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Mining decision to discover the relation of rules among decision points in a non-free choice construct

机译:挖掘决策以发现非自由选择构造中决策点之间的规则关系

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Decision mining is a combination of process mining and machine learning algorithms to retrieve information on how data attributes in a business process affect routing of a case. It analyzes decision point by looking for XOR-splits in petri-net workflow model and examining rules for each choice based on available attributes using decision tree. The rules for each decision point are based on the attribute's influence to the case. Meanwhile, a non-free choice construct is a mixture of choice and synchronization, which will create limited choices in the workflow. The limitation of choice will then affect the rules found in non-free choice construct using decision mining technique. Limitation of rules makes it possible to examine the relation among rules in the workflow. The relation of these rules will emerge a certain property of a non-free choice construct. Rules for two decision points within a non-free choice construct will have similarities. Regarding to this, when the same rule is found during a decision mining process, we can determine that the decision points have a non-free choice relationship.
机译:决策挖掘是流程挖掘和机器学习算法的组合,用于检索有关业务流程中的数据属性如何影响案例路由的信息。它通过在petri-net工作流模型中查找XOR拆分并使用决策树基于可用属性检查每个选择的规则来分析决策点。每个决策点的规则都是基于属性对案例的影响。同时,非自由选择结构混合了选择和同步,这将在工作流中创建有限的选择。然后,选择的局限性将影响使用决策挖掘技术在非自由选择构造中找到的规则。规则的限制使检查工作流中规则之间的关系成为可能。这些规则之间的关系将成为非自由选择结构的某些属性。非自由选择构造中两个决策点的规则将具有相似性。关于这一点,当在决策挖掘过程中找到相同的规则时,我们可以确定决策点具有非自由选择关系。

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