Quality-Informed Semi-Automated Event Log Generation for Process Mining

Title data

Andrews, Robert ; van Dun, Christopher ; Wynn, Moe T. ; Kratsch, Wolfgang ; Röglinger, Maximilian ; ter Hofstede, Arthur H. M.:
Quality-Informed Semi-Automated Event Log Generation for Process Mining.
In: Decision Support Systems. Vol. 132 (2020) . - 113265.
ISSN 1873-5797
DOI: https://doi.org/10.1016/j.dss.2020.113265

Project information

Abstract in another language

Process mining, as any form of data analysis, relies heavily on the quality ofinput data to generate accurate and reliable results. A fit-for-purpose event lognearly always requires time-consuming, manual pre-processing to extract eventsfrom source data, with data quality dependent on the analyst’s domain knowledgeand skills. Despite much being written about data quality in general, ageneralisable framework for analysing event data quality issues when extractinglogs for process mining remains unrealised. Following the DSR paradigm,we present RDB2Log, a quality-aware, semi-automated approach for extractingevent logs from relational data. We validated RDB2Log’s design against designobjectives extracted from literature and competing artifacts, evaluated itsdesign and performance with process mining experts, implemented a prototypewith a defined set of quality metrics, and applied it in laboratory settings and ina real-world case study. The evaluation shows that RDB2Log is understandable,of relevance in current research, and supports process mining in practice.