Domain experts in the loop: Leveraging generative artificial intelligence for interactive data validation in process mining

Process mining analyzes process execution data to derive insights that support operational process improvement. However, event logs often suffer from poor data quality, typically resulting from process deficiencies, which can lead to inaccurate or misleading insights. To mitigate this risk, domain experts and process analysts engage in data validation during event data preparation to assess whether an event log is fit for its intended analytical purpose. Yet, current practices often fail to sufficiently align event logs with their analytical objectives, commonly formalized as analysis questions. This misalignment impedes the detection of data quality issues, which frequently vary across application domains and analytical contexts. Generative artificial intelligence offers promising capabilities in this regard, including adaptability to diverse contexts, the ability to interpret complex data, and the generation of context-aware recommendations. To leverage this potential, we adopt the Design Science Research paradigm to iteratively develop Artificial Intelligence-Assisted Data Validation For Domain Experts (AID4DE) that integrates domain knowledge — rooted in experts’ practical engagement with operational processes — with generative artificial intelligence support to facilitate interaction with complex event log data. We instantiate AID4DE as an open-source software prototype and evaluate it through a three-phase approach: a competing artifact analysis, 14 semi-structured expert interviews, and a user study involving 18 information systems researchers. Our results show that AID4DE is both applicable and effective in supporting domain experts in data validation, enabling the systematic externalization of domain knowledge and rigorous assessment of event log’s fitness for purpose. • Improved data validation for domain experts through artificial intelligence support. • Artificial intelligence derives event log understanding from semantic visual analysis. • Contextual guidance improves experts’ understanding and validation of event log data. • Instantiated prototype evaluated as useful and applicable in a real-world setting. • Artificial intelligence and domain knowledge support fitness for purpose evaluation.