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.

The analysis of event logs 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 generated the logs. Yet, any event log is merely a sample of the past and possible behaviour of a business process, which raises the question of log representativeness: To which extent does the log capture the characteristics of the process that are relevant for the analysis? In this paper, we propose to answer this question using estimators from biodiversity research. Interpreting log representativeness as the completeness regarding distinct properties of a process, we show how to estimate the number of properties often leveraged in process mining in some unknown population. Applying the estimators to real-world event logs, we highlight potential issues in terms of result trustworthiness, also attributing these issues to particular parts of a process.