The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations

Shneiderman, Ben

doi:10.1016/b978-155860915-0/50046-9

Cited by 1,457 publications

(1,863 citation statements)

References 17 publications

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“…We designed a visual encoding that captures all of these features in a single representation with the intent to eliminate the need for laborious cross-referencing between data sources. Inspired by the guidelines of Shneiderman [23], we built an interactive interface that provides an initial overview of the data set, while enabling subsequent focusing on regions of interest and access to details on demand.…”

Section: Design Decisionsmentioning

confidence: 99%

ABySS-Explorer: Visualizing Genome Sequence Assemblies

Nielsen

Jackman

Birol

et al. 2009

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. ABySS-Explorer employs a novel graph representation enabling biologists to examine the global structure of a genome sequence assembly.Abstract-One bottleneck in large-scale genome sequencing projects is reconstructing the full genome sequence from the short subsequences produced by current technologies. The final stages of the genome assembly process inevitably require manual inspection of data inconsistencies and could be greatly aided by visualization. This paper presents our design decisions in translating key data features identified through discussions with analysts into a concise visual encoding. Current visualization tools in this domain focus on local sequence errors making high-level inspection of the assembly difficult if not impossible. We present a novel interactive graph display, ABySS-Explorer, that emphasizes the global assembly structure while also integrating salient data features such as sequence length. Our tool replaces manual and in some cases pen-and-paper based analysis tasks, and we discuss how user feedback was incorporated into iterative design refinements. Finally, we touch on applications of this representation not initially considered in our design phase, suggesting the generality of this encoding for DNA sequence data.

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Section: Design Decisionsmentioning

confidence: 99%

ABySS-Explorer: Visualizing Genome Sequence Assemblies

Nielsen

Jackman

Birol

et al. 2009

IEEE Trans. Visual. Comput. Graphics

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“…This attribute of the visualizations nicely complement the existing categorization of visualizations (e.g. [3,4]). …”

Section: Three Levels Of Missing Data Impactmentioning

confidence: 70%

Visualizing Missing Data: Graph Interpretation User Study

Eaton

Plaisant

Drizd

2005

Lecture Notes in Computer Science

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Abstract. Most visualization tools fail to provide support for missing data. In this paper, we identify sources of missing data and describe three levels of impact missing data can have on the visualization: perceivable, invisible or propagating. We then report on a user study with 30 participants that compared three design variants. A between-subject graph interpretation study provides strong evidence for the need of indicating the presence of missing information, and some direction for addressing the problem.

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“…Global approaches, best characterized by Shneiderman's mantra "overview, zoom & filter, details-on-demand" pattern in visual information seeking [42], have conventionally received much attention and have worked well for numerous kinds of data in many domains [43], [44], [45], [46], [47], [48], [49], [50], [42]. However, in this big data era, top-down approaches that focus on providing overviews of global information landscapes face significant challenges when applied to graphs with millions or billions of nodes and edges [49], [50]: graph overviews for large graphs are time-consuming to generate [8], [7]; the seminal work on graph clustering by Leskovec & Faloutsos [9] suggests there are simply no perfect overviews (i.e., no single best way to partition graphs into smaller communities), a view echoed by sensemaking literature in that people may have very different mental representations of information depending on their individual goals and prior experiences [51].…”

Section: A Graph Sensemakingmentioning

confidence: 99%

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Pienta

Abello

Kahng

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Abstract-Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields -data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems -to underline their parallel and complementary contributions to graph sensemaking.We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.

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The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations

Cited by 1,457 publications

References 17 publications

ABySS-Explorer: Visualizing Genome Sequence Assemblies

ABySS-Explorer: Visualizing Genome Sequence Assemblies

Visualizing Missing Data: Graph Interpretation User Study

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

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