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Improving process models by discovering decision points

机译：通过发现决策点来改进流程模型

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摘要

Workflow management systems (WfMS) are widely used by business enterprises as tools for administrating, automating and scheduling the business process activities with the available resources. Since the control flow specifications of workflows are manually designed, they entail assumptions and errors, leading to inaccurate workflow models. Decision points, the XOR nodes in a workflow graph model, determine the path chosen toward completion of any process invocation. In this work, we show that positioning the decision points at their earliest points can improve process efficiency by decreasing their uncertainties and identifying redundant activities. We present novel techniques to discover the earliest positions by analyzing workflow logs and to transform the model graph. The experimental results show that the transformed model is more efficient with respect to its average execution time and uncertainty, when compared to the original model.
机译：工作流管理系统（WfMS）被企业广泛用作管理，自动化和安排具有可用资源的业务流程活动的工具。由于工作流的控制流规范是手动设计的，因此会带来假设和错误，从而导致工作流模型不准确。决策点（工作流图形模型中的XOR节点）确定为完成任何流程调用选择的路径。在这项工作中，我们表明将决策点放在最早的位置可以通过减少不确定性和识别多余的活动来提高流程效率。我们提出了新颖的技术来通过分析工作流程日志并转换模型图来发现最早的位置。实验结果表明，与原始模型相比，转换后的模型在平均执行时间和不确定性方面更为有效。

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