Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Mining unconnected patterns in workflows
Information Systems
Conformance checking of processes based on monitoring real behavior
Information Systems
A fresh look at precision in process conformance
BPM'10 Proceedings of the 8th international conference on Business process management
Mining usage patterns from a repository of scientific workflows
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Decomposing process mining problems using passages
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
Repairing process models to reflect reality
BPM'12 Proceedings of the 10th international conference on Business Process Management
Where did i misbehave? diagnostic information in compliance checking
BPM'12 Proceedings of the 10th international conference on Business Process Management
Decomposing Petri nets for process mining: A generic approach
Distributed and Parallel Databases
Process Discovery and Conformance Checking Using Passages
Fundamenta Informaticae - Application and Theory of Petri Nets and Concurrency, 2012
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In the field of process mining, the goal is to automatically extract process models from event logs. Recently, many algorithms have been proposed for this task. For comparing these models, different quality measures have been proposed. Most of these measures, however, have several disadvantages; they are model-dependent, assume that the model that generated the log is known, or need negative examples of event sequences. In this paper we propose a new measure, based on the minimal description length principle, to evaluate the quality of process models that does not have these disadvantages. To illustrate the properties of the new measure we conduct experiments and discuss the trade-off between model complexity and compression.