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A Low-Code Framework for Complex Crowdsourcing Work Based on Process Modeling.

Tianhong Xiong1, Maolin Pan1, Yang Yu1

  • 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.

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This summary is machine-generated.

This study introduces a novel approach for crowdsourcing process modeling, enabling dynamic task creation and collaboration for complex, open-ended work. The CrowdModeller framework effectively addresses the limitations of traditional models in handling unpredictable task execution.

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Human-Computer Interaction

Background:

  • Crowdsourcing leverages online communities for complex problem-solving.
  • Crowdsourcing process modeling is gaining attention to simplify application development.
  • Existing models struggle with dynamic, open-ended tasks common in creative crowdsourcing.

Purpose of the Study:

  • To address the limitations of current process modeling for dynamic crowdsourcing tasks.
  • To propose a flexible approach for modeling complex, unpredictable crowdsourcing workflows.
  • To introduce a framework supporting on-demand task creation and dynamic collaboration.

Main Methods:

  • Developed a task model composition for on-demand task creation.
  • Implemented a tree-based collaboration structure to adapt to dynamic task execution.
  • Introduced message communication modes for inter-task data exchange.
  • Constructed the CrowdModeller framework to embody the proposed approach.

Main Results:

  • The CrowdModeller framework effectively models dynamic crowdsourcing processes.
  • Evaluations demonstrated the framework's ability to handle unpredictable task structures.
  • The approach supports flexible task creation and data exchange in complex workflows.

Conclusions:

  • The proposed modeling approach and CrowdModeller framework successfully address the challenge of modeling dynamic crowdsourcing processes.
  • This work enables more effective crowdsourcing application development for creative and open-ended tasks.
  • The findings facilitate the creation of adaptable and robust crowdsourcing systems.