Time Efficient End-State Prediction Through Hybrid Trace Decomposition Using Process Mining

In a real-world environment, business processes are complex and challenging to monitor for potential irregularities. Service-oriented organisations rely primarily on the effectiveness of their processes to maintain the quality of their offered services and prefer to be notified in advance if an ongoing business process is not executing as expected. Process mining techniques analyse business processes, but heterogeneity & variability in the real-world event logs make it challenging and time-consuming to mine using standard methods. Trace clustering divides event logs into sub-logs with similar properties based on the case and event attributes. However, given the size of the event logs collected from heterogeneous information systems, it is difficult to identify meaningful clusters in actual business process logs. This paper proposes a hybrid technique to decompose large event logs into several smaller sub-logs, making it easier and more time efficient to analyse. Each sub-log is independently investigated to gain valuable insights into the process's underlying behaviour. Non-traditional process mining techniques are applied to identify possible behavioural correlations and discrepancies between simulated and observed process execution by extracting features from the log segment. We used a real-world case study to depict our framework's usefulness in prediction accuracy and timeliness. Furthermore, we compared our findings to those of previously proposed methodologies from the literature. We demonstrated that the framework improved the fitness quality of the resultant business process models.