Transition-driven time prediction for business processes with cycles

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Business process time prediction aims to predict the time of the running process instance, including the elapsed time and the predicted remaining time. Existing time prediction methods rarely take the cycles of business process into consideration. However, cycles of business process are one of the main factors affecting the accuracy of time prediction. To address this issue, a new transition-driven time prediction for business processes with cycles is proposed. First of all, on the one hand, a Petri net from the event log is mined to obtain the reachability graph, and then the transition division sequence is obtained from the reachability graph of a Petri net. On the other hand, the prefixes are generated by the event log, then, the prefix is feature-encoded and the corresponding business process time is calculated. Second, all prefixes are partitioned according to the activity of the last event of the prefix based on the transition division sequence. Finally, different autoencoders are applied to different transition divisions to reduce dimensionality, and transfer learning is performed by different deep neural networks. Furthermore, by extensive experimental evaluation using the publicly available synthetic event logs and real-life event logs, we show that the proposed method outperforms existing baseline methods.