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Extracting Data Objects and Their States from Process Models

Business process model collections received much attention as option to store an organization's process models and to make them available for further usage. Recently, also data objects and their states are studied intensively. However, process model collections often do not contain data objects. Generally, they focus on control flow structures of the process models. In this paper, we propose an approach to extract data objects from process models existing in a process model collection based on label analysis. In addition, the states of these data objects are extracted as well. After extraction, the data objects and their states are added to the corresponding process model. These resulting process models then can be used to, for instance, perform consistency checks between the process model and object life cycles in real world process model collections. First, the approach is presented generally for block structured process models independently of their notation before the approach gets adapted exemplarily to BPMN and EPCs to show the simplicity of adaptation to specific modeling notations. Finally, the approach is evaluated in a user experiment using the BPMAI model collection.