WordNet: a lexical database for English
Communications of the ACM
A vector space model for automatic indexing
Communications of the ACM
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The refined process structure tree
Data & Knowledge Engineering
Aligning Business Process Models
EDOC '09 Proceedings of the 2009 IEEE International Enterprise Distributed Object Computing Conference (edoc 2009)
Alternative Approaches for Workflow Similarity
SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
The ICoP Framework: identification of correspondences between process models
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Similarity of business process models: Metrics and evaluation
Information Systems
Schema Matching and Mapping
Behavioral similarity: a proper metric
BPM'11 Proceedings of the 9th international conference on Business process management
A comparative survey of business process similarity measures
Computers in Industry
BPEL processes matchmaking for service discovery
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
On the refactoring of activity labels in business process models
Information Systems
Matching business process workflows across abstraction levels
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
Approximate clone detection in repositories of business process models
BPM'12 Proceedings of the 10th international conference on Business Process Management
Probabilistic optimization of semantic process model matching
BPM'12 Proceedings of the 10th international conference on Business Process Management
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Process model matching refers to the task of creating correspondences among activities of different process models. This task is crucial whenever comparison and alignment of process models are called for. In recent years, there have been a few attempts to tackle process model matching. Yet, evaluating the obtained sets of correspondences reveals high variability in the results. Addressing this issue, we propose a method for predicting the quality of results derived by process model matchers. As such, prediction serves as a case-by-case decision making tool in estimating the amount of trust one should put into automatic matching. This paper proposes a model of prediction for process matching based on both process properties and preliminary match results.