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SmartCrowd: A Workflow Framework for Complex Crowdsourcing Tasks

  • Tianhong Xiong
  • Yang YuEmail author
  • Maolin Pan
  • Jing Yang
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)

Abstract

Over the past decade, a number of frameworks have been introduced to support different crowdsourcing tasks. However, complex creative tasks have remained out of reach for workflow modeling. Unlike typical tasks, creative tasks are often interdependent, requiring human cognitive ability and team collaboration. The crowd workers are required not only to perform typical tasks, but also to participate in the analysis and manipulation of complex tasks, hence the number and execution order of tasks are unknown until runtime. Thus, it is difficult to model this kind of complex tasks by using existing workflow approaches. Therefore, we propose a workflow modeling approach based on state machine to design crowdsourcing model that can be translated into SCXML code and executed by an open source engine. This approach and engine are embodied in SmartCrowd. Through two evaluations, we found that SmartCrowd can provide support for complex crowdsourcing tasks, especially on creative tasks. Moreover, we introduce a set of basic design patterns, and by employing them to compose complex patterns, our framework can support more crowdsourcing research.

Keywords

Crowdsourcing State machine Workflow Complex tasks Creative tasks Design patterns 

Notes

Acknowledgments

This work was supported by the National Key Research and Development Program of China under Grant No. 2017YFB0202200; the National Natural Science Foundation of China under Grant No. 61572539; the Research Foundation of Science and Technology Major Project in Guangdong Province under Grant Nos. 2015B010106007, 2016B010110003; the Research Foundation of Science and Technology Plan Project in Guangdong Province under Grant No. 2016B050502006.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tianhong Xiong
    • 1
  • Yang Yu
    • 1
    Email author
  • Maolin Pan
    • 1
  • Jing Yang
    • 1
  1. 1.Sun Yat-sen UniversityGuangzhouChina

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