Process Mining in Business Process Management
Summary
Process mining sits at the intersection of data science and business process management, offering systematic ways to derive process insights from the digital footprints of organisational activities. By extracting and analysing event logs from enterprise systems, process mining delivers three primary capabilities: discovery of process models, conformance checking against prescribed workflows, and enhancement through performance metrics. Its global significance lies in bridging the gap between theoretical process models and actual execution, thereby supporting data-driven decision-making, compliance monitoring and continuous optimisation. Use cases span manufacturing, finance, healthcare and public administration, where process mining fosters efficiency gains, transparency and the realisation of digital transformation strategies.
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Process Mining in Business Process Management publication trend
The graph below shows the total number of articles in process mining in business process management across all publications each year (not limited to Nature Index journals).
Technical terms
Process mining: A set of data-driven techniques for analysing business processes by extracting knowledge from event logs produced by information systems.
Business process management: A discipline focused on modelling, analysing and optimising end-to-end organisational processes to improve efficiency and compliance.
Event log: A chronological record of activities and associated data generated by an information system during process execution.
Process discovery: The task of reconstructing a process model from event log data without prior knowledge of its structure.
Conformance checking: The comparison of an existing process model against an event log to identify deviations and quantify compliance.
Trace encoding: The transformation of process traces into numerical feature vectors that capture control-flow and additional perspectives for analysis.
De jure model and de facto model: Prescribed process designs versus models derived from observed execution, used to identify workarounds.
References
- Process mining in mHealth data analysis. npj Digital Medicine (2024).
- Trace Encoding Techniques for Multi‐Perspective Process Mining: A Comparative Study. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2024).
- Using process mining for workarounds analysis in context: Learning from a small and medium-sized company case. International Journal of Information Management Data Insights (2023).
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