56 research outputs found

    Towards Scalable Visual Exploration of Very Large RDF Graphs

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    In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.Comment: 12th Extended Semantic Web Conference (ESWC 2015

    Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors

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    While visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology), knowledge about humans' perception of graph similarity is currently quite limited. By graph similarity perception we mean how humans perceive commonalities and differences in graphs and herewith come to a similarity judgment. As a step toward filling this gap the study reported in this paper strives to identify factors which influence the similarity perception of DAGs. In particular, we conducted a card-sorting study employing a qualitative and quantitative analysis approach to identify 1) groups of DAGs that are perceived as similar by the participants and 2) the reasons behind their choice of groups. Our results suggest that similarity is mainly influenced by the number of levels, the number of nodes on a level, and the overall shape of the graph.Comment: Graph Drawing 2017 - arXiv Version; Keywords: Graphs, Perception, Similarity, Comparison, Visualizatio

    Survey of Surveys (SoS) ‐ Mapping The Landscape of Survey Papers in Information Visualization

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    Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995.However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field.Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers,and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantumstep forward by surveying and classifying literature survey papers in order to help researchers understand the current landscapeof Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. Thispaper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops thefirst classification of classifications. The paper also enables researchers to identify both mature and less developed researchdirections as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in andoutside the field of Information Visualization and Visual Analytic

    Scalability considerations for multivariate graph visualization

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    Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations
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