Theme

 

The First International Workshop on Data Quality and Transformation in Process Mining (DQT-PM2022) aims to facilitate the exchange of research findings, ideas, and experiences on techniques and practices to data transformation and quality improvement at Stage 0 of a process mining project.

These days, the amount of available data is increased in organisations, so is its perceived value for stakeholders. A broad spectrum of process mining techniques (e.g., process discovery, conformance checking, and performance analysis) exists to derive actionable business insights from the recorded process data. As these process mining techniques rely on historical process data as ‘the single source of truth’, working with data that is of low and dubious quality poses significant hurdles to successfully translating data into actionable business insights.

It is also well-known that significant time and effort associated with process mining projects is being spent on data preparation tasks. A recent survey within the process mining community (XES) ‎[1] shows that more than 60% of the overall effort is spent on data preparation, where challenges such as complex data structures, incomplete, and inconstant data are being addressed. Current approaches to data preparation (e.g., data transformation, data quality auditing and remedies for repairs) are mostly ad-hoc and manual. Thus, there is a need for systematic and preferably automated approaches to event data transformation that will speed up the production of high-quality process data for decision-making purposes.