In complex processes, various events can happen in different sequences. The prediction of the next event activity given an a-priori process state is of importance in such processes. Recent methods leverage deep learning techniques such as recurrent neural networks to predict event activities from raw process logs. However, deep learning techniques cannot efficiently model logical behaviors of complex processes. In this paper, we take advantage of Petri nets as a powerful tool in modeling logical behaviors of complex processes. We propose an approach which first discovers Petri nets from event logs utilizing a recent process mining algorithm. In a second step, we enhance the obtained model with time decay functions to create timed process state samples. Finally, we use these samples in combination with token movement counters and Petri net markings to train a deep learning model that predicts the next event activity. We demonstrate significant performance improvements and outperform the state-of-the-art methods on eight out of nine real-world benchmark event logs in accuracy.
Process event prediction is the prediction of various properties of the remaining path of a process sequence or workflow. The prediction is based on the data extracted from a combination of historical (closed) and or live (open) workflows (jobs or process instances). In real-world applications, the problem is compounded by the fact that the number of unique workflows (process prototypes) can be enormous, their occurrences can be limited, and a real process may deviate from the designed process when executed in real environment and under realistic constraints. It is necessary for an efficient predictor to be able to cope with the diverse characteristics of the data.We also have to ensure that useful process data is collected to build the appropriate predictive model. In this paper we propose an extension of Markov models for predicting the next step in a process instance.We have shown, via a set of experiments, that our model offers better results when compared to methods based on random guess, Markov models and Hidden Markov models. The data for our experiments comes from a real live process in a major telecommunication company.