An Expert Lens on Data Quality in Process Mining

被引：4

作者：

Andrews, Robert ^{[1
]}

Emamjome, Fahame ^{[1
]}

ter Hofstede, Arthur H. M. ^{[1
]}

Reijers, Hajo A. ^{[2
]}

机构：

[1] Queensland Univ Technol, Brisbane, Qld, Australia

[2] Univ Utrecht, Utrecht, Netherlands

来源：

2020 2ND INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2020) | 2020年

关键词：

process mining; data quality; Odigos framework; expert validation;

D O I：

10.1109/ICPM49681.2020.00018

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 semi-structured 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.

引用

页码：49 / 56

页数：8

共 20 条

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