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Título
Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning
Autor(es)
Materia
Automatic generation
Domain engineering
Meta-modeling
Information Dashboards
High-level requirements
Clasificación UNESCO
1203.17 Informática
Fecha de publicación
2019
Citación
A. Vázquez-Ingelmo, F. J. García-Peñalvo and R. Therón, "Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning," in TEEM’19 Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality (Leon, Spain, October 16th-18th, 2019), M. Á. Conde-González, F. J. Rodríguez-Sedano, C. Fernández-Llamas and F. J. García-Peñalvo, Eds. ICPS: ACM International Conference Proceedings Series, pp. 1007-1011, New York, NY, USA: ACM, 2019. doi: 10.1145/3362789.3362923.
Resumen
Information dashboards are sophisticated tools. Although they
enable users to reach useful insights and support their decisionmaking
challenges, a good design process is essential to obtain
powerful tools. Users need to be part of these design processes,
as they will be the consumers of the information displayed. But
users are very diverse and can have different goals, beliefs,
preferences, etc., and creating a new dashboard for each
potential user is not viable. There exist several tools that allow
users to configure their displays without requiring programming
skills. However, users might not exactly know what they want to
visualize or explore, also becoming the configuration process a
tedious task. This research project aims to explore the automatic
generation of user interfaces for supporting these decisionmaking
processes. To tackle these challenges, a domain
engineering, and machine learning approach is taken. The main
goal is to automatize the design process of dashboards by
learning from the context, including the end-users and the target
data to be displayed.
URI
DOI
10.1145/3362789.3362923
Colecciones
- GRIAL. Artículos [441]