Abstract: The present disclosure provides techniques and solutions for benchmarking process models by evaluating characteristics of the model, such as those reflecting model complexity. Metrics can include the number of elements in a model, the number of roles, and the number of handoffs between roles, as a few examples. Metrics for a model can be compared with reference metrics, such as those calculated from a set of other models, which can be for the same modeled process or different processes. Collections of process models can be evaluated in a similar manner, including for a set of related models that may be expressed at different levels of specificity. Metrics for individual models in the collection can be evaluated and aggregated, and then compared with aggregated metric values of other model collections, for the same or different modeled processes.

Abstract: A process data store may contain a process model (e.g., a process graph, with process graph elements that include nodes and edges, as generated via process mining or a BPMN representation). A process server may retrieve information from the process data store and receive user feedback data. The server may determine if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store. If the information retrieved from the process data store is not associated with a prior mapping of survey questions, embodiments may utilize Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store. The server may then automatically assign, group, and analyze the user feedback data to generate a recommended alteration.

Abstract: A process data store may contain a process model (e.g., a process graph, with process graph elements that include nodes and edges, as generated via process mining or a BPMN representation). A process server may retrieve information from the process data store and receive user feedback data. The server may determine if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store. If the information retrieved from the process data store is not associated with a prior mapping of survey questions, embodiments may utilize Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store. The server may then automatically assign, group, and analyze the user feedback data to generate a recommended alteration.