Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Event-based detection of concurrency
SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
Distributed and Parallel Databases
A Machine Learning Approach to Workflow Management
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Discovering Workflow Performance Models from Timed Logs
EDCIS '02 Proceedings of the First International Conference on Engineering and Deployment of Cooperative Information Systems
Process Miner - A Tool for Mining Process Schemes from Event-Based Data
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Computers in Industry - Special issue: Process/workflow mining
Computers in Industry - Special issue: Process/workflow mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Towards mining structural workflow patterns
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Mining workflow recovery from event based logs
BPM'05 Proceedings of the 3rd international conference on Business Process Management
Log-based transactional workflow mining
Distributed and Parallel Databases
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Engineering workflow applications are becoming more and more complex, involving numerous interacting business objects within considerable processes. Analysing the interaction structure of those complex applications will enable them to be well understood, controlled, and redesigned. Our contribution to workflow mining is a statistical technique to discover workflow patterns from event-based log. Our approach is characterised by a ”local” workflow patterns discovery that allows to cover partial results through a dynamic programming algorithm. Those local discovered workflow patterns are then composed iteratively until discovering the global workflow model. Our approach has been implemented within our prototype WorkflowMiner.