Lumina: an adaptive, automated and extensible prototype for exploring, enriching and visualizing data

Given a tabular dataset which should be graphically represented, how could the current complex visualization pipeline be improved? Could we produce a more visually enriched final representation, while minimizing the user intervention? Most of the existing approaches lack in capacity to provide a simplified end-to-end solution and leave the intricate process of setting up the data connections to the user. Their results mainly depend on necessary user actions at every step of the visualization pipeline and fail to consider the data structural properties and the constantly rising volume of open and linked data. This work is motivated by the need of a flexible framework which will improve the user experience and interaction by simplifying the process and enhancing the result, capitalizing on the enrichment of the final visualization based on the semantic analysis of linked data. We propose Lumina, a visualization framework, which : (a) builds on structural data analytics and semantic analysis principles, (b) increases the explainability and expressiveness of the visualization leveraging open data and semantic enrichment, (c) minimizes user interventions at every step of the visualization pipeline and (d) fulfills the growing need for open-source, modular and self-hosted solutions. Using publicly available read-world datasets, we validate the adaptability of Lumina and demonstrate the effectiveness and practicality of our method, in comparison to other open source solutions.