C4.5: programs for machine learning
C4.5: programs for machine learning
Predictive data mining: a practical guide
Predictive data mining: a practical guide
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
Sap R/3 Process Oriented Implementation
Sap R/3 Process Oriented Implementation
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Process Mining: Discovering Direct Successors in Process Logs
DS '02 Proceedings of the 5th International Conference on Discovery Science
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Mining invisible tasks from event logs
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Outlier detection techniques for process mining applications
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Process mining as first-order classification learning on logs with negative events
BPM'07 Proceedings of the 2007 international conference on Business process management
Mining process models with prime invisible tasks
Data & Knowledge Engineering
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Improving the understanding of BAM technology for real-time decision support
International Journal of Business Information Systems
Using graph aggregation for service interaction message correlation
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Projection approaches to process mining using region-based techniques
Data Mining and Knowledge Discovery
A Study of Quality and Accuracy Trade-offs in Process Mining
INFORMS Journal on Computing
Data & Knowledge Engineering
Improved artificial negative event generation to enhance process event logs
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
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Effective information systems require the existence of explicit process models. A completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that constructs the process model from process log data, by determining the relations between process tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data in a case study.