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Journal of Visualization

Erschienen in:

06.10.2018 | Regular Paper

Toward the better modeling and visualization of uncertainty for streaming data

verfasst von: Tan Tang, Kaijuan Yuan, Junxiu Tang, Yingcai Wu

Erschienen in: Journal of Visualization | Ausgabe 1/2019

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Abstract

Streaming data can be found in many different scenarios, in which data are generated and arriving continuously. Sampling approaches have been proven as an effective means to cope with the sheer volume of the streaming data. However, sampling methods also introduce uncertainty, which can affect the reliability of subsequent analysis and visualization. In this paper, we propose a novel model called PDm and visualization named uncertainty tree to present uncertainty that arises from sampling streaming data. PDm is first introduced to characterize uncertainty of streaming data, and an optimization method is then proposed to minimize uncertainty. Uncertainty tree is further developed to enhance data understanding by visualizing uncertainty and revealing temporal patterns of streaming data. Lastly, a quantitative evaluation and real-world examples have been conducted to demonstrate the effectiveness and efficacy of the proposed techniques.

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Vorheriger Artikel A CNN-based vortex identification method

Nächster Artikel A novel in situ compression method for CFD data based on generative adversarial network

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Titel: Toward the better modeling and visualization of uncertainty for streaming data
verfasst von: Tan Tang
Kaijuan Yuan
Junxiu Tang
Yingcai Wu
Publikationsdatum: 06.10.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Visualization / Ausgabe 1/2019
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI: https://doi.org/10.1007/s12650-018-0518-y

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