Comparing Ordering Strategies for Process Discovery Using Synthesis Rules

Process discovery aims to learn process models from observed behaviors, i.e., event logs, in the information systems. The discovered models serve as the starting point for process mining techniques that are used to address performance and compliance problems. Compared to the state-of-the-art Inductive Miner, the algorithm applying synthesis rules from the free-choice net theory discovers process models with more flexible (non-block) structures while ensuring the same desirable soundness and free-choiceness properties. Moreover, recent development in this line of work shows that the discoveredmodels have compatible quality. Following the synthesis rules, the algorithm incrementally modifies an existing process model by adding the activities in the event log one at a time. As the applications of rules are highly dependent on the existing model structure, the model quality and computation time are significantly influenced by the order of adding activities. In this paper, we investigate the effect of different ordering strategies on the discovered models (w.r.t. fitness and precision) and the computation time using real-life event data. The results show that the proposed ordering strategy can improve the quality of the resulting process models while requiring less time compared to the ordering strategy solely based on the frequency of activities.

Automated summary

Process discovery aims to learn process models from observed behaviors in information systems. The discovered models serve as the starting point for process mining techniques to address performance and compliance problems. The algorithm applying synthesis rules from the free-choice net theory can discover process models with flexible structures while ensuring desirable properties. The proposed ordering strategy improves the quality of resulting process models and reduces computation time compared to frequency-based ordering strategy.