Toward flexible visual analytics augmented through smooth display transitions

Tominski, Christian; Andrienko, Gennady; Bleisch, Susanne; Fabrikant, Sara Irina; Mayr, Eva; Miksch, Silvia; Pohl, Margit; Skupin, André

doi:10.1016/j.visinf.2021.06.004

Cited by 18 publications

(8 citation statements)

References 39 publications

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“…We are especially inspired by the notion of ‘fluid interaction’ suggesting that in‐situ representation changes stimulate open‐ended and engaging flow experiences by making use of direct manipulation and animated transitions between views [EMJ * 11]. Such transitioned representations for the exploration of complex multivariate data have been characterized as flexible, elastic, and ‘folded’ spaces whose elements can be dynamically manipulated inside a given layout [BBD20, BBBD20, TAA * 21]. Following this line of research, we developed the concept of unfolding edges.…”

Section: Related Workmentioning

confidence: 99%

Unfolding Edges: Adding Context to Edges in Multivariate Graph Visualization

Bludau

Dörk

Tominski

2023

Computer Graphics Forum

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Existing work on visualizing multivariate graphs is primarily concerned with representing the attributes of nodes. Even though edges are the constitutive elements of networks, there have been only few attempts to visualize attributes of edges. In this work, we focus on the critical importance of edge attributes for interpreting network visualizations and building trust in the underlying data. We propose ‘unfolding of edges’ as an interactive approach to integrate multivariate edge attributes dynamically into existing node‐link diagrams. Unfolding edges is an in‐situ approach that gradually transforms basic links into detailed representations of the associated edge attributes. This approach extends focus+context, semantic zoom, and animated transitions for network visualizations to accommodate edge details on‐demand without cluttering the overall graph layout. We explore the design space for the unfolding of edges, which covers aspects of making space for the unfolding, of actually representing the edge context, and of navigating between edges. To demonstrate the utility of our approach, we present two case studies in the context of historical network analysis and computational social science. For these, web‐based prototypes were implemented based on which we conducted interviews with domain experts. The experts' feedback suggests that the proposed unfolding of edges is a useful tool for exploring rich edge information of multivariate graphs.

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Section: Related Workmentioning

confidence: 99%

Unfolding Edges: Adding Context to Edges in Multivariate Graph Visualization

Bludau

Dörk

Tominski

2023

Computer Graphics Forum

Self Cite

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“…Analyzing a large amount of fragmented data can be simplified by converging the big data and augmented analytics. Moreover, it helps to provide simplified statements to the customers in an understandable manner [ 68 ]. The visualization of a large pool of data can be made easier with the help of animation.…”

Section: Future Research Directionsmentioning

confidence: 99%

Psychological Impact and Influence of Animation on Viewer’s Visual Attention and Cognition: A Systematic Literature Review, Open Challenges, and Future Research Directions

Praveen

Srinivasan

2022

Computational and Mathematical Methods in Medicine

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Animation is an excellent method to associate with the audience in a fun and innovative manner. In recent span, animation has been employed in various fields to enhance knowledge, marketing, advertisement, and age groups from infants to adults. The present communication expounds the systematic review on the impact created by animation on the viewer’s visual attention. For this review, a database such as Google Scholar, ScienceDirect, Taylor & Francis, and IEEE Xplore were pursued for publications on the impact of animation on viewer’s visual attention from January 2015 to December 2021. The search results showcased 175 titles with 114 full articles, out of which 35 were related to viewers’ visual attention towards animation. These reviewed studies comprised of physical outcome ( n = 9 ), psychological outcome ( n = 15 ), and cognitive outcome ( n = 11 ) from which the attention-related factors, physical effects, and cognitive effects of animation were assessed. The animation has influenced the viewer’s visual attention through the integration of the different stimuli and the highly organized presentation. Furthermore, the animation has also aided the viewer in attaining greater conceptual understanding, thereby facilitating their cognitive response. As a result, the animation was found to be helpful in enhancing learning skills, food marketing, and teaching strategy. Furthermore, the drawbacks and future recommendations of the studies were elaborated. In addition, challenges and open issues faced during the studies were discussed. Finally, the priority areas in animation identified for promising future directions to visualize large pool data, provide smart communication, and design 3D modeling structures were highlighted.

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“…Further, Trifacta and its predecessor Data Wrangler [17,29] integrate an inference engine that generates a ranked list of suggested transformations. Others aim to address wrangling graph [10,20,22,42,44,62] and website [7,24,39,41,46] data. Some of the above tools [17,23,29,58,64] support recording the process of data transformations through a domainspecific language, and provide textual descriptions of transformations.…”

Section: Data Wranglingmentioning

confidence: 99%