| Title |
EA ModelSet - A FAIR Dataset for Machine Learning in Enterprise Modeling |
| ID_Doc |
15858 |
| Authors |
Glaser, PL; Sallinger, E; Bork, D |
| Title |
EA ModelSet - A FAIR Dataset for Machine Learning in Enterprise Modeling |
| Year |
2024 |
| Published |
|
| DOI |
10.1007/978-3-031-48583-1_2 |
| Abstract |
The conceptual modeling community and its subdivisions of enterprise modeling are increasingly investigating the potentials of applying artificial intelligence, in particularmachine learning (ML), to tasks like model creation, model analysis, and model processing. A prerequisiteand currently a limiting factor for the community-to conduct research involving ML is the scarcity of openly available models of adequate quality and quantity. With the paper at hand, we aim to tackle this limitation by introducing an EA ModelSet, i.e., a curated and FAIR repository of enterprise architecture models that can be used by the community. We report on our efforts in building this data set and elaborate on the possibilities of conducting ML-based modeling research with it. We hope this paper sparks a community effort toward the development of a FAIR, large model set that enables ML research with conceptual models. |
| Author Keywords |
Enterprise modeling; Machine learning; FAIR; Enterprise architecture; Data set |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) |
| EID |
WOS:001285963600002 |
| WoS Category |
Business; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Management; Operations Research & Management Science |
| Research Area |
Business & Economics; Computer Science; Operations Research & Management Science |
| PDF |
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