Interactive Visual Analysis of Set-Typed Data

Freiler, W.; Matković, Krešimir; Hauser, Helwig

doi:10.1109/tvcg.2008.144

Cited by 27 publications

(21 citation statements)

References 16 publications

(14 reference statements)

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“…Matrix based methods exploiting Karnaugh maps have been developed, such as KMVQL [Huo 2008], but they are typically considered to be restricted to 6 sets or fewer [Alsallakh et al 2014] meaning that they lack general applicability. Some aggregation techniques cope better with larger number of sets, including Set'o'grams [Freiler et al 2008] and Radial Sets [Alsallakh et al 2013]. Set'o'grams are designed to be used in an interactive environment, only revealing set intersections on user input.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Sets with Linear Diagrams

Rodgers

Chapman

2015

ACM Trans. Comput.-Hum. Interact.

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This paper presents the first design principles that optimize the visualization of sets using linear diagrams. These principles are justified through empirical studies that evaluate the impact of graphical features on task performance. Linear diagrams represent sets using straight line segments, with line overlaps corresponding to set intersections. This work builds on recent empirical research which establishes that linear diagrams can be superior to prominent set visualization techniques, namely Euler and Venn diagrams. We address the problem of how to best visualize overlapping sets using linear diagrams. To solve the problem, we investigate which graphical features of linear diagrams significantly impact user task performance. To this end, we conducted seven crowd-sourced empirical studies involving a total of 1760 participants. These studies allowed us to identify the following design principles, which significantly aid task performance: use a minimal number of line segments, use guide-lines where overlaps start and end, and draw lines that are thin as opposed to thick bars. We also evaluated the following graphical properties which did not significantly impact task performance: colour, orientation, and set-order. The results are brought to life through a freely available software implementation that automatically draws linear diagrams with user-controlled graphical choices. An important consequence of our research is that users are now able to create effective visualizations of sets automatically, thus improving human-computer interaction.

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Section: Introductionmentioning

confidence: 99%

Visualizing Sets with Linear Diagrams

Rodgers

Chapman

2015

ACM Trans. Comput.-Hum. Interact.

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“…When considered against the alternatives, other than what we have just discussed, the only other current proposal for set-valued attributes we are aware of is the Set'o'Gram technique [5]. We believe the advantage of our method is that it maintains the itemorientation of the views.…”

Section: Resultsmentioning

confidence: 99%

“…Techniques that create more visually dense widgets for set-valued attributes also may have the virtue of more efficient use of visual real estate. Freiler and Matkovic have recently proposed a method called Set'o'Grams that is visually dense and provides for certain types of insights into the data [5]. They also integrate through brushing other well known visualization methods such as parallel coordinates, scatterplots, and so on, that would be suitable for non-set-valued attribute types.…”

Section: Introductionmentioning

confidence: 99%

Setting the bar for set-valued attributes

Wittenburg

2010

Proceedings of the International Conference on Advanced Visual Interfaces

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In this paper, we present an interactive visualization method for set-valued attributes that maintains the advantages of item-oriented views and interactions found in parallel multivariate visualizations such as bargrams (equal-height histograms). The challenge is to accommodate rendering of an item when it appears multiple times in set-valued attribute views while at the same time preserving value-and item-based selection, brushing, and filtering. Such techniques can help users derive particular types of insights into data based on distributions and correlations of attribute values. AVI 2010This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTIn this paper, we present an interactive visualization method for set-valued attributes that maintains the advantages of item-oriented views and interactions found in parallel multivariate visualizations such as bargrams (equal-height histograms). The challenge is to accommodate renderings of an item when it appears multiple times in set-valued attribute views while at the same time preserving value-and item-based selection, brushing, and filtering. Such techniques can help users derive particular types of insights into data based on distributions and correlations of attribute values.

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“…The Set'o'grams technique [10] relies heavily on brushing to show how elements occur across several sets. In this representation, element groups are distributed across several histogram-like blocks representing different categories/sets.…”

Section: Related Workmentioning

confidence: 99%

“…The visualization research community recently has developed a number of techniques to represent flexible collections of set elements [1,7,10,14,17,20]. Increasing quantities, both of elements and sets, remains a challenge though, and these techniques quickly become complex and difficult to visually interpret and understand as the sets contain larger numbers of elements.…”

Section: Introductionmentioning

confidence: 99%

OnSet: A Visualization Technique for Large-scale Binary Set Data

Sadana¹,

Major²,

Dove

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. User interface of OnSet. The list of sets is on the right (the orange bar indicates the number of elements in the set). Each set is represented as a rectangular region with the set elements being smaller interior rectangles. This view shows two individual sets (one is dimmed) on the left and two compositions of individual sets on the right.Abstract-Visualizing sets to reveal relationships between constituent elements is a complex representational problem. Recent research presents several automated placement and grouping techniques to highlight connections between set elements. However, these techniques do not scale well for sets with cardinality greater than one hundred elements. We present OnSet, an interactive, scalable visualization technique for representing large-scale binary set data. The visualization technique defines a single, combined domain of elements for all sets, and models each set by the elements that it both contains and does not contain. OnSet employs direct manipulation interaction and visual highlighting to support easy identification of commonalities and differences as well as membership patterns across different sets of elements. We present case studies to illustrate how the technique can be successfully applied across different domains such as bio-chemical metabolomics and task and event scheduling.

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Interactive Visual Analysis of Set-Typed Data

Cited by 27 publications

References 16 publications

Visualizing Sets with Linear Diagrams

Visualizing Sets with Linear Diagrams

Setting the bar for set-valued attributes

OnSet: A Visualization Technique for Large-scale Binary Set Data

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