A Meta-Framework for Creating Coordinated and Multiple-View Applications

Abstract

The visualization field has been part of the advancement of science throughout history. In the 1970s, a movement emerged that advocated for graphical representations as one of the necessary pillars of data analysis. Since then, numerous scientific fields and businesses have adopted visualization as a pillar to facilitate understanding information and disseminating results. Currently, part of the research in this field focuses on new approaches aimed at increasing the accessibility of visualization in order to promote its adoption. These works maintain a balance between visualization expressiveness and ease of use in order to address the needs of the broadest audience possible. However, they remain primarily limited to developing single views, a highly limiting factor to their adoption. Complete data analysis requires a mix of different visualizations and data processing algorithms. Furthermore, to reduce the complexity problem inherent to visualizations and to maximize the analysis capabilities, it is necessary to use highly interactive visualizations and multiple views. Some state-of-the-art works define visualization dashboards that implement this multi-view paradigm, allowing their coordination through graphical user interfaces. To further facilitate this perspective, other methods use data flow diagrams tailored for visualization to assemble the functionality that drives these visualization dashboards. These diagrams have the advantage of being a visual programming approach, which makes them very accessible to users without programming skills, further increasing the accessibility of these tools. However, the field of visualization, and science in general, is a rapidly changing field where researchers are constantly proposing new advances. Therefore, the previous tools must be able to adapt to new developments in order to maintain pace with this constant evolution. Despite this, state-of-the-art visualization solutions are usually highly limited to a fixed functionality. This thesis addresses the previous problems by defining a meta-framework for building data analysis applications based on coordinated and multiple views through data flow diagrams, VMetaFlow. Unlike other works that follow this approach, we use diagram connections to model coordination. Therefore, our approach allows us to extend the range of supported interactions between views, effectively supporting a more comprehensive range of multiple views techniques. Furthermore, we define our meta-framework as an abstraction layer over concrete visualizations and data processing algorithms implementations. This way, our work can adapt and benefit from new advances in these fields by integrating the latest developments. Finally, to evaluate our work, we define three use cases and a critical inspection with expert users. The first use case allows us to compare VMetaFlow with the most recent work based on data flow diagrams for building visualization applications. In the second use case, we address an exploratory analysis where combining and coordinating information visualization, scientific visualization, and data processing modules is necessary. In the third use case, we address the study of the behavior of large-scale optimization algorithms using visualization and dimensionality reduction, research that we will publish shortly. Finally, the visualization experts interviewed for the critical inspection confirm the advantages of our meta-framework over other state-of-the-art works.