Comparing Ordering Strategies For Process Discovery Using Synthesis Rules
Tsung-Hao Huang, Wil M. P. van der Aalst

TL;DR
This paper evaluates how different activity ordering strategies affect the quality and efficiency of process models discovered through synthesis rules, demonstrating improvements over frequency-based strategies.
Contribution
It introduces and assesses new ordering strategies for process discovery that enhance model quality and reduce computation time compared to existing methods.
Findings
Proposed ordering strategies improve model fitness and precision.
New strategies reduce computation time compared to frequency-based ordering.
Models discovered with new strategies maintain soundness and free-choiceness.
Abstract
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 discovered models 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…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Semantic Web and Ontologies · Service-Oriented Architecture and Web Services
