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.
Goal-Based Selection of Visual Representations for Big Data Analytics / Golfarelli, Matteo; Pirini, Tommaso; Rizzi, Stefano. - ELETTRONICO. - 10651:(2017), pp. 47-57. (Intervento presentato al convegno Sixth International Workshop on Modeling and Management of Big Data tenutosi a Valencia, Spain nel November 6, 2017) [10.1007/978-3-319-70625-2_5].
Goal-Based Selection of Visual Representations for Big Data Analytics
GOLFARELLI, MATTEO;PIRINI, TOMMASO;RIZZI, STEFANO
2017
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.File | Dimensione | Formato | |
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