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Title: Representing Data Visualization Goals and Tasks Through Meta-Modeling to Tailor Information Dashboards
Authors: Vázquez-Ingelmo, A.
García-Peñalvo, F. J.
Therón, R.
Conde, M. Á.
Keywords: information dashboards
visualization goals
visualization tasks
data visualization
educational dashboards
Issue Date: 30-Mar-2020
Publisher: MDPI
Citation: Vázquez-Ingelmo, A., García-Peñalvo, F. J., Therón, R., & Conde, M. Á. (2020). Representing Data Visualization Goals and Tasks Through Meta-Modeling to Tailor Information Dashboards. Applied Sciences, 10(7), 2306. doi:10.3390/app10072306
Abstract: Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies.
ISSN: 2076-3417
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