EuroVisSTAR2020
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Browsing EuroVisSTAR2020 by Author "Ottley, Alvitta"
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Item Survey on Individual Differences in Visualization(The Eurographics Association and John Wiley & Sons Ltd., 2020) Liu, Zhengliang; Crouser, R. Jordan; Ottley, Alvitta; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, BeiDevelopments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one-size-fits-all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence-based reviews, and spur future inquiry.Item Survey on the Analysis of User Interactions and Visualization Provenance(The Eurographics Association and John Wiley & Sons Ltd., 2020) Xu, Kai; Ottley, Alvitta; Walchshofer, Conny; Streit, Marc; Chang, Remco; Wenskovitch, John; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, BeiThere is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence-based guidelines and highlights concrete opportunities for future development in this emerging area.