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Communication dans un congrès Année : 2016

Data Model Development for Process Modeling Recommender Systems

Michael Fellmann
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Résumé

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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Dates et versions

hal-01653517, version 1 (01-12-2017)

Licence

Paternité - CC BY 4.0

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Citer

Michael Fellmann, Dirk Metzger, Oliver Thomas. Data Model Development for Process Modeling Recommender Systems. 9th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM), Nov 2016, Skövde, Sweden. pp.87-101, ⟨10.1007/978-3-319-48393-1_7⟩. ⟨hal-01653517⟩
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