Data model development for process modeling recommender systems
Autor(en): | Fellmann, M. Metzger, D. Thomas, O. |
Herausgeber: | Horkoff, J. Jeusfeld, M.A. Persson, A. |
Stichwörter: | Data model; Data structures; Electronic commerce; Enterprise process modeling; Error prone tasks; Model development; Process Modeling; Real-world system; Recommender systems; Recommender systems, Business process model; Requirements; Stepwise approach, Process engineering | Erscheinungsdatum: | 2016 | Herausgeber: | Springer Verlag | Journal: | Lecture Notes in Business Information Processing | Volumen: | 267 | Startseite: | 87 | Seitenende: | 101 | Zusammenfassung: | 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. © IFIP International Federation for Information Processing 2016. |
Beschreibung: | Conference of 9th IFIP WG 8.1. Working Conference on the Practice of Enterprise Modeling, PoEM 2016 ; Conference Date: 8 November 2016 Through 10 November 2016; Conference Code:186109 |
ISBN: | 9783319483924 | ISSN: | 18651348 | DOI: | 10.1007/978-3-319-48393-1_7 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994851022&doi=10.1007%2f978-3-319-48393-1_7&partnerID=40&md5=84aeddb0dd4df213a141022b5961307d |
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geprüft am 21.05.2024