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: In some implementations, techniques may include accessing stored payment data. For each payment term of a plurality of payment terms, the techniques may include calculating a distribution of payment dates before, on, and after a due date. The techniques may include determining the effectiveness of each of a plurality of payment terms based in part on the distribution of payment dates, where effectiveness is defined as a time difference between a payment due date and a payment date. The techniques may include determining a recommended payment term based at least in part on the effectiveness of each of the plurality of payment terms and an objective. The techniques may include generating an invoice including the recommended payment term.

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: The example embodiments are directed to systems and methods which may provide a guided user interface session for user input to a software process based on annotations added to a process model of the software process. In one example, a method may include receiving runtime data of an instance of software process from a workflow engine that is executing the instance of the software process, determining a process activity that is excepted to happen next within the running instance of the software process, identifying GUI and a subset of input elements within the GUI which are mapped to the determined process activity based on annotations within a process model of the software process, highlighting the identified subset of input elements and disabling any remaining input elements within the GUI to generate a guided GUI, and displaying the guided GUI via a computing system of a user.

Abstract: The example embodiments are directed to systems and methods which can generate a process graph with important process insights in a manner that is significantly faster and more scalable than related art approaches and answers questions most customers have through the use of standardization. In one example, a method may include querying a data store for document data of a process, identifying a plurality of milestones that occur within the process and blockers that prevent at least one of the milestones from completing based on the document data, generating a graph model of the process based on a predefined template, wherein the graph model comprises a plurality of nodes corresponding to the plurality of milestones and edges between the plurality of nodes based on the blockers, and displaying the graph model of the process via a user interface.

Abstract: Provided is a system and method for filtering data records via user interaction on a user interface. During the filtering process, the user interface can provide insights into the next filtering step by displaying additional insight on the user interface. In one example, the method may include displaying a user interface comprising interactive controls, receiving a selection of a filtering condition based on input on the user interface, in response to the selection, filtering a plurality of data records based on the selected filtering condition to identify a subset of data records that satisfy the filtering condition from among the plurality of data records, identifying a subset of filtering conditions from among the plurality of filtering conditions that are available for the subset of data records, and displaying an identifier of the subset of data records and identifiers of the subset of filtering conditions on the user interface.

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.

Abstract: The example embodiments are directed to systems and methods which can generate a process graph with important process insights in a manner that is significantly faster and more scalable than related art approaches and answers questions most customers have through the use of standardization. In one example, a method may include querying a data store for document data of a process, identifying a plurality of milestones that occur within the process and blockers that prevent at least one of the milestones from completing based on the document data, generating a graph model of the process based on a predefined template, wherein the graph model comprises a plurality of nodes corresponding to the plurality of milestones and edges between the plurality of nodes based on the blockers, and displaying the graph model of the process via a user interface.

Abstract: The example embodiments are directed to systems and methods which may provide a guided user interface session for user input to a software process based on annotations added to a process model of the software process. In one example, a method may include receiving runtime data of an instance of software process from a workflow engine that is executing the instance of the software process, determining a process activity that is excepted to happen next within the running instance of the software process, identifying GUI and a subset of input elements within the GUI which are mapped to the determined process activity based on annotations within a process model of the software process, highlighting the identified subset of input elements and disabling any remaining input elements within the GUI to generate a guided GUI, and displaying the guided GUI via a computing system of a user.

Abstract: The example embodiments are directed to systems and methods which can generate a process graph with important process insights in a manner that is significantly faster and more scalable than related art approaches and answers questions most customers have through the use of standardization. In one example, a method may include querying a data store for document data of a process, identifying a plurality of milestones that occur within the process and blockers that prevent at least one of the milestones from completing based on the document data, generating a graph model of the process based on a predefined template, wherein the graph model comprises a plurality of nodes corresponding to the plurality of milestones and edges between the plurality of nodes based on the blockers, and displaying the graph model of the process via a user interface.

Abstract: Data is gathered from a log file on a first application server, a log file on a second application server, a database on a database server, or any suitable combination thereof. By correlating the data from different sources, XP-Functions that execute in sequence on a single application server are identified and combined into a sequence referred to as an executable process chain (XP-Chain). The automatic process discovery server reconstructs end-to-end processes out of XP-Chains, even when the XP-Chains are executed on different application servers, based on log files and database data. A test script may be generated for an identified end-to-end process. By running the test script, proper functioning of the end-to-end process may be confirmed. Existing test scripts may be disabled for a formerly identified end-to-end process that is no longer found to be executed.

Abstract: The example embodiments are directed to systems and methods which can generate process insights in a manner that is significantly faster than process mining. In one example, a method may include loading a schema of a data system into memory, identifying a plurality of events that occur within a process supported by data from the data system and a plurality of blockers that prevent the plurality of events from completing, defining a query that comprises instructions for extracting data of the plurality of blockers from the data system and for analyzing the process based on the extracted data and the plurality of blockers, and generating a script for executing the defined query based on the schema of the data system and transmit the script to a software application.

Abstract: An application server provides an application to client devices. Users of the client devices interact with the application to perform a business process. Data regarding user interactions with the application is transmitted from the client devices to the application server. Based on an analysis of the received data, a bot generation server generates a bot to automate a process step. The bot generation server provides a heatmap user interface (UI) that provides information regarding the process steps. Using the heatmap UI, the administrator selects a process step for automation. In response to the selection, the bot generation server identifies, based on the observed behavior, relationships between input fields, typical values for input fields, typical order of data entry into input fields, or any suitable combination thereof. Based on the identified patterns, the bot generation server generates a bot to automate some or all of the process step.

Abstract: An application server provides an application to client devices. Users of the client devices interact with the application to perform a business process. Data regarding user interactions with the application is transmitted from the client devices to the application server. Based on an analysis of the received data, a bot generation server generates a bot to automate a process step. The bot generation server provides a heatmap user interface (UI) that provides information regarding the process steps. Using the heatmap UI, the administrator selects a process step for automation. In response to the selection, the hot generation server identifies, based on the observed behavior, relationships between input fields, typical values for input fields, typical order of data entry into input fields, or any suitable combination thereof. Based on the identified patterns, the hot generation server generates a bot to automate some or all of the process step.