Graph-based hierarchical conceptual clustering
The Journal of Machine Learning Research
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Workflow Similarity Measure for Process Clustering in Grid
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Hierarchical Business Process Clustering
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Future Generation Computer Systems
Using minimum description length for process mining
Proceedings of the 2009 ACM symposium on Applied Computing
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process mining by measuring process block similarity
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
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In many experimental domains, especially e-Science, workflow management systems are gaining increasing attention to design and execute in-silico experiments involving data analysis tools. As a by-product, a repository of workflows is generated, that formally describes experimental protocols and the way different tools are combined inside experiments. In this paper we describe the use of the SUBDUE graph clustering algorithm to discover sub-workflows from a repository. Since sub-workflows represent significant usage patterns of tools, the discovered knowledge can be exploited by scientists to learn by-example about design practices, or to retrieve and reuse workflows. Such a knowledge, ultimately, leverages the potential of scientific workflow repositories to become a knowledge-asset. A set of experiments is conducted on the my Experiment repository to assess the effectiveness of the approach.