论文标题
有效地构造行为图的不确定事件数据
Efficient Construction of Behavior Graphs for Uncertain Event Data
论文作者
论文摘要
流程挖掘的学科涉及分析操作过程的执行数据，从事件数据中提取模型，检查事件数据和规范模型之间的符合性以及增强流程的所有方面。最近，已经开发了新技术来分析包含不确定性的事件数据。这些技术强烈依赖于通过捕获不确定性的基于图的模型来表示不确定的事件数据。在本文中，我们提出了一种新的方法，可以有效计算不确定过程轨迹中包含的行为的图表表示。我们介绍了新算法，分析其时间复杂性，并报告实验结果，显示了行为图构造的刻度性能提高。
The discipline of process mining deals with analyzing execution data of operational processes, extracting models from event data, checking the conformance between event data and normative models, and enhancing all aspects of processes. Recently, new techniques have been developed to analyze event data containing uncertainty; these techniques strongly rely on representing uncertain event data through graph-based models capturing uncertainty. In this paper we present a novel approach to efficiently compute a graph representation of the behavior contained in an uncertain process trace. We present our new algorithm, analyze its time complexity, and report experimental results showing order-of-magnitude performance improvements for behavior graph construction.
