The manual construction of business process models is a timeconsuming and error-prone task. To ease the construction of such models, several modeling support techniques have been suggested. However, while recommendation systems are widely used e.g. in e-commerce, such techniques are rarely implemented in process modeling tools. The creation of such systems is a complex task since a large number of requirements and parameters have to be addressed. In order to improve the situation, we develop a data model that can serve as a backbone for the development of process modeling recommender systems (PMRS). We systematically develop the model in a stepwise approach using established requirements and validate it against a data model that has been reverse-engineered from a real-world system. We expect that our contribution will provide a useful starting point for designing the data perspective of process modeling recommendation features.
CITATION STYLE
Fellmann, M., Metzger, D., & Thomas, O. (2016). Data model development for process modeling recommender systems. In Lecture Notes in Business Information Processing (Vol. 267, pp. 87–101). Springer Verlag. https://doi.org/10.1007/978-3-319-48393-1_7
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