Abstract
The H2020 TOREADOR Project adopts a model-driven architecture to streamline big data analytics and make it widely available to companies as a service. Our work in this context focuses on visualization, in particular on how to automate the translation of the visualization objectives declared by the user into a suitable visualization type. To this end we first define a visualization context based on seven prioritizable coordinates for assessing the user’s objectives and describing the data to be visualized; then we propose a skyline-based technique for automatically translating a visualization context into a set of suitable visualization types. Finally, we evaluate our approach on a real use case excerpted from the pilot applications of TOREADOR.
This work was partly supported by the EU-funded project TOREADOR (contract n. H2020-688797).
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Golfarelli, M., Pirini, T., Rizzi, S. (2017). Goal-Based Selection of Visual Representations for Big Data Analytics. In: de Cesare, S., Frank, U. (eds) Advances in Conceptual Modeling. ER 2017. Lecture Notes in Computer Science(), vol 10651. Springer, Cham. https://doi.org/10.1007/978-3-319-70625-2_5
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DOI: https://doi.org/10.1007/978-3-319-70625-2_5
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