From crowdsourcing to crowdmining: using implicit human intelligence for better understanding of crowdsourced data
From crowdsourcing to crowdmining: using implicit human intelligence for better understanding of crowdsourced data
复制标题
从众包到众包挖掘：利用隐式人类智能更好地理解众包数据
DOI：
10.1007/s11280-019-00718-5
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发表时间：
2019-08
影响因子：
3.7
通讯作者：
Yu Zhiwen
中科院分区：
文献类型：
--
作者：
Guo Bin;Chen Huihui;Liu Yan;Chen Chao;Han Qi;Yu Zhiwen
随着移动社交网络的发展，越来越多的众包数据是在Web上产生的，或者是从现实世界的感知中收集的。然而，线上/线下众包数据的碎片化、异构性和噪声性质使得它很难被理解。传统的基于内容的分析方法存在计算量大、性能差等问题。为了解决这些问题，本文提出了CrowdMining。特别是，我们观察到，在众包数据挖掘中，隐藏在数据生成过程中的关于个人/人群行为模式(例如，流动模式、社区上下文，如社会关系和结构)以及人群-对象交互模式(闪烁或推特模式)的知识被忽略。因此，提出了一种利用隐含人类智能进行众包数据挖掘和理解的新方法。两项名为CrowdEvent和Crowdroute的研究展示了它的使用情况，其中隐含的HIS是从在线或离线众包数据中提取的。在一系列已有研究的基础上，进一步提出了CrowdMining的通用模型。基于真实数据集的实验证明了CrowdMining的有效性。
With the development of mobile social networks, more and more crowdsourced data are generated on the Web or collected from real-world sensing. The fragment, heterogeneous, and noisy nature of online/offline crowdsourced data, however, makes it difficult to be understood. Traditional content-based analyzing methods suffer from potential issues such as computational intensiveness and poor performance. To address them, this paper presents CrowdMining. In particular, we observe that the knowledge hidden in the process of data generation, regarding individual/crowd behavior patterns (e.g., mobility patterns, community contexts such as social ties and structure) and crowd-object interaction patterns (flickering or tweeting patterns) are neglected in crowdsourced data mining. Therefore, a novel approach that leverages implicit human intelligence (implicit HI) for crowdsourced data mining and understanding is proposed. Two studies titled CrowdEvent and CrowdRoute are presented to showcase its usage, where implicit HIs are extracted either from online or offline crowdsourced data. A generic model for CrowdMining is further proposed based on a set of existing studies. Experiments based on real-world datasets demonstrate the effectiveness of CrowdMining.
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DOI：
10.1145/1989323.1989331
发表时间：
2011-06
期刊：
IEEE/RSJ International Conference on Intelligent Robots and Systems
影响因子：
--
作者：
M. Franklin;Donald Kossmann;Tim Kraska;Sukriti Ramesh;Reynold Xin
通讯作者：
M. Franklin;Donald Kossmann;Tim Kraska;Sukriti Ramesh;Reynold Xin
DOI：
10.1145/2684822.2685287
发表时间：
2015-02
期刊：
Proceedings of the Eighth ACM International Conference on Web Search and Data Mining
影响因子：
--
作者：
Yuan Zhong;Nicholas Jing Yuan;Wen Zhong;Fuzheng Zhang;Xing Xie
通讯作者：
Yuan Zhong;Nicholas Jing Yuan;Wen Zhong;Fuzheng Zhang;Xing Xie
影响因子：
10.6
作者：
Jianwen Xu;K. Ota;M. Dong
通讯作者：
Jianwen Xu;K. Ota;M. Dong
DOI：
10.1145/957013.957093
发表时间：
2003-11
期刊：
Proceedings of the eleventh ACM international conference on Multimedia
影响因子：
--
作者：
Matthew L. Cooper;J. Foote;Andreas Girgensohn;L. Wilcox
通讯作者：
Matthew L. Cooper;J. Foote;Andreas Girgensohn;L. Wilcox
影响因子：
7.9
作者：
Yi Wang;Wenjie Hu;Yibo Wu;G. Cao
通讯作者：
Yi Wang;Wenjie Hu;Yibo Wu;G. Cao