Inferring the Repetitive Behaviour from Event Logs for Process Mining Discovery

被引:1
作者
Tapia-Flores, Tonatiuh [1 ]
Lopez-Mellado, Ernesto [1 ]
机构
[1] CINVESTAV Unidad Guadalajara, Av Bosque 1145, Col El Bajio 45015, Zapopan, Mexico
来源
MINING INTELLIGENCE AND KNOWLEDGE EXPLORATION (MIKE 2016) | 2017年 / 10089卷
关键词
Process mining; Petri nets discovery; t-invariants; PROCESS MODELS; IDENTIFICATION;
D O I
10.1007/978-3-319-58130-9_16
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper addresses the problem of discovering a sound Workflow net (WFN) from event traces representing the behavior of a discrete event process. A novel and efficient method for inferring the repetitive behaviour in a workflow log is proposed. It is based on an iterative search and filtering of cycles computed in each trace; a graph of causal relations is built for each cycle, which helps to find the supports of the t-invariants of an extended WFN. The t-invariants are used for determining causal and concurrent relations between events, allowing building the WFN efficiently in a complete discovery technique.
引用
收藏
页码:164 / 173
页数:10
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