Please use this identifier to cite or link to this item:
|Title:||A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare|
García-Peñalvo, F. J.
|Citation:||Vázquez-Ingelmo, A., García-Holgado, A., García-Peñalvo, F. J., & Therón, R. (2020). A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare. Sensors, 20(15), 4072. doi:10.3390/s20154072|
|Abstract:||Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge—experience, skills, insights, intuition, judgment and know-how—into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a different context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from different sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain|
|Appears in Collections:||Publications|
Files in This Item:
|sensors-20-04072.pdf||Article||3,78 MB||Adobe PDF||View/Open|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.