An Expert Lens on Data Quality in Process Mining

R. Andrews, Fahame F. Emamjome, A. T. Hofstede, H. Reijers
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引用次数: 2

Abstract

The success of a process mining project is highly dependent on the quality of the event log data, the degree to which quality issues are detected, and the way they are resolved. The detection and resolution of data quality issues requires a systematic approach that is aware of the organisational context in which event log data is created. To this end, the Odigos framework has been developed in prior work. The focus of this paper is the validation of this framework through semistructured interviews with a range of experts in process mining. The experts confirmed the utility of the framework, provided valuable insights into data quality in practical settings, and suggested enhancements to the Odigos framework.
过程挖掘中数据质量的专家视角
流程挖掘项目的成功高度依赖于事件日志数据的质量、检测质量问题的程度以及解决问题的方式。数据质量问题的检测和解决需要一种系统的方法,该方法了解创建事件日志数据的组织上下文。为此,在之前的工作中开发了Odigos框架。本文的重点是通过与一系列过程挖掘专家的半结构化访谈来验证该框架。专家们确认了该框架的实用性,对实际环境中的数据质量提供了有价值的见解,并提出了对Odigos框架的改进建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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