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Data Centric Workflows for Crowdsourcing

机译:以数据为中心的众包工作流

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摘要

Crowdsourcing consists in hiring workers on internet to perform large amounts of simple, independent and replicated work units, before assembling the returned results. A challenge to solve intricate problems is to define orchestrations of tasks, and allow higher-order answers where workers can suggest a process to obtain data rather than a plain answer. Another challenge is to guarantee that an orchestration with correct input data terminates, and produces correct output data. This work proposes complex workflows, a data-centric model for crowdsourcing based on orchestration of concurrent tasks and higher order schemes. We consider termination (whether some/all runs of a complex workflow terminate) and correctness (whether some/all runs of a workflow terminate with data satisfying FO requirements). We show that existential termination/correctness are undecidable in general excepted for specifications with bounded recursion. However, universal termination/correctness are decidable when constraints on inputs are specified in a decidable fragment of FO, and are at least in co - 2EXPTIME.
机译:众包在于在组装返回的结果之前,在互联网上雇用工人执行大量简单,独立和重复的工作单元。解决复杂问题的挑战是定义任务的编排,并允许高阶答案,以便工人可以建议一个获取数据的过程,而不是简单的答案。另一个挑战是确保具有正确输入数据的业务流程终止,并产生正确的输出数据。这项工作提出了复杂的工作流程,一个基于数据的以众包为基础的模型,用于基于并发任务和高阶方案的编排。我们考虑终止(复杂工作流的某些/全部运行是否终止)和正确性(工作流的某些/所有运行是否以满足FO要求的数据终止)。我们表明,除了具有有限递归的规范外,存在终止/正确性通常是不可确定的。但是,当在FO的可确定片段中指定对输入的约束时,通用终止/正确性是可确定的,并且至少在co-2EXPTIME中。

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