Assessing State Spaces Using Petri-Net Synthesis and Attribute-Based Visualization

State spaces are commonly used representations of system behavior. A state space may be derived from a model of system behavior but can also be obtained through process mining. For a good understanding of the system's behavior, an analyst may need to assess the state space. Unfortunately, state spaces of realistic applications tend to be very large. This makes this assessment hard. In this paper, we tackle this problem by combining Petri-net synthesis (i.e., regions theory) and visualization. Using Petri-net synthesis we generate the attributes needed for attribute-based visualization. Using visualization we can assess the state space. We demonstrate that such an approach is possible and describe our implementation using existing tools. The only limiting factor of our approach is the performance of current synthesis techniques.