Data Model Development for Process Modeling Recommender Systems

Abstract : The manual construction of business process models is a time-consuming 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.
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Communication dans un congrès
Jennifer Horkoff; Manfred A. Jeusfeld; Anne Persson. 9th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM), Nov 2016, Skövde, Sweden. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-267, pp.87-101, 2016, The Practice of Enterprise Modeling 〈10.1007/978-3-319-48393-1_7〉
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Michael Fellmann, Dirk Metzger, Oliver Thomas. Data Model Development for Process Modeling Recommender Systems. Jennifer Horkoff; Manfred A. Jeusfeld; Anne Persson. 9th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM), Nov 2016, Skövde, Sweden. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-267, pp.87-101, 2016, The Practice of Enterprise Modeling 〈10.1007/978-3-319-48393-1_7〉. 〈hal-01653517〉

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