Modeling the Dashboard Provenance

Johne Marcus Jarske, Jorge Rady de Almeida Júnior, Lucia Vilela Leite Filgueiras, Leandro Manuel Reis Velloso, Tânia Letícia Letícia Santos dos

Room: 110

2023-10-22T22:00:00ZGMT-0600Change your timezone on the schedule page
2023-10-22T22:00:00Z
Exemplar figure, described by caption below
Provenance Dashboard Model to improve reliability and effectiveness of a dashboard. Organizations across various sectors rely on dashboards for effective data visualization. Nevertheless, the reliability and effectiveness of these dashboards hinge on the quality of the visual elements and data they present. But how can we ensure the reliability and effectiveness of a dashboard? By integrating provenance into dashboards, users can significantly improve their decision-making processes by gaining a more comprehensive understanding of the dashboard's context and reliability. Provenance-driven dashboards empower users to explore a richer narrative of the data and visualizations, unveiling their meticulously curated history.
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Abstract

Organizations of all kinds, whether public or private, profit-driven or non-profit, and across various industries and sectors, rely on dashboards for effective data visualization. However, the reliability and efficacy of these dashboards rely on the quality of the visual and data they present. Studies show that less than a quarter of dashboards provide information about their sources, which is just one of the expected metadata when provenance is seriously considered. Provenance is a record that describes people, organizations, entities, and activities that had a role in the production, influence, or delivery of a piece of data or an object. This paper aims to provide a provenance representation model, that entitles standardization, modeling, generation, capture, and visualization, specifically designed for dashboards and its visual and data components. The proposed model will offer a comprehensive set of essential provenance metadata that enables users to evaluate the quality, consistency, and reliability of the information presented on dashboards. This will allow a clear and precise understanding of the context in which a specific dashboard was developed, ultimately leading to better decision-making.