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The stem-based vector space model for automatic resource allocation in workflow

机译:用于工作流中自动资源分配的基于词干的向量空间模型

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In the context of workflow,resource allocation is often performed manually and empirically. In this paper,we present a novel approach based on stem-based vector space model (VSM) to automate the resource allocation works. The VSM similarity is utilized to compute the cohesion measures between the resources and the tasks,and the cohesion measures are then used to rank the resource candidates,and the resource with the highest score will be allocated to the specific workflow task. The initial evaluation on our experiments shows that our method can efficiently predicate the performers for the workflow tasks with good precision.
机译:在工作流的上下文中,资源分配通常是手动和凭经验执行的。在本文中,我们提出了一种基于词干的向量空间模型(VSM)的新颖方法来自动执行资源分配工作。利用VSM相似度计算资源与任务之间的内聚度,然后使用内聚度对候选资源进行排序,将得分最高的资源分配给特定的工作流任务。对我们的实验的初步评估表明,我们的方法可以高效地预测工作流程任务的执行者,并且精度很高。

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