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Title: Domain engineering for generating dashboards to analyze employment and employability in the academic context
Authors: Vázquez-Ingelmo, A.
García-Peñalvo, F. J.
Therón, R.
Keywords: Domain engineering
software product lines
information dashboards
information systems
university employment
university employability
Issue Date: 24-Oct-2018
Publisher: ACM
Citation: Vázquez-Ingelmo, A., García-Peñalvo, F. J., & Therón, R. (2018). Domain engineering for generating dashboards to analyze employment and employability in the academic context. In F. J. García-Peñalvo (Ed.), TEEM’18 Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality (Salamanca, Spain, October 24th-26th, 2018) (pp. 896-901). New York, NY, USA: ACM. doi:10.1145/3284179.3284329
Abstract: Data analysis is a key process to foster knowledge generation regarding particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Dashboards offer a software solution for visually analyzing large volumes of data in order to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Having a methodology to efficiently generate dashboards taking into account differing needs would add a customization layer to allow particular users to reach their own goals. This approach can be achieved through domain engineering and automatic code generation processes. This paper presents the application of domain engineering within the dashboards’ domain through a case study in the context of the Spanish Observatory for University Employment and Employability, in which a set of dashboards can be generated to exploit different perspectives of employment and employability data in the academic context.
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