on Microblog[J]. Journal of Computer Research and Development, 2015, 52(2): 437-444. DOI: 10.7544/issn1000-1239.2015.20140187
[10]	She Qiaoqiao, Yu Yang, Jiang Yuan, and Zhou Zhihua. Large-Scale Image Annotation via Random Forest Based Label Propagation[J]. Journal of Computer Research and Development, 2012, 49(11): 2289-2295.