Case model landscapes: toward an improved representation of knowledge-intensive processes using the fCM-language

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 information due to the low abstraction level at which the inherently complex processes are represented. Thus, readability and understandability are more challenging than in imperative process models. This paper extends a case modeling language—the fragment-based Case Management (fCM) language—to a so-called fCM landscape (fCML) with the goal of modeling a single knowledge-intensive process from a higher abstraction level. Following the Design Science Research (DSR) methodology, we first define requirements for an fCML, and then review how literature—in the fields of process overviews and case management—could support them. Design decisions are formalized by specifying a syntax for an fCML and the transformation rules from a given fCM model. The proposal is empirically evaluated via a laboratory experiment. Quantitative results imply that interpreting an fCML requires less effort in terms of time—and is thus more efficient—than interpreting its equivalent fCM case model. Qualitative results provide indications on the factors affecting case model interpretation and guidelines for future work.