A comparison of two display models for collaborative sensemaking

Chung, Haeyong; Chu, Sharon Lynn; North, Chris

doi:10.1145/2491568.2491577

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The WeSpace project at MERL also explored this style of interaction, where mobile devices were used around a digital table, and screen content was shared on a large vertical wall [33]. Similarly , VisPorter provides a platform for collaborative sensemaking across large displays and mobile devices [7].…”

Section: Small and Large Displaysmentioning

confidence: 99%

Conductor

Hamilton

Wigdor

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

128

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The proliferation of inexpensive connected devices has created a situation where a person, at any given moment, is surrounded by interactive computers. Despite this fact, there are very few means by which a user may take advantage of this large number of screens. We present Conductor, a prototype framework which serves as an exemplar for the construction of cross-device applications. We present a series of interaction methods by which users can easily share information, chain tasks across devices, and manage sessions across devices. We also present a cross-device usage scenario which utilizes several cross -device applications built within our prototype framework. We also describe a user study, which helped us to understand how users will take advantage of a large number of devices in support of performance of a sense making task.

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Section: Small and Large Displaysmentioning

confidence: 99%

Conductor

Hamilton

Wigdor

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

128

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“…In using multi-viewed business intelligence (BI) or visual analytics (VA) systems, analysts often expand a scatterplot of interest in thumbnail views to the main view to conduct in-depth exploration. Likewise, sharing a scatterplot across various displays of different sizes has nearly become a routine operation for communicating findings in collaborative data analysis [7,17] (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots

Wei

Mei

Zhao

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.

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Shaping Unlimited Patterns

Bieh-Zimmert

Felden

2014

Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

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A comparison of two display models for collaborative sensemaking

Cited by 3 publications

References 9 publications

Conductor

Conductor

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots

Shaping Unlimited Patterns

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