Interactive data analysis tool by augmenting MATLAB with semantic objects

Changhyun Lee; Jaegul Choo; Duen Horng Polo Chau; Haesun Park

doi:10.1109/ICDMW.2013.167

Interactive data analysis tool by augmenting MATLAB with semantic objects

Changhyun Lee, Jaegul Choo, Duen Horng Polo Chau, Haesun Park

Research output: Contribution to conference › Paper › peer-review

Abstract

The traditional data analysis tools support strong computational capabilities and numerous standard visualization techniques. However, they provide little visual interactions due to the fact that the tools maintain a wide applicability to diverse data domains, and thus any inherent meanings associated with the data domains are hardly allowed. To cover these limitations, we propose to augment Mat lab, one of the widely used data analysis tools and computational languages, by imposing the capabilities of handling semantic objects so that diverse essential interaction capabilities could be allowed such as brushing-and-linking, details-on-demand, and dynamic interactive updating on visualization. In our demonstration, we will show our audience how to import semantic data, how visual interactions are occurred, and how these functionalities are convenient using the movie similarity graph data set.

Original language	English
Pages	1093-1096
Number of pages	4
DOIs	https://doi.org/10.1109/ICDMW.2013.167
Publication status	Published - 2013
Event	2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 - Dallas, TX, United States Duration: 2013 Dec 7 → 2013 Dec 10

Conference

Conference	2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013
Country/Territory	United States
City	Dallas, TX
Period	13/12/7 → 13/12/10

Keywords

Clustering dimension reduction
Interactive visualization
Visual analytics

ASJC Scopus subject areas

Software

Access to Document

10.1109/ICDMW.2013.167

Cite this

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abstract = "The traditional data analysis tools support strong computational capabilities and numerous standard visualization techniques. However, they provide little visual interactions due to the fact that the tools maintain a wide applicability to diverse data domains, and thus any inherent meanings associated with the data domains are hardly allowed. To cover these limitations, we propose to augment Mat lab, one of the widely used data analysis tools and computational languages, by imposing the capabilities of handling semantic objects so that diverse essential interaction capabilities could be allowed such as brushing-and-linking, details-on-demand, and dynamic interactive updating on visualization. In our demonstration, we will show our audience how to import semantic data, how visual interactions are occurred, and how these functionalities are convenient using the movie similarity graph data set.",

keywords = "Clustering dimension reduction, Interactive visualization, Visual analytics",

author = "Changhyun Lee and Jaegul Choo and Chau, {Duen Horng Polo} and Haesun Park",

year = "2013",

doi = "10.1109/ICDMW.2013.167",

language = "English",

pages = "1093--1096",

note = "2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 ; Conference date: 07-12-2013 Through 10-12-2013",

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AU - Choo, Jaegul

AU - Chau, Duen Horng Polo

AU - Park, Haesun

PY - 2013

Y1 - 2013

N2 - The traditional data analysis tools support strong computational capabilities and numerous standard visualization techniques. However, they provide little visual interactions due to the fact that the tools maintain a wide applicability to diverse data domains, and thus any inherent meanings associated with the data domains are hardly allowed. To cover these limitations, we propose to augment Mat lab, one of the widely used data analysis tools and computational languages, by imposing the capabilities of handling semantic objects so that diverse essential interaction capabilities could be allowed such as brushing-and-linking, details-on-demand, and dynamic interactive updating on visualization. In our demonstration, we will show our audience how to import semantic data, how visual interactions are occurred, and how these functionalities are convenient using the movie similarity graph data set.

AB - The traditional data analysis tools support strong computational capabilities and numerous standard visualization techniques. However, they provide little visual interactions due to the fact that the tools maintain a wide applicability to diverse data domains, and thus any inherent meanings associated with the data domains are hardly allowed. To cover these limitations, we propose to augment Mat lab, one of the widely used data analysis tools and computational languages, by imposing the capabilities of handling semantic objects so that diverse essential interaction capabilities could be allowed such as brushing-and-linking, details-on-demand, and dynamic interactive updating on visualization. In our demonstration, we will show our audience how to import semantic data, how visual interactions are occurred, and how these functionalities are convenient using the movie similarity graph data set.

KW - Clustering dimension reduction

KW - Interactive visualization

KW - Visual analytics

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DO - 10.1109/ICDMW.2013.167

M3 - Paper

AN - SCOPUS:84898047031

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T2 - 2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013

Y2 - 7 December 2013 through 10 December 2013

ER -

Interactive data analysis tool by augmenting MATLAB with semantic objects

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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