A theory and methodology of inductive learning
Readings in knowledge acquisition and learning
Software process validation: quantitatively measuring the correspondence of a process to a model
ACM Transactions on Software Engineering and Methodology (TOSEM)
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Business process mining: An industrial application
Information Systems
The prom framework: a new era in process mining tool support
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
| Hi-index | 0.00 |
Industrial, scientific, and commercial applications use information systems to trace the execution of a business process. Relevant events are registered in massive logs and process mining techniques are used to automatically discover knowledge that reveals the execution and organization of the process instances (cases). In this paper, we investigate the use of a multi-level relational frequent pattern discovery method as a means of process mining. In order to process such massive logs we resort to a Grid-based implementation of the knowledge discovery algorithm that distributes the computation on several nodes of a Grid platform. Experiments are performed on real event logs.