Free choice Petri nets
Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
A Machine Learning Approach to Workflow Management
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
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
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Quantifying process equivalence based on observed behavior
Data & Knowledge Engineering
Conformance checking of service behavior
ACM Transactions on Internet Technology (TOIT)
Workflow Mining Application to Ambient Intelligence Behavior Modeling
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
Measuring the Compliance of Processes with Reference Models
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Fraud detection in process aware systems
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
Process discovery in event logs: An application in the telecom industry
Applied Soft Computing
Process equivalence: comparing two process models based on observed behavior
BPM'06 Proceedings of the 4th international conference on Business Process Management
Conformance testing: measuring the fit and appropriateness of event logs and process models
BPM'05 Proceedings of the Third international conference on Business Process Management
Multidimensional process mining: a flexible analysis approach for health services research
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. To overcome these problems, we try to use genetic algorithms to mine process models. The non-trivial constructs are tackled by choosing an internal representation that supports them. The noise problem is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The definition of a good fitness measure is the most critical challenge in a genetic approach. This paper presents the current status of our research and the pros and cons of the fitness measure that we used so far. Experiments show that the fitness measure leads to the mining of process models that can reproduce all the behavior in the log, but these mined models may also allow for extra behavior. In short, the current version of the genetic algorithm can already be used to mine process models, but future research is necessary to always ensure that the mined models do not allow for extra behavior. Thus, this paper also discusses some ideas for future research that could ensure that the mined models will always only reflect the behavior in the log.