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.

Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders. To this end, existing approaches add noise to the results of queries that extract properties of an event log, such as the frequency distribution of trace variants, for analysis. Noise insertion neglects the semantics of the process, though, and may generate traces not present in the original log. This is problematic. It lowers the utility of the published data and makes noise easily identifiable, as some traces will violate well-known semantic constraints. In this paper, we therefore argue for privacy preservation that incorporates a process’ semantics. For common trace-variant queries, we show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result. Experiments demonstrate that our semantics-aware anonymization yields event logs of significantly higher utility than existing approaches.

The comparison of a model of a process against event data recorded during its execution, known as conformance checking, is an important means in process analysis. Yet, common conformance checking techniques are computationally expensive, which makes a complete analysis infeasible for large logs. To mitigate this problem, existing techniques leverage data samples. Then, the result quality depends on the relevance of the sample for a specific analysis task. Existing sampling strategies therefore rely on a static assumption on what constitutes relevant event data, which is generally unknown a priori.In this paper, we present relevance-guided sampling of event logs. Instead of employing a fixed relevance hypothesis, our approach learns the characteristics of event data that determine its relevance for conformance checking. To this end, we first explore the correlations between characteristics of the event data and the goal of a conformance checking task, before exploiting these correlations to guide the selection of a data sample. We present different instantiations of this approach and demonstrate that they significantly improve the quality of samples, and hence of conformance checking results, compared to baseline strategies.