SAP R/3 business blueprint: understanding the business process reference model
SAP R/3 business blueprint: understanding the business process reference model
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Refactoring Process Models in Large Process Repositories
CAiSE '08 Proceedings of the 20th international conference on Advanced Information Systems Engineering
Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Data & Knowledge Engineering
Graph Matching Algorithms for Business Process Model Similarity Search
BPM '09 Proceedings of the 7th 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
Similarity of business process models: Metrics and evaluation
Information Systems
Behavioral similarity: a proper metric
BPM'11 Proceedings of the 9th international conference on Business process management
Design by selection: a reuse-based approach for business process modeling
ER'11 Proceedings of the 30th international conference on Conceptual modeling
When is nearest neighbors indexable?
ICDT'05 Proceedings of the 10th international conference on Database Theory
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
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With the increasing uptake of business process management efforts in companies, similarity search in large process model repositories has gained significance, as it forms a cornerstone of effective process model management and reuse. Similarity search uses a process model as query and retrieves all models, which resemble the query, in a ranked order. So far, the quality of the ranking has not been investigated. In this paper, we propose quality measures for similarity search results in order to address this problem, providing information on how good and how differentiated the results are. Our measures assess result statistics, which are derived from the similarity to the query model, and the agreement of different rankings, produced by diverse similarity measures. We apply our findings to a reference process model collection and comprehensively evaluate their prediction towards human assessment of process similarity.