Model-driven visual analytics for big data

Shenghui Cheng; Bing Wang; Wen Zhong; Cong Xie; Salman Mahmood; Jun Wang; Klaus Mueller

doi:10.1109/NYSDS.2016.7747827

Model-driven visual analytics for big data

Shenghui Cheng
, Bing Wang
, Wen Zhong
, Cong Xie
, Salman Mahmood
, Jun Wang
, Klaus Mueller

Computer Science

Stony Brook University

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

3 Scopus citations

Abstract

The growth of digital data is tremendous. Any aspect of life and matter is being recorded and stored on cheap disks, either in the cloud, in businesses, or in research labs. We can now afford to explore very complex relationships with many variables playing a part. But for this we need powerful tools that allow us to be creative, to sculpt this intricate insight formulated as models from the raw block of data. High-quality visual feedback plays a decisive role here. The subject of this poster is a framework we have developed over the years to make the exploration of large multivariate data more intuitive and direct. The components of this framework were conceived in tight collaborations with domain experts in the fields of climate science, health informatics, computer systems, and others.

Original language	English
Title of host publication	2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467390514
DOIs	https://doi.org/10.1109/NYSDS.2016.7747827
State	Published - Nov 17 2016
Event	2016 New York Scientific Data Summit, NYSDS 2016 - New York, United States Duration: Aug 14 2016 → Aug 17 2016

Publication series

Name	2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings

Conference

Conference	2016 New York Scientific Data Summit, NYSDS 2016
Country/Territory	United States
City	New York
Period	08/14/16 → 08/17/16

Keywords

data science
high-dimensional data
visualization

Access to Document

10.1109/NYSDS.2016.7747827

Cite this

Cheng, S., Wang, B., Zhong, W., Xie, C., Mahmood, S., Wang, J., & Mueller, K. (2016). Model-driven visual analytics for big data. In 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings Article 7747827 (2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NYSDS.2016.7747827

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title = "Model-driven visual analytics for big data",

abstract = "The growth of digital data is tremendous. Any aspect of life and matter is being recorded and stored on cheap disks, either in the cloud, in businesses, or in research labs. We can now afford to explore very complex relationships with many variables playing a part. But for this we need powerful tools that allow us to be creative, to sculpt this intricate insight formulated as models from the raw block of data. High-quality visual feedback plays a decisive role here. The subject of this poster is a framework we have developed over the years to make the exploration of large multivariate data more intuitive and direct. The components of this framework were conceived in tight collaborations with domain experts in the fields of climate science, health informatics, computer systems, and others.",

keywords = "data science, high-dimensional data, visualization",

author = "Shenghui Cheng and Bing Wang and Wen Zhong and Cong Xie and Salman Mahmood and Jun Wang and Klaus Mueller",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 New York Scientific Data Summit, NYSDS 2016 ; Conference date: 14-08-2016 Through 17-08-2016",

year = "2016",

month = nov,

day = "17",

doi = "10.1109/NYSDS.2016.7747827",

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series = "2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings",

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Cheng, S, Wang, B, Zhong, W, Xie, C, Mahmood, S, Wang, J & Mueller, K 2016, Model-driven visual analytics for big data. in 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings., 7747827, 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2016 New York Scientific Data Summit, NYSDS 2016, New York, United States, 08/14/16. https://doi.org/10.1109/NYSDS.2016.7747827

Model-driven visual analytics for big data. / Cheng, Shenghui; Wang, Bing; Zhong, Wen et al.
2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7747827 (2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings).

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

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T1 - Model-driven visual analytics for big data

AU - Cheng, Shenghui

AU - Wang, Bing

AU - Zhong, Wen

AU - Xie, Cong

AU - Mahmood, Salman

AU - Wang, Jun

AU - Mueller, Klaus

PY - 2016/11/17

Y1 - 2016/11/17

N2 - The growth of digital data is tremendous. Any aspect of life and matter is being recorded and stored on cheap disks, either in the cloud, in businesses, or in research labs. We can now afford to explore very complex relationships with many variables playing a part. But for this we need powerful tools that allow us to be creative, to sculpt this intricate insight formulated as models from the raw block of data. High-quality visual feedback plays a decisive role here. The subject of this poster is a framework we have developed over the years to make the exploration of large multivariate data more intuitive and direct. The components of this framework were conceived in tight collaborations with domain experts in the fields of climate science, health informatics, computer systems, and others.

AB - The growth of digital data is tremendous. Any aspect of life and matter is being recorded and stored on cheap disks, either in the cloud, in businesses, or in research labs. We can now afford to explore very complex relationships with many variables playing a part. But for this we need powerful tools that allow us to be creative, to sculpt this intricate insight formulated as models from the raw block of data. High-quality visual feedback plays a decisive role here. The subject of this poster is a framework we have developed over the years to make the exploration of large multivariate data more intuitive and direct. The components of this framework were conceived in tight collaborations with domain experts in the fields of climate science, health informatics, computer systems, and others.

KW - data science

KW - high-dimensional data

KW - visualization

UR - https://www.scopus.com/pages/publications/85006854953

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DO - 10.1109/NYSDS.2016.7747827

M3 - Conference contribution

AN - SCOPUS:85006854953

T3 - 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings

BT - 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 New York Scientific Data Summit, NYSDS 2016

Y2 - 14 August 2016 through 17 August 2016

ER -

Model-driven visual analytics for big data

Abstract

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Keywords

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