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
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
A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs
Data Mining and Knowledge Discovery
Detecting implicit dependencies between tasks from event logs
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
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
Process Mining: Overview and Outlook of Petri Net Discovery Algorithms
Transactions on Petri Nets and Other Models of Concurrency II
Towards workflow-driven database system workload modeling
Proceedings of the Second International Workshop on Testing Database Systems
Mining process models with prime invisible tasks
Data & Knowledge Engineering
Discovering block-structured process models from event logs - a constructive approach
PETRI NETS'13 Proceedings of the 34th international conference on Application and Theory of Petri Nets and Concurrency
Learning probabilistic real-time automata from multi-attribute event logs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
| Hi-index | 0.00 |
Most existing process mining algorithms have problems in dealing with invisible tasks. In this paper, a new process mining algorithm named α # is proposed, which extends the mining capacity of the classical α algorithm by supporting the detection of invisible tasks from event logs. Invisible tasks are first divided into four types according to their functional features, i.e., SIDE, SKIP, REDO and SWITCH. After that, the new ordering relation for detecting mendacious dependencies between tasks that reflects invisible tasks is introduced. Then the construction algorithms for invisible tasks of SIDE and SKIP/REDO/ SWITCH types are proposed respectively. Finally, the α # algorithm constructs the mined process models incorporating invisible tasks in WF-net. A lot of experiments are done to evaluate the mining quality of the proposed α # algorithm and the results are promising.