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
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
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Discovering models of behavior for concurrent workflows
Computers in Industry - Special issue: Process/workflow mining
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Mining process models with non-free-choice constructs
Data Mining and Knowledge Discovery
A change detection method for sequential patterns
Decision Support Systems
A comparative study of dimensionality reduction techniques to enhance trace clustering performances
Expert Systems with Applications: An International Journal
Mining non-redundant time-gap sequential patterns
Applied Intelligence
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A process management technique, called process mining, received much attention recently. Process mining can extract organizational or social structures from event logs recorded in an information system. However, when constructing process models, most process mining searches consider only the topology information among events, but do not include the time information. To overcome the drawbacks, a time-interval genetic process mining framework is proposed. First, time-intervals between events are derived for all event sequences. A discretization procedure is then developed to transform time-interval data from continues type to categorical type. Second, the genetic process mining method which is based on global search strategy is applied to generate time-interval process models. Finally, a precision measure is defined to evaluate the quality of the generated models. With the measure, managers can select the best process model among a set of candidate models without human involvement.