Abstract
The robotic process mining focuses on the analysis of historical process sequences in order to build up a process model for the investigated field. One of the main tasks in robotic process mining is the construction of process schema for the input sequences. Usual methods are able to generate models using only baseline graph structures. In order to support high level structures like parallelism, the input event sequence structure must support additional attributes on the events. This paper presents a novel approach on sequence segmentation providing an intermediate graph structure which can be used to mine complex graph patterns. The tested prototype system contains a Python-based implementation of the proposed algorithm. In the paper, some tests are shown to illustrate the suitability of the proposed model.
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