Automated Planning of Context-aware Process Models

Most real world processes are heavily influenced by environmental factors, which are referred to as the context of a process. Thus, the consideration of context is proposed within the research strand of Business Process Modeling. Most existing context-aware modeling approaches con-sider context only in terms of static information like, for instance, the location where a process is performed. However, context information like the weather could change during the conduction of a process, which we will denote as non-static context information. In order to increase the flexibility concerning environmental influences in general and especially context-related events of context-aware processes, we present an approach for the automated planning of context-aware process models that considers static and non-static context information. We therefore propose an extended state transition system in order to represent context information in terms of context variables and consider process exogenous changes of these context variables through context signals and receive context actions. Further, to ensure a correct, complete and time effi-cient construction of context-aware process models, a planning approach is used to support modelers by means of an algorithm. To demonstrate the feasibility of our approach we mathe-matically evaluated the algorithm and applied it to real world processes.