Quantifying and relating the completeness and diversity of process representations using species estimation

The analysis of process representations, such as event logs or process models, has become a staple in the context of business process management. Insights gained from such an analysis serve to monitor and improve the business processes that is captured. Yet, any process representation is merely a sample of the past and possible behaviour of a business process, which raises the question of its representativeness: To which extent does the process representation capture the process characteristics that are relevant for the analysis? In this paper, we propose to answer this question using estimators from biodiversity research. Specifically, we propose to infer a completeness profile based on the estimated number of distinct relevant characteristics of the process representation and a diversity profile, that captures the heterogeneity of relevant distinct characteristics using asymptotic Hill numbers. We validate the applicability of the proposed estimators for process analysis in a series of controlled experiments. Applying the estimators to real-world event logs, we highlight potential issues in terms of trustworthiness of analysis that is based on them, and show how the profiles can be leveraged to compare different process representations concerning their similarity and completeness.