论文标题
块结构工作流网的语言保存减少规则
Language-Preserving Reduction Rules for Block-Structured Workflow Nets
论文作者
论文摘要
人类分析师使用过程模型来对行为进行建模和分析，并由机器验证诸如健全性，可观性或其他可及性属性等属性，并将其表达的行为与组织业务过程中的记录行为进行比较。对于人类和机器的使用，小型模型比大型且复杂的模型更可取：为了易于人类的理解并减少机器在州空间探索中所花费的时间。为培养皿网定义了保留模型行为的还原规则，但是在本文中，我们表明，可以通过考虑其过程树中的块结构来进一步降低，即通过过程发现技术返回的培养皿的子类。我们重新审视了过程树的现有减少规则，并表明规则是正确的，终止，汇合和完整的，并且它们既不完整又不完整。在现实生活实验中，我们表明这些规则可以减少从现实事件日志中发现的过程模型与仅考虑Petri Net结构的规则相比。
Process models are used by human analysts to model and analyse behaviour, and by machines to verify properties such as soundness, liveness or other reachability properties, and to compare their expressed behaviour with recorded behaviour within business processes of organisations. For both human and machine use, small models are preferable over large and complex models: for ease of human understanding and to reduce the time spent by machines in state space explorations. Reduction rules that preserve the behaviour of models have been defined for Petri nets, however in this paper we show that a subclass of Petri nets returned by process discovery techniques, that is, block-structured workflow nets, can be further reduced by considering their block structure in process trees. We revisit an existing set of reduction rules for process trees and show that the rules are correct, terminating, confluent and complete, and for which classes of process trees they are and are not complete. In a real-life experiment, we show that these rules can reduce process models discovered from real-life event logs further compared with rules that consider only Petri net structures.
