Neural Approach to the Discovery Problem in Process Mining
Process mining deals with various types of formal models. Some of them are used at intermediate stages of synthesis and analysis, whereas others are the desired goals themselves. Transition systems (TS) are widely used in both scenarios. Process discovery, which is a special case of the synthesis problem, tries to find patterns in event logs. In this paper, we propose a new approach to the discovery problem based on recurrent neural networks (RNN). Here, an event log serves as a training sample for a neural network; the algorithm extracts RNN's internal state as the desired TS that describes the behavior present in the log. Models derived by the approach contain all behaviors from the event log (i.e. are perfectly fit) and vary in simplicity and precision, the key model quality metrics. One of the main advantages of the neural method is the natural ability to detect and merge common behavioral parts that are scattered across the log. The paper studies the proposed method, its properties and possible cases where the application of this approach is sensible as compared to other methods of TS synthesis.