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
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Structural Patterns for Soundness of Business Process Models
EDOC '06 Proceedings of the 10th IEEE International Enterprise Distributed Object Computing Conference
Conformance checking of processes based on monitoring real behavior
Information Systems
Quantifying process equivalence based on observed behavior
Data & Knowledge Engineering
Process Discovery Using Integer Linear Programming
PETRI NETS '08 Proceedings of the 29th international conference on Applications and Theory of Petri Nets
A Region-Based Algorithm for Discovering Petri Nets from Event Logs
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Using minimum description length for process mining
Proceedings of the 2009 ACM symposium on Applied Computing
Process-Aware Information Systems: Lessons to Be Learned from Process Mining
Transactions on Petri Nets and Other Models of Concurrency II
Process mining based on regions of languages
BPM'07 Proceedings of the 5th international conference on Business process management
The need for a process mining evaluation framework in research and practice: position paper
BPM'07 Proceedings of the 2007 international conference on Business process management
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
Process mining from a basis of state regions
PETRI NETS'10 Proceedings of the 31st international conference on Applications and Theory of Petri Nets
Simplifying mined process models: an approach based on unfoldings
BPM'11 Proceedings of the 9th international conference on Business process management
Transactions on Petri Nets and Other Models of Concurrency V
Distributed process discovery and conformance checking
FASE'12 Proceedings of the 15th international conference on Fundamental Approaches to Software Engineering
Replaying history on process models for conformance checking and performance analysis
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Applying process analysis to the italian egovernment enterprise architecture
WS-FM'11 Proceedings of the 8th international conference on Web Services and Formal Methods
An SMT-Based discovery algorithm for c-nets
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
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
Simplifying discovered process models in a controlled manner
Information Systems
Amending C-net discovery algorithms
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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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Process Conformance is a crucial step in the area of Process Mining: the adequacy of a model derived from applying a discovery algorithm to a log must be certified before making further decisions that affect the system under consideration. Among the different conformance dimensions, in this paper we propose a novel measure for precision, based on the simple idea of counting these situations were the model deviates from the log. Moreover, a log-based traversal of the model that avoids inspecting its whole behavior is presented. Experimental results show a significant improvement when compared to current approaches for the same task. Finally, the detection of the shortest traces in the model that lead to discrepancies is presented.