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Title: Information Dashboards and Tailoring Capabilities - A Systematic Literature Review
Authors: Vázquez-Ingelmo, A.
García-Peñalvo, F. J.
Therón, R.
Keywords: SLR
systematic literature review
nformation dashboards
Issue Date: 19-Aug-2019
Publisher: IEEE
Citation: A. Vázquez-Ingelmo, F. J. García-Peñalvo and R. Therón, "Information Dashboards and Tailoring Capabilities - A Systematic Literature Review," IEEE Access, vol. 7, pp. 109673-109688, 2019. doi: 10.1109/ACCESS.2019.2933472.
Abstract: The design and development of information dashboards are not trivial. Several factors must be accounted; from the data to be displayed to the audience that will use the dashboard. However, the increase in popularity of these tools has extended their use in several and very different contexts among very different user profiles. This popularization has increased the necessity of building tailored displays focused on specific requirements, goals, user roles, situations, domains, etc. Requirements are more sophisticated and varying; thus, dashboards need to match them to enhance knowledge generation and support more complex decision-making processes. This sophistication has led to the proposal of new approaches to address personal requirements and foster individualization regarding dashboards without involving high quantities of resources and long development processes. The goal of this work is to present a systematic review of the literature to analyze and classify the existing dashboard solutions that support tailoring capabilities and the methodologies used to achieve them. The methodology follows the guidelines proposed by Kitchenham and other authors in the field of software engineering. As results, 23 papers about tailored dashboards were retrieved. Three main approaches were identified regarding tailored solutions: customization, personalization, and adaptation. However, there is a wide variety of employed paradigms and features to develop tailored dashboards. The present systematic literature review analyzes challenges and issues regarding the existing solutions. It also identifies new research paths to enhance tailoring capabilities and thus, to improve user experience and insight delivery when it comes to visual analysis.
ISSN: 2169-3536
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