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Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes

机译:通过跟踪聚类和文本挖掘来利用过程发现来对事件管理过程进行智能分析

摘要

Recent years have witnessed the ability to gatheran enormous amount of data in a large number of domains.Also in the field of business process management, there exists an urgent need to beneficially use these data to retrieve actionable knowledge about the actual way of working in the context of a certain business process. The research field concerned is process mining, which can be defined as a whole family of analysis techniques for extracting knowledge from information system event logs. In this paper, we present a solution strategy to leveragetraditional process discovery techniques in the flexible environment of incident management processes. In such environments, it is typically observed that single model discovery techniques are incapable of dealing with the large number of different types of execution traces. Accordingly, we propose a combination of trace clustering and text mining to enhance process discovery techniques with the purpose of retrieving more useful insights from process data.
机译:近年来,见证了在大量域中收集大量数据的能力。此外,在业务流程管理领域,迫切需要有效地使用这些数据来检索有关实际工作方式的可行知识。特定业务流程的上下文。有关的研究领域是过程挖掘,可以定义为一整套分析技术,用于从信息系统事件日志中提取知识。在本文中,我们提出了在事件管理流程的灵活环境中利用传统流程发现技术的解决方案策略。在这样的环境中,通常会观察到单一模型发现技术无法处理大量不同类型的执行跟踪。因此,我们提出了跟踪聚类和文本挖掘相结合的方法,以增强过程发现技术,从而从过程数据中检索更多有用的见解。

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