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How can business model processes be compared in a systematic way? 


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Business process models can be compared in a systematic way by measuring their similarity and assessing their behavioral distance. Various similarity measures have been developed to quantify the consistency between process models across different abstraction levels, such as control flow and data flow perspectives . These measures can be used to determine the structural distance between process models and assess the behavioral distance between the corresponding event logs . Additionally, the values of most similarity measures highly correlate, indicating their applicability in different contexts . To compare process models, it is important to consider the correspondences between models or between their nodes, and various similarity measures have been proposed for this purpose . Furthermore, a translation from process models to Asymmetric Event Structures (AES) can be used to diagnose behavioral differences between pairs of models, capturing behavioral relations and differences in terms of repetition and causal relations .

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The paper proposes a solution based on translating process models to Asymmetric Event Structures (AES) to compare business process models in a systematic way.
The paper provides an overview of various similarity measures for comparing business process models, including structure-based quantification using metrics and generic distance measures like Euclidean, Hamming, or Jaccard distance.
The paper discusses the development of automatic similarity measurement approaches for comparing business process models. It analyzes the correlation between different similarity measures and their performance in terms of resource consumption. However, it does not provide a systematic way to compare business model processes.
The paper presents a systematic approach to quantitatively measure the consistency between business processes across different abstraction levels.
The paper discusses the comparison of business process models by visually comparing conceptual process models discovered from different "slices" of event logs.

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