Gradual Process Discovery: Conceptual Initiation, Framework, System, and Experimental Validation

This paper presents a novel discovery approach in process mining methodology that facilitates the incremental derivation of a series of Structured Information Control Net (SICN) process models. Each model is discovered from selectively grouped temporal traces within large-scale and fragmented process log datasets, enabling its effective and efficient application in the life-cycle management of the corresponding original process model. By conceptualizing the framework and implementing its system, the paper establishes a practical foundation for the proposed approach and ensures that gradual discovery is executed while preserving complete control-flow structures across selected process event log trace groups. Grounded in the SICN process modeling methodology, this approach is further supported by the development of a novel gradual [Formula: see text]-Algorithm, an extension of the original [Formula: see text]-Algorithm. The original [Formula: see text]-Algorithm is a validated process mining technique designed to extract SICN process models from datasets of process enactment event logs and histories. The proposed approach is particularly advantageous for managing the life cycles of complex and large-scale business process models, coping with offering both scalability and efficiency. Conclusively, the primary objective of the paper is to discover and visualize a series of SICN process models, each of which is gradually discovered from selectively chosen temporal trace-groups within large-scale and fragmented process log datasets and completely and safely (matched pairing and proper nesting properties) constructed from combinational building blocks derived from four fundamental process patterns, such as linear (sequential), disjunctive (exclusive-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP). To validate the conceptual robustness and functional accuracy of the proposed approach and system, three experiments are conducted. These experiments assess the approach using synthetic non-noisy datasets as well as real-world datasets, all of which are publicly available through the 4TU.ResearchData repository. The datasets utilized adhere to the standardized XES (eXtensible Event Stream) data format established by IEEE.