Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program to generate an execution graph. The execution graph includes a semantic layer that is generated based on the processes involved in the system and the object types shared between the processes. The execution graph further includes an execution layer that is generated by modifying the semantic layer to include object instances that are involved in the processes. The execution layer can be presented on a display, thus allowing a user to visualize the process interactions taking place between the processes.

Abstract: Each event type in a business process is represented by a node in a process discovery graph. Semantic clustering merges nodes based on a commonality between them. As a result, a graphical user interface (GUI) may be presented that shows fewer nodes. This process may be repeated, using different commonalities at each level. A user may select a node to zoom in, showing the constituent nodes. The frequency of an event or its other attribute values may be compared to a predetermined threshold to determine if the event is a blocking event. The GUI may indicate that the node for an event is a blocking event. When the node for the event is merged with other nodes using semantic clustering, the blocking status of the node is transferred to the merged node. After zooming in on the blocking event, the GUI allows the user to select the blocking event to cause display of a recommendation.

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: In an example embodiment, a machine learning model is trained to predict one or more transportation modes for a portion of a process flow (such as a shipment). This prediction may be based on, for example, the size and weight of the shipment, the distance and geographical features of the distance between the pickup location for the shipment and the delivery location for the shipment. Based on the prediction as well as a calculated metric called “risk of inaccuracy”, a sustainability score may be calculated for the shipment. The sustainability score may then be used to recommend one or more actions to adjust a process flow that includes the shipment to reduce environmental impact of the shipment and future similar shipments.

Abstract: Large enterprises often modify standard process templates provided by software vendors to better suit their organizational processes. These modifications, which can include tracking new types of events or using modified event definitions, are typically difficult for the software vendor to detect. However, they can represent improvements to the template that could provide enhanced functionality for other organizations. The present disclosure provides techniques and solutions for detecting and analyzing such modifications made by organizations. It determines whether a subset of these modifications are semantically equivalent and whether they satisfy a threshold. If the threshold is satisfied, the process template is updated to include a modification of the semantically equivalent modifications, providing an updated version of the standard template that can be provided to other organizations.

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: The present disclosure provides techniques and solutions for improving computer-implemented processes. Process mining is performed to identifying processes of a first entity. Characteristics of the first entity at a first time are used to identify a set of reference entities having similar characteristics to the first entity at a second, earlier time. Process mining is performed for the set of reference entities to identify process changes that led to process improvements for the set of reference entities. These process changes are used to suggest process changes to the first entity that may improve performance of the process.

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: Provided is a system and method for evaluating the performance of a process using external process data, for example, from another similar process. In one example, the method may include generating a diagram of a process based on data from the process, where the diagram comprises a sequence of nodes that correspond to a sequence of events and edges between the sequence of nodes which indicate execution times between the events, displaying the diagram via a user interface of a software application, selecting a reference diagram of a reference process that includes a different sequence of nodes corresponding to a different sequence of events, identifying an improvement to the process based on the reference diagram, and modifying the diagram to include a different execution flow included in the reference diagram based on the identified improvement.