Abstract
Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.
Highlights
Analyzing multi-faceted big data is challenging (Kehrer and Hauser, 2013; Hadlak et al, 2015)
We introduce an alternative approach situated at the interface of integration and separation, which we call flexible visual analytics (FVA)
Which aspects need to be transitioned via interpolation in the data space, which aspects are safe to be transitioned in the visual space? How to best group and stage individual atomic transitions to generate an overall comprehensible and helpful view transition? The literature does not yet provide guidelines in this regard, which calls for more research on FVA
Summary
Analyzing multi-faceted big data is challenging (Kehrer and Hauser, 2013; Hadlak et al, 2015). Example is shown, where data entities (white dots) and their structural connections (gray lines) are embedded within selected geographic regions of a perspective 3D map display (Hadlak et al, 2010). Blue and red spikes between the layers indicate where data entities start or cease to exist across time. While this visual representation integrates time, space, and structural connections, it is rather complex and requires some training to decipher and some interaction to explore. With many separate single-aspect views, the user needs to visually integrate findings made in one view with patterns of different data characteristics shown in other views. We propose flexible visual analytics to combine the strengths of both data visualization approaches, as we discuss
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