SmartCrowd: A Workflow Framework for Complex Crowdsourcing Tasks

Xiong, Tianhong; Yu, Yang; Pan, Maolin; Yang, Jing

doi:10.1007/978-3-030-11641-5_31

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is paper extends our previous work [23] in the following five aspects: we introduced the model composition to represent the crowdsourcing process, rather than a single specific model; we gave the formal definition of task model composition and instance tree, rather than the text description of a single model; we improved the framework structure, model design, and implementation interfaces; we provided a more comprehensive comparison of the existing crowdsourcing modeling approaches; and we conducted a set of more extensive experiments.…”

Section: Introductionsupporting

confidence: 86%

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

Xiong

Pan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Crowdsourcing has become a new distributed paradigm, which uses online crowds to solve complex problems. Recently, in order to reduce the development workload and research threshold of crowdsourcing applications, crowdsourcing process modeling is attracting more and more attention. However, complex crowdsourcing processes used for creative and open-ended work have remained out of reach for process modeling, because this type of process usually has a dynamic execution, in which the type, number, and order of tasks and subtasks are often unknown in advance but are determined dynamically at runtime. In this paper, we propose a modeling approach and supporting framework to fill this gap. Specifically, we provide a task model composition to allow task creation on demand, while collaborating on tasks in a tree structure to adapt to the dynamic execution. Moreover, we introduce a set of message communication modes to support data exchange among tasks. Finally, we construct a framework named CrowdModeller to embody this approach. Through two evaluations, we demonstrate its effectiveness.

show abstract

Section: Introductionsupporting

confidence: 86%

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

Xiong

Pan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zheng et al proposed a general workflow technology by using a state machine based on recursive decomposition approaches, wherein varying types of crowdsourcing applications could be developed using this platform, to meet the requirement of solving diverse tasks [21]. Xiong et al extended Zheng's work by proposing a workflow framework called the SmartCrowd for complex crowdsourcing tasks [22]. Wu et al presented the Service4Crowd, which was a highly flexible and extensible process management platform for crowdsourcing on the basis of service-oriented architectures [23].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Fairness of Task Allocation in Crowdsourcing Workflows

Liu

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Fairness plays a vital role in crowd computing by attracting its workers. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Requester-centric task allocation algorithms aim to maximize the completion quality of the entire workflow and minimize its total cost, which are discriminatory for workers. The crowdsourcing workflow needs to balance two objectives, namely, fairness and cost. In this study, we propose an alternative greedy approach with four heuristic strategies to address such an issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. The F-Aware allocates each task to the worker that maximizes the fairness and minimizes the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, fairness, and cost by using a customer objective function on the WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that the F-Aware, which is 1% better than the best competitor algorithm, outperforms other optimal solutions in finding the tradeoff between fairness and cost.

show abstract

Trust-aware Task Allocation in Collaborative Crowdsourcing Model

Liu

2021

The Computer Journal

View full text Add to dashboard Cite

Task allocation plays a vital role in crowd computing by determining its performance. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Task allocation aims to maximize the completion quality of the entire workflow and minimize its total cost. Trust can affect the quality of the produced results and costs. Selecting workers with high levels of trust could provide better solution to the workflow and increase the budget. Crowdsourcing workflow needs to balance the two conflicting objectives. In this paper, we propose an alternative greedy approach with four heuristic strategies to address the issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. T-Aware allocates each task to the worker that maximizes the trust level, while minimizing the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, task failure ratio, trust and cost using a customer objective function on WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that T-Aware outperforms other optimal solutions on finding the tradeoff between trust and cost, which is 3 to 6% better than the best competitor algorithm.

show abstract

SmartCrowd: A Workflow Framework for Complex Crowdsourcing Tasks

Cited by 3 publications

References 15 publications

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

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

Fairness of Task Allocation in Crowdsourcing Workflows

Trust-aware Task Allocation in Collaborative Crowdsourcing Model

Contact Info

Product

Resources

About