Measuring the Quality of Models with Respect to the Underlying System: An Empirical Study

Fitness and precision are two widely studied criteria to determine the quality of a discovered process model. These metrics measure how well a model represents the log from which it is learned. However, often the goal of discovery is not to represent the log, but the underlying system. This paper discusses the need to explicitly distinguish between a log and system perspective when interpreting the fitness and precision of a model. An empirical analysis was conducted to investigate whether the existing log-based fitness and precision measures are good estimators for system-based metrics. The analysis reveals that incompleteness and noisiness of event logs significantly impact fitness and precision measures. This makes them biased estimators of a model’s ability to represent the true underlying process.