StreamVisND: Visualizing relationships in streaming multivariate data

Shenghui Cheng; Yue Wang; Dan Zhang; Zhifang Jiang; Klaus Mueller

doi:10.1109/VAST.2015.7347673

StreamVis^ND: Visualizing relationships in streaming multivariate data

Shenghui Cheng
, Yue Wang
, Dan Zhang
, Zhifang Jiang
, Klaus Mueller

Computer Science

Stony Brook University
Shandong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In streaming acquisitions the data changes over time. ThemeRiver and line charts are common methods to display data over time. However, these methods can only show the values of the variables (or attributes) but not the relationships among them over time. We propose a framework we call StreamVis^ND that can display these types of streaming data relations. It first slices the data stream into different time slices, then it visualizes each slice with a sequence of multivariate 2D data layouts, and finally it flattens this series of displays into a parallel coordinate type display. Our framework is fully interactive and lends itself well to real-time displays.

Original language	English
Title of host publication	2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
Editors	Min Chen, Gennady Andrienko
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	191-192
Number of pages	2
ISBN (Electronic)	9781467397834
DOIs	https://doi.org/10.1109/VAST.2015.7347673
State	Published - Dec 4 2015
Event	10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Chicago, United States Duration: Oct 25 2015 → Oct 30 2015

Publication series

Name	2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings

Conference

Conference	10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015
Country/Territory	United States
City	Chicago
Period	10/25/15 → 10/30/15

Access to Document

10.1109/VAST.2015.7347673

Cite this

Cheng, S., Wang, Y., Zhang, D., Jiang, Z., & Mueller, K. (2015). StreamVis^ND: Visualizing relationships in streaming multivariate data. In M. Chen, & G. Andrienko (Eds.), 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings (pp. 191-192). Article 7347673 (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2015.7347673

Cheng, Shenghui ; Wang, Yue ; Zhang, Dan et al. / StreamVis^ND : Visualizing relationships in streaming multivariate data. 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. editor / Min Chen ; Gennady Andrienko. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 191-192 (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings).

@inproceedings{3c63bcf423a64cd5b7b47b023cd0c295,

title = "StreamVisND: Visualizing relationships in streaming multivariate data",

abstract = "In streaming acquisitions the data changes over time. ThemeRiver and line charts are common methods to display data over time. However, these methods can only show the values of the variables (or attributes) but not the relationships among them over time. We propose a framework we call StreamVisND that can display these types of streaming data relations. It first slices the data stream into different time slices, then it visualizes each slice with a sequence of multivariate 2D data layouts, and finally it flattens this series of displays into a parallel coordinate type display. Our framework is fully interactive and lends itself well to real-time displays.",

author = "Shenghui Cheng and Yue Wang and Dan Zhang and Zhifang Jiang and Klaus Mueller",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015 ; Conference date: 25-10-2015 Through 30-10-2015",

year = "2015",

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doi = "10.1109/VAST.2015.7347673",

language = "English",

series = "2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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editor = "Min Chen and Gennady Andrienko",

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Cheng, S, Wang, Y, Zhang, D, Jiang, Z & Mueller, K 2015, StreamVis^ND: Visualizing relationships in streaming multivariate data. in M Chen & G Andrienko (eds), 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings., 7347673, 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 191-192, 10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015, Chicago, United States, 10/25/15. https://doi.org/10.1109/VAST.2015.7347673

StreamVis^ND: Visualizing relationships in streaming multivariate data. / Cheng, Shenghui; Wang, Yue; Zhang, Dan et al.
2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. ed. / Min Chen; Gennady Andrienko. Institute of Electrical and Electronics Engineers Inc., 2015. p. 191-192 7347673 (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In streaming acquisitions the data changes over time. ThemeRiver and line charts are common methods to display data over time. However, these methods can only show the values of the variables (or attributes) but not the relationships among them over time. We propose a framework we call StreamVisND that can display these types of streaming data relations. It first slices the data stream into different time slices, then it visualizes each slice with a sequence of multivariate 2D data layouts, and finally it flattens this series of displays into a parallel coordinate type display. Our framework is fully interactive and lends itself well to real-time displays.

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Cheng S, Wang Y, Zhang D, Jiang Z, Mueller K. StreamVis^ND: Visualizing relationships in streaming multivariate data. In Chen M, Andrienko G, editors, 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 191-192. 7347673. (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings). doi: 10.1109/VAST.2015.7347673