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
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
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
Process mining for ubiquitous mobile systems: an overview and a concrete algorithm
UMICS'04 Proceedings of the Second CAiSE conference on Ubiquitous Mobile Information and Collaboration Systems
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To understand process executed in many activities, process mining technologies are now extensively studied. However, three major problems in the current process mining techniques are identified. First, most process mining techniques mainly use local search strategy to generate process models. Second, time intervals between two actives are not considered so that patterns that are different in view of time are regarded as the same behaviors. Third, no precision evaluation measure is defined to evaluate the quality of process models. To solve these difficulties, this research proposes a time-interval process mining method. A genetic process mining algorithm with time-interval consideration is developed. Then, a precision evaluation measure is defined to evaluate the quality of the generated process models. Finally, the best process model with highest precision value is reported.