Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Conformance checking of processes based on monitoring real behavior
Information Systems
Modularity in Process Models: Review and Effects
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Reasoning on Semantically Annotated Processes
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
Optimizing the trade-off between complexity and conformance in process reduction
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
A discourse on complexity of process models
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
ICST '12 Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation
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Process models play a key role in taking decisions when existing procedures and systems need to be changed and improved. However, these models are often not available or not aligned with the actual process implementation. In these cases, process model recovery techniques can be applied to analyze the existing system implementation and capture the underlying business process models. Several techniques have been proposed in the literature to recover business processes, although the resulting processes are often complex, intricate and thus difficult to understand for business analysts. In this paper, we propose a process reduction technique based on multi-objective optimization, which minimizes at the same time process complexity, non-conformances, and loss of business content. This allows us to improve the process model understandability by decreasing its structural complexity, while preserving the completeness of the described business and domain-specific information. We conducted a case study based on a real-life e-commerce system. Results indicate that by balancing complexity, conformance and business content our technique produces understandable and meaningful reduced process models.