Complex Control-Flow Constructs Detection from Process Related Data

Hind R’bigui, Chiwoon Cho
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引用次数: 2

Abstract

Process mining is new techniques whereby knowledge from event log stored in today’s information systems are extracted to automatically construct business process models to have a full understanding of the real behaviour of processes, identify bottlenecks, and then improve them. Many process discovery algorithms have been proposed today. However, there are complex control-flow constructs that current discovery techniques cannot correctly discover in models based on event logs. These constructs are short loops, invisible tasks, and nonfree choice constructs. There is currently no algorithm that can handle all of these structures in a restricted time. In this paper, we propose a framework that detects from event logs the complex control-flow constructs that exist. By identifying the existing constructs from a given event log, one can identify the process model notation or the process discovery algorithm appropriate for the given event log. The framework has been implemented in ProM and the results show that constructs are identified correctly.
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复杂控制流构造从过程相关数据检测
流程挖掘是一种新技术,通过从存储在当今信息系统中的事件日志中提取知识,自动构建业务流程模型,从而全面了解流程的真实行为,识别瓶颈,然后改进它们。目前已经提出了许多过程发现算法。然而,当前的发现技术无法在基于事件日志的模型中正确地发现复杂的控制流构造。这些结构是短循环、不可见任务和非自由选择结构。目前还没有一种算法可以在有限的时间内处理所有这些结构。在本文中,我们提出了一个从事件日志中检测存在的复杂控制流结构的框架。通过标识给定事件日志中的现有构造,可以标识适合于给定事件日志的流程模型符号或流程发现算法。在ProM中实现了该框架,结果表明该框架能够正确识别结构。
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