Abstract
Case Management is a paradigm to support knowledge-intensive processes. The different approaches developed for modeling these types of processes tend to result in scattered models due to the low abstraction level at which the inherently complex processes are therein represented. Thus, readability and understandability is more challenging than that of traditional process models. By reviewing existing proposals in the field of process overviews and case models, this paper extends a case modeling language – the fragment-based Case Management (fCM) language – with the goal of modeling knowledge-intensive processes from a higher abstraction level – to generate a so-called fCM landscape. This proposal is empirically evaluated via an online experiment. Results indicate that interpreting an fCM landscape might be more effective and efficient than interpreting an informationally equivalent case model.
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A language is a structured set of symbols whose combination represents concepts which carry a certain meaning. A language is specified using a meta-model describing its abstract syntax (i.e. constituting concepts and their relations) and its semantics (i.e. meaning of the concepts).
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Forms and raw data available at: https://drive.google.com/drive/folders/1c-ZZ6HA6H7d7yOgthcoVANLt-wnRfOhS?usp=sharing.
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Gonzalez-Lopez, F., Pufahl, L. (2019). A Landscape for Case Models. In: Reinhartz-Berger, I., Zdravkovic, J., Gulden, J., Schmidt, R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2019 2019. Lecture Notes in Business Information Processing, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-20618-5_6
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