On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Graph Matching Algorithms for Business Process Model Similarity Search
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Similarity of business process models: Metrics and evaluation
Information Systems
FlowRecommender: a workflow recommendation technique for process provenance
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
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How to improve the modeling efficiency and accuracy has become a burning problem. The popularization of recommendation technique in E-Commerce provide us new trajectories that can be used for addressing the problem. In this paper, we propose a graph-based workflow recommendation for improving business process modeling. The start point is so-called "workflow repository" including a set of already developed process models. Graph mining method is used to extract the process patterns from the repository. Based on graph edit distance (GED) [2], we calculate the distance between patterns and the partial business process, viewed as reference model, which is under modeling and select the candidate nodes with smaller distances for recommendation. The performance study show its feasibility for practical uses.