Design Choices when Architecting Visualizations

Tang, Dajun; Stolte, Chris; Bosch, Robert

doi:10.1057/palgrave.ivs.9500067

Cited by 16 publications

(5 citation statements)

References 22 publications

(34 reference statements)

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“…More flexible end user desktop visualization applications include Spotfire [21] and Tableau [23], which both offer advanced data mapping paradigms. Many Eyes may be most similar to the systems of [17] and [24] that provide a number of commonly used visualizations with predefined slots and map data tuples to slots.…”

Section: Related Work-end User Construction Ofmentioning

confidence: 99%

ManyEyes: a Site for Visualization at Internet Scale

Viégas

Wattenberg

Ham

et al. 2007

IEEE Trans. Visual. Comput. Graphics

576

382

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Abstract-We describe the design and deployment of Many Eyes, a public web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around visualizations at a large scale by fostering a social style of data analysis in which visualizations not only serve as a discovery tool for individuals but also as a medium to spur discussion among users. To support this goal, the site includes novel mechanisms for end-user creation of visualizations and asynchronous collaboration around those visualizations. In addition to describing these technologies, we provide a preliminary report on the activity of our users.

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Section: Related Work-end User Construction Ofmentioning

confidence: 99%

ManyEyes: a Site for Visualization at Internet Scale

Viégas

Wattenberg

Ham

et al. 2007

IEEE Trans. Visual. Comput. Graphics

576

382

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show abstract

“…and to the final presentation of the graph should be managed. For this we wish to consider some experience and suggestions from more literature (Atkinson et al, 2013;Tang et al, 2004).…”

Section: Future Developmentmentioning

confidence: 99%

New Tool for Visualization of Time Series and Anomalies in Streaming Data

Otáhal¹,

Štěpánková²

2016

Acta Univ. Agric. Silvic. Mendelianae Brun.

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A new software tool for simultaneous visualization of multiple time dependent signals, featuring a novel and highly useful combination of capabilities, and published as an open source solution is presented in this paper. The tool is designed to meet the needs of its users who expect lightweight, interactive & intuitive use and ease of deployment in current setups, including live monitoring systems with anomaly detection, highlighting and streaming data processing abilities. The functionality and motivation for our system is derived from various signal analysis applications, our research activities related to design and evaluation of neural network models, and from systems for continuous monitoring and anomaly detection (e.g. in IT or medical domains), which is demonstrated on simple use case examples.

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“…Constraining dashboards to display views of the same dataset (propagating the filters) limits the expressibility of multiple views, but increases usability as it helps manipulating data attributes. Limiting expressibility (e.g., each multiple view must display aspects of the same species population) in favor of usability is reasonable for an audience of non-expert users [19]. Situation Awareness -Endsley distinguishes 3 levels in situation awareness related to information processing [9], which echo visual analytical tasks (locate and associate information [2,18,22]): Perception of cues, occurring when information is simply read, without further interpretation or correlation; Comprehension, i.e., integrating multiple pieces of information ; and Projection, i.e., forecasting elements of an unknown situation (e.g., future events, interpretations of uncertain data).…”

Section: Uncertainty In Specific Outputmentioning

confidence: 99%

Supporting Non-Experts' Awareness of Uncertainty

Beauxis-Aussalet

Arslanova

Hardman

2015

Proceedings of the European Conference on Cognitive Ergonomics 2015

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Video analysis tools can provide valuable datasets for a wide range of applications, such as monitoring animal populations for ecology research, while reducing human efforts for collecting information. Transferring such technology to novel application domains implies exposing non-expert users to unfamiliar datasets and technical concepts. Existing data analysis practices must adapt to the new data characteristics and technical constraints. With such changes, uncertainty is of major concern as it can yield misinterpretation of data, or distrust and rejection of valid results. We present a study of an interactive visualization of computer vision results and uncertainty. We evaluate the correctness of users' interpretation of data, and their confidence in their interpretation. We compare the impact of either data features (i.e., the true level of uncertainty) or visualization features on user perception of uncertainty. Visualization features had a similar impact on user responses than the data uncertainty itself, thus biasing user awareness of uncertainty. We conclude with the opportunities (intuitive navigation in complex unfamiliar data) and limitations (poor extrapolation and memory loss) of our visualization design which integrates simple graphs in coordinated multiple views. Our design and insights contribute to other cases where non-experts need to familiarize with novel datasets and explore their uncertainty.

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Design Choices when Architecting Visualizations

Cited by 16 publications

References 22 publications

ManyEyes: a Site for Visualization at Internet Scale

ManyEyes: a Site for Visualization at Internet Scale

New Tool for Visualization of Time Series and Anomalies in Streaming Data

Supporting Non-Experts' Awareness of Uncertainty

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