An Improvement of McMillan's Unfolding Algorithm
Formal Methods in System Design
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Querying and re-using workflows with VsTrails
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
WISE: A Workflow Information Search Engine
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
A workflow net similarity measure based on transition adjacency relations
Computers in Industry
Efficient and accurate retrieval of business process models through indexing
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Querying business process models based on semantics
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Efficient retrieval of similar business process models based on structure
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Querying large graph databases
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Efficient querying of large process model repositories
Computers in Industry
Querying business process model repositories
World Wide Web
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With the workflow technology being more widely used, there are more and more workflow models. How to retrieve the similar models efficiently from a large model repository is challenging. Since dynamic behavior is the essential characteristic of workflow models, we measure the similarity between models based on their behavior. Since the number of models is large, the efficiency of similarity retrieval is very important. To improve the efficiency of similarity retrieval based on behavior, we propose a more efficient algorithm for similarity calculation and use an index named TARIndex for query processing. To make our approach more applicable, we consider the semantic similarity between labels. Analysis and experiments show that our approach is efficient.