Reliable Process Tracking Under Incomplete Event Logs Using Timed Genetic-Inductive Process Mining

Abstract

Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete event logs and the complexities of concurrent activities present significant challenges in achieving accurate process models that fulfill the completeness condition required in process mining. This paper introduces a Timed Genetic-Inductive Process Mining (TGIPM) algorithm, a novel approach that integrates the strengths of Timed Genetic Process Mining (TGPM) and Inductive Mining (IM). TGPM extends traditional Genetic Process Mining (GPM) by incorporating time-based analysis, while the IM is widely recognized for producing sound and precise process models. For the first time, these two algorithms are combined into a unified framework to address both missing activity recovery and structural correctness in process discovery. This study evaluates two scenarios: a sequential approach, in which TGPM and IM are executed independently and sequentially, and the TGIPM approach, where both algorithms are integrated into a unified framework. Experimental results using real-world event logs from a health service in Indonesia demonstrate that TGIPM achieves higher fitness, precision, and generalization compared to the sequential approach, while slightly compromising simplicity. Moreover, the TGIPM algorithm exhibits lower computational cost and more effectively captures parallelism, making it particularly suitable for large and incomplete datasets. This research underscores the potential of TGIPM to enhance process mining outcomes, offering a robust framework for accurate and efficient process discovery while driving process innovation across industries.

Keywords

• incomplete event log
• genetic process mining
• inductive mining
• process model
• parallel processes
• process mining