通过声明性规则检测过程变体之间的统计显著差异
Detection of statistically significant differences between process variants through declarative rules
AI总结：
本文提出通过声明性规则检测过程变体之间的统计显著差异，展示了其在表达性和执行效率上的优势。
摘要：
服务和产品通常通过根据上下文、需求或定制需求变化的过程执行来提供。对这类过程变体的分析可以突出服务结果或质量的差异，从而导致过程调整和改进。过程挖掘领域已提供了一些过程变体分析方法。然而，非常少有这些方法考虑了其输出的统计显著性分析。此外，这些技术在过程轨迹、单个活动或性能层面检测差异。在本文中，我们旨在通过声明性过程规则的形式描述变体之间的独特行为特征。本研究的贡献是双方面的：使用声明性规则来解释过程变体，并对结果进行统计显著性分析。我们通过将结果与最新过程变体分析方法进行比较来评估所提出的方法。我们的结果不仅表明声明性规则在表达性层面揭示差异，而且我们的方法在执行时间方面也优于现有技术。
英文摘要：
Services and products are often offered via the execution of processes that vary according to the context, requirements, or customisation needs. The analysis of such process variants can highlight differences in the service outcome or quality, leading to process adjustments and improvement. Research in the area of process mining has provided several methods for process variants analysis. However, very few of those account for a statistical significance analysis of their output. Moreover, those techniques detect differences at the level of process traces, single activities, or performance. In this paper, we aim at describing the distinctive behavioural characteristics between variants expressed in the form of declarative process rules. The contribution to the research area is two-pronged: the use of declarative rules for the explanation of the process variants and the statistical significance analysis of the outcome. We assess the proposed method by comparing its results to the most recent process variants analysis methods. Our results demonstrate not only that declarative rules reveal differences at an unprecedented level of expressiveness, but also that our method outperforms the state of the art in terms of execution time.
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