Uncertainty in Process Mining: Survey and Conceptual Framework

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Abstract
Process mining analyzes processes based on event logs, which are generally assumed to be complete and trustworthy. These assumptions are often violated in real-world contexts, raising uncertainty about result reliability. Uncertainty-aware process mining promises to address this by explicitly leveraging uncertainty in calculations. We perform a structured survey of uncertainty-aware process mining (34 papers), which illustrates the currently dominant conceptualization of uncertainty in process mining and reveals the need for a conceptual foundation and integration with related research that handles uncertainty implicitly (e.g., process data quality). To address this, we construct an integrative conceptual framework for uncertainty in process mining by extending the core survey findings through an iterative hermeneutic review of seminal works from related research. The framework consists of two complementary lenses, detailing the (i) possible sources of and (ii) viable treatment strategies for uncertainty. Application of the framework shows both its integrative power for (future) research and its utility for guiding the handling of uncertainty in practical application.
Funding Information
  • Schleswig-Holstein (EIP-AGRI OG ProcessPig)

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