Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (9): 37-41.DOI: 10.3778/j.issn.1002-8331.1703-0012

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Optimization analysis of process mining based on quasi indirect dependence

CAO Rui, FANG Xianwen, WANG Lili   

  1. College of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, Anhui 232001, China
  • Online:2018-05-01 Published:2018-05-15


曹  蕊,方贤文,王丽丽   

  1. 安徽理工大学 数学与大数据学院,安徽 淮南 232001

Abstract: Business process mining aims to dig out the process model to meet the needs of people from the event logs. Most of the previous methods are based on the direct dependencies between events to build process models. Previous methods have some limitations. This paper proposes a method of optimization analysis of process mining based on quasi indirect dependency. Firstly, according to the event log, the initial model is constructed on the basis of behavior profile. Then, this paper finds the transition pairs with quasi indirect dependency through the basic constraint body of the ILP-based process discovery algorithm based on example event log. The model is improved and the optimized model is mined. Finally, a concrete example analysis is given to illustrate the effectiveness of the proposed method.

Key words: Petri net, behavior profiles, event log, process mining, quasi indirect dependence

摘要: 业务流程挖掘旨在从记录的事件日志中挖掘出满足人们需求的流程模型。以往的方法多是根据事件之间的直接依赖关系建立流程模型,具有一定的局限性,提出了基于拟间接依赖的流程挖掘优化分析方法。依据事件日志,以行为轮廓为基础,构建初始模型。在执行日志下,通过基于整数线性规划流程发现算法的基本约束体查找出具有拟间接依赖关系的变迁对,并对模型进行完善,挖掘出优化模型。通过具体的实例分析验证了该方法的有效性。

关键词: Petri网, 行为轮廓, 事件日志, 过程挖掘, 拟间接依赖关系