ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2015, Vol. 52 ›› Issue (2): 309-317.doi: 10.7544/issn1000-1239.2015.20140267

Special Issue: 2015大数据管理

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Theme-Aware Task Assignment in Crowd Computing on Big Data

Zhang Xiaohang1,2,Li Guoliang2, Feng Jianhua2   

  1. 1(Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084); 2(Department of Computer Science and Technology, Tsinghua University, Beijing 100084)
  • Online:2015-02-01

Abstract: Big data has brought tremendous challenges for the traditional computing model, because of its inherent characteristics such as large volume, high velocity, high variety, low-density value. On the one hand, the large volume and high velocity require the techniques of massive data computation and analysis; on the other hand, the high variety and low-density value make big data computing tasks highly depend on the complex cognitive reasoning technology. To overcome the coexistence challenges of massive data analysis and complex cognitive reasoning, human-machine collaboration based crowd computing is an effective way to solve the big data problem. In crowd computing, task assignment is one of the basic problems. However the current crowdsourcing platforms cannot support the active task assignment, which iteratively assigns tasks to appropriate workers based on the knowledge background or users. To address this problem, we propose an iterative theme-aware task assignment framework, and deploy it into existing crowdsourcing platforms. The framework includes two components. The first component is task modeling, which models the tasks as a graph where vertices are tasks and edges are task relationships. The second component is the iterative task assignment algorithm, which identifies the themes of the workers by their historical records, computes the workers’ accuracy on different themes, and assigns the tasks to the appropriate workers. Various experiments validate the effectiveness of our method.

Key words: crowd computing, human computation, big data, crowdsourcing, human-computer interaction

CLC Number: