Abstract
In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feature of event instances. In this paper, we take advantage of Petri nets as a powerful tool in modeling complex process behaviors considering time as an elemental variable. We propose an approach which starts from a Petri net process model constructed by a process mining algorithm. We enhance the Petri net model with time decay functions to create continuous process state samples. Finally, we use these samples in combination with discrete token movement counters and Petri net markings to train a deep learning model that predicts the next event. We demonstrate significant performance improvements and outperform the state-of-the-art methods on nine real-world benchmark event logs.
Highlights
With the ongoing development of digitizing and automatizing industries along with the steady increment of interconnected devices, we can project more interactions onto processes [1], [2]
Most recent advances are made in utilizing different deep learning architectures such as Long Short-Term Memory (LSTM) neural networks and stacked autoencoders [15]–[18]
Conformance is defined as the evaluation of the quality of a discovered process model, i.e. if it is a good representation of the process recorded by an event log
Summary
With the ongoing development of digitizing and automatizing industries along with the steady increment of interconnected devices, we can project more interactions onto processes [1], [2]. Most recent advances are made in utilizing different deep learning architectures such as Long Short-Term Memory (LSTM) neural networks and stacked autoencoders [15]–[18] These techniques do not discover process models at first, but perform their predictions on the raw event logs. We enhance the process model with time decay functions In this way, we can create continuous and timed state samples which we couple with process resources to train a neural network for the prediction of the event. We can create continuous and timed state samples which we couple with process resources to train a neural network for the prediction of the event We call this approach Decay Replay Mining - TrAnsition Prediction (DREAM-NAP).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
