The program structure tree: computing control regions in linear time
PLDI '94 Proceedings of the ACM SIGPLAN 1994 conference on Programming language design and implementation
UMLDiff: an algorithm for object-oriented design differencing
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Measuring similarity between semantic business process models
APCCM '07 Proceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 67
Change Distilling: Tree Differencing for Fine-Grained Source Code Change Extraction
IEEE Transactions on Software Engineering
A Classification of Differences between Similar BusinessProcesses
EDOC '07 Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference
Faster and More Focused Control-Flow Analysis for Business Process Models Through SESE Decomposition
ICSOC '07 Proceedings of the 5th international conference on Service-Oriented Computing
The Refined Process Structure Tree
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Aligning Business Process Models
EDOC '09 Proceedings of the 2009 IEEE International Enterprise Distributed Object Computing Conference (edoc 2009)
Detection of Semantically Equivalent Fragments for Business Process Model Change Management
SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
Deciding behaviour compatibility of complex correspondences between process models
BPM'10 Proceedings of the 8th international conference on Business process management
The ICoP Framework: identification of correspondences between process models
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Simplified computation and generalization of the refined process structure tree
WS-FM'10 Proceedings of the 7th international conference on Web services and formal methods
SOCA '11 Proceedings of the 2011 IEEE International Conference on Service-Oriented Computing and Applications
A flexible approach for abstracting and personalizing large business process models
ACM SIGAPP Applied Computing Review
Data flow abstractions and adaptations through updatable process views
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Supporting different process views through a shared process model
ECMFA'13 Proceedings of the 9th European conference on Modelling Foundations and Applications
Predicting the quality of process model matching
BPM'13 Proceedings of the 11th international conference on Business Process Management
Increasing recall of process model matching by improved activity label matching
BPM'13 Proceedings of the 11th international conference on Business Process Management
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In Business Process Modeling, several models are defined for the same system, supporting the transition from business requirements to IT implementations. Each of these models targets a different abstraction level and stakeholder perspective. In order to maintain consistency among these models, which has become a major challenge not only in this field, the correspondence between them has to be identified. A correspondence between process models establishes which activities in one model correspond to which activities in another model. This paper presents an algorithm for determining such correspondences. The algorithm is based on an empirical study of process models at a large company in the banking sector, which revealed frequent correspondence patterns between models spanning multiple abstraction levels. The algorithm has two phases, first establishing correspondences based on similarity of model element attributes such as types and names and then refining the result based on the structure of the models. Compared to previous work, our algorithm can recover complex correspondences relating whole process fragments rather than just individual activities. We evaluate the algorithm on 26 pairs of business-technical and technical-IT level models from four real-world projects, achieving overall precision of 93% and recall of 70%. Given the substantial recall and the high precision, the algorithm helps automating significant part of the correspondence recovery for such models.