Parallel bargrams for consumer-based information exploration and choice

Wittenburg, Kent; Lanning, Tom; Heinrichs, Michael; Stanton, Michael

doi:10.1145/502348.502357

Cited by 57 publications

(41 citation statements)

References 17 publications

(15 reference statements)

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“…Linear diagrams were introduced by Leibniz [3], with parallel bargrams [4] and double decker plots [5] being similar. Each set is represented as one or more horizontal line segments, with all sets drawn in parallel.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Sets: An Empirical Comparison of Diagram Types

Chapman

Rodgers

Micallef

et al. 2014

Diagrammatic Representation and Inference

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Abstract. There are a range of diagram types that can be used to visualize sets. However, there is a significant lack of insight into which is the most effective visualization. To address this knowledge gap, this paper empirically evaluates four diagram types: Venn diagrams, Euler diagrams with shading, Euler diagrams without shading, and the less well-known linear diagrams. By collecting performance data (time to complete tasks and error rate), through crowdsourcing, we establish that linear diagrams outperform the other three diagram types in terms of both task completion time and number of errors. Venn diagrams perform worst from both perspectives. Thus, we provide evidence that linear diagrams are the most effective of these four diagram types for representing sets.

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Section: Introductionmentioning

confidence: 99%

Visualizing Sets: An Empirical Comparison of Diagram Types

Chapman

Rodgers

Micallef

et al. 2014

Diagrammatic Representation and Inference

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“…Parallel Bargrams (Wittenburg, Lanning, Heinrichs, & Stanton, 2001) and SimulSort (Hur & Yi, 2009) are examples of such variations. By sorting values in all columns, a user can see overall trends more easily without changing sorting orders.…”

Section: Tabular Visualizationsmentioning

confidence: 99%

“…Wittenburg et al (2001) reported that the participants selecting an item from a menu of multiple items generally preferred EZChooser, an implementation of Parallel Bargrams, over a spreadsheet application. Table Lens was also compared with S-PLUS in terms of exploratory data analysis (EDA) using the GOMS (Goals, Operators, Methods, and Selection rules) method (Pirolli & Rao, 1996).…”

Section: Evaluation Of Tabular Visualizationsmentioning

confidence: 99%

“…Wittenburg et al (2001) only reported participants' subjective ratings, so it is difficult to know whether Parallel Bargrams actually improved decision quality, or if it was highly rated because of novelty effects. The evaluation should be designed to capture if the visualization is actually influencing the information process of individuals (Carswell, 1992).…”

Section: Evaluation Of Tabular Visualizationsmentioning

confidence: 99%

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A Comparative Study of Three Sorting Techniques in Performing Cognitive Tasks on a Tabular Representation

Hur

Kim

Samak

et al. 2013

International Journal of Human-Computer Interaction

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We investigated the impacts of three sorting techniques on various cognitive tasks performed on a tabular representation. The tasks under study were a multi-attribute object selection task and selected low-level analytic tasks.Three sorting techniques, including sorting by a column (Typical Sort: TS), sorting by all columns simultaneously (SimulSort: SS), and sorting by all columns with faithful vertical locations (ParallelTable: PT), were compared with a static table without the sorting feature (Baseline: B). An incentivized controlled laboratory study with 80 participants and a preliminary eye-tracker study were conducted to better understand the strengths and weaknesses of the four different approaches. We found that SimulSort and ParallelTable significantly improved the performance of multi-attribute object selection. ParallelTable, however, suffers from an occlusion problem, so it is not an appropriate support for some low-level analytic tasks. We used the findings to propose appropriate sorting techniques for specific tasks performed on a table.

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“…Besides presenting a visualization that clusters similar antennas, the system also affords users the opportunity to explore the tradeoffs between the antennas' numeric performance measures [2]. Each of the sliders in Figure 4 corresponds to one dimension in the performance vector.…”

Section: Figure 2 the Dispersion Processmentioning

confidence: 99%

Semi-automatic antenna design via sampling and visualization

Quigley¹,

Leigh²,

Lesh³

et al.

IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313)

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Optimization-based approaches to antenna design have enjoyed limited success. The task is often computationally intractable. Moreover, it is also often difficult to capture all relevant design issues and tradeoffs in a single mathematical objective function. Therefore, human experts typically specify and refine antenna designsby hand,üsing computers only to evaluate their candidate designs by simulation. In this paper we propose a middle ground between this traditional approach and fully automatic optimization. We use computation to sample a space of possible antenna designs, and we rely on human judgment to select good designs from the samples returned by the computer. The key elements of our computer system are a parallel algorithm for intelligently sampling a space of possible antenna designs and a graphical user interface for visualizing and exploring candidate designs and managing the sampling process. IEEE AP-S Intl SymposiumThis 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. AbstractOptimization-based approaches to antenna design have enjoyed limited success. The task is often computationally intractable. Moreover, it is also often difficult to capture all relevant design issues and tradeoffs in a single mathematical objective function. Therefore, human experts typically specify and refine antenna designs "by hand," using computers only to evaluate their candidate designs by simulation. In this paper we propose a middle ground between this traditional approach and fully automatic optimization. We use computation to sample a space of possible antenna designs, and we rely on human judgment to select good designs from the samples returned by the computer. The key elements of our computer system are a parallel algorithm for intelligently sampling a space of possible antenna designs and a graphical user interface for visualizing and exploring candidate designs and managing the sampling process. IntroductionThe idea of using computer-based optimization for design tasks has been applied to many problems, including antenna design. However, this idea does not always work well: the optimization problems are often intractable; and it often proves impossible to consider all relevant design criteria in the optimization process. In this paper we propose that the computer be used differently. Instead of having the computer search for a single optimal design, we program it to intelligently samp...

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Parallel bargrams for consumer-based information exploration and choice

Cited by 57 publications

References 17 publications

Visualizing Sets: An Empirical Comparison of Diagram Types

Visualizing Sets: An Empirical Comparison of Diagram Types

A Comparative Study of Three Sorting Techniques in Performing Cognitive Tasks on a Tabular Representation

Semi-automatic antenna design via sampling and visualization

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