Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems
International Journal on Software Tools for Technology Transfer (STTT)
A Region-Based Algorithm for Discovering Petri Nets from Event Logs
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Decomposing process mining problems using passages
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
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Process-aware information systems (PAIS) enable developing models for interaction of processes, monitoring accuracy of their execution and checking if they interact with each other properly. PAIS can generate large data logs that contain information about the interaction of processes in time. Studying PAIS logs with the purpose of data mining and modeling lies within the scope of Process Mining. There is a number of tools developed for Process Mining, including the most ubiquitous ProM, whose functionality is extended by plugins. To perform an object-aware experiment one has to sequentially run multiple plugins. This process becomes extremely time-consuming in the case of large-scale experiments involving a large number of plugins. The paper proposes a concept of DPMine/P language of process modeling and analysis to be implemented in ProM. The language under development aims at joining separate stages of the experiment into a single sequence, that is an experiment model. The implementation of the basic semantics of the language is done through the concept of blocks, ports, connectors and schemes. These items are discussed in detail in the paper, and examples of their use for specific tasks are presented ibid.