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Inferring the Repetitive Behaviour from Event Logs for Process Mining Discovery

机译:从事件日志中推断重复行为以进行进程挖掘发现

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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.
机译:本文解决了从代表离散事件过程行为的事件跟踪中发现健全的工作流网(WFN)的问题。提出了一种新颖有效的方法来推断工作流日志中的重复行为。它基于对每个跟踪中计算出的循环进行迭代搜索和过滤。为每个周期建立因果关系图,这有助于找到扩展WFN的t不变量的支持。 t不变量用于确定事件之间的因果关系和并发关系,从而允许以完整的发现技术有效地构建WFN。

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