论文标题
使用抽样策略来协助共识序列分析
Using Sampling Strategy to Assist Consensus Sequence Analysis
论文作者
论文摘要
事件日志的共识序列通常用于过程挖掘中，以快速掌握在过程中要执行的事件的核心序列，或代表进行其他分析的过程的骨干。但是，尚不清楚多少迹线足以正确地表示基础过程。在本文中，我们提出了一种新型的抽样策略，以确定产生代表性共识序列所需的痕迹数量。我们展示了如何估计预定义的专家模型与进行的实际过程之间的差异。该差异级别可用作域专家调整专家模型的参考。此外，我们将此策略应用于案例研究，将此策略应用于几个现实世界中的工作流活动数据集。我们展示了样本曲线拟合任务，以帮助读者更好地了解我们提出的方法。
Consensus Sequences of event logs are often used in process mining to quickly grasp the core sequence of events to be performed in a process, or to represent the backbone of the process for doing other analyses. However, it is still not clear how many traces are enough to properly represent the underlying process. In this paper, we propose a novel sampling strategy to determine the number of traces necessary to produce a representative consensus sequence. We show how to estimate the difference between the predefined Expert Model and the real processes carried out. This difference level can be used as reference for domain experts to adjust the Expert Model. In addition, we apply this strategy to several real-world workflow activity datasets as a case study. We show a sample curve fitting task to help readers better understand our proposed methodology.
