基于合成规则的进程发现排序策略比较
Comparing Ordering Strategies For Process Discovery Using Synthesis Rules
摘要：
过程发现的目的是从观察到的行为中学习过程模型，即，所发现的模型用作用于解决性能和遵从性问题的流程挖掘技术的起点。与最先进的归纳矿工相比，该算法应用来自自由选择网络理论的合成规则，发现具有更灵活（非块）结构的过程模型，同时确保相同的理想可靠性和自由选择属性。此外，这一领域的最新发展表明，所发现的模型具有兼容性。遵循合成规则，算法通过在事件日志中一次添加一个活动来增量地修改现有的过程模型。由于规则的应用高度依赖于现有的模型结构，模型的质量和计算时间显着影响的顺序添加活动。在本文中，我们研究了不同的排序策略对所发现的模型（w.r.t.适应度和精度）和使用真实事件数据的计算时间。结果表明，所提出的排序策略可以提高质量的过程模型，同时需要更少的时间相比，排序策略完全基于活动的频率。
英文摘要：
Process discovery aims to learn process models from observed behaviors, i.e., event logs, in the information systems.The discovered models serve as the starting point for process mining techniques that are used to address performance and compliance problems. Compared to the state-of-the-art Inductive Miner, the algorithm applying synthesis rules from the free-choice net theory discovers process models with more flexible (non-block) structures while ensuring the same desirable soundness and free-choiceness properties. Moreover, recent development in this line of work shows that the discovered models have compatible quality. Following the synthesis rules, the algorithm incrementally modifies an existing process model by adding the activities in the event log one at a time. As the applications of rules are highly dependent on the existing model structure, the model quality and computation time are significantly influenced by the order of adding activities. In this paper, we investigate the effect of different ordering strategies on the discovered models (w.r.t. fitness and precision) and the computation time using real-life event data. The results show that the proposed ordering strategy can improve the quality of the resulting process models while requiring less time compared to the ordering strategy solely based on the frequency of activities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
