Sap Se

Gregor Berg

Andre Niklas Wenz

Bernhard Hoeppner

Olga Cherepanova

Thomas Bodner

Pascal Schulze

Konstantin Dobler

Lasse Steffen

Kris-Fillip Kahl

Paul Udo Beneke

Philipp Bernhard Hoberg

Jan Siebert

David Hennemann

Frank Feinbube

Lukas Egger

Sushovan Chattaraj

Natallia Jamieson

Chirag Poddar

Stefan Voll

Bastian Hoeltkemeier

Sean Leonard

Bernd Lauer

Andreas Breitrueck

Maximilian Hoelzig

Zinayida Kensche

Paul-Alexander Herr

Florian Moritz

Alexander Cramer

Florian Haupt

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.

Process execution guide based on process model

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.

Process modeling based on process insights

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.

Automatic discovery of executed processes

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.

Generation of bots based on observed behavior

Provided are systems and methods for creating and managing an interactive process graphs which expedite performance of a multi-user enterprise process between user interfaces and the underlying systems. In one example, a method may include generating a process graph of a user interface process, wherein the process graph comprises nodes corresponding to activities and vertices between the nodes identifying dependencies among the activities, embedding input fields in the nodes of the process graph, embedding, via the process graph, an identifier of a current location of a data object within an instance within the user interface process, and displaying an instance of the process graph corresponding to the instance of the user interface process which includes the embedded input fields in the nodes and the identifier of the current location of the data object within the instance of the user interface process.

Interactive process graph for multi-UI process

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.

An application server provides an application or set of applications to one or more client devices. One or more users of the client devices interact with the applications to perform a business process. Data is stored on one or more of the client devices that tracks user interactions with the application. The data is transmitted from the client devices to the application server for analysis. Data from multiple client devices is correlated to enable analysis of a complex business process. As a result of the analysis, bottlenecks may be identified based on a particular process step taking a long time to complete or receiving repeated re-entry of data. The solution is automated and scalable, allowing for analysis of an entire business process landscape.

Frontend process mining

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Berlin, Germany

Gregor Berg