Customizing computational methods for visual analytics with big data

Jaegul Choo; Haesun Park

doi:10.1109/MCG.2013.39

Customizing computational methods for visual analytics with big data

Jaegul Choo, Haesun Park

Research output: Contribution to journal › Article › peer-review

49 Citations (Scopus)

Abstract

The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.

Original language	English
Article number	6506085
Pages (from-to)	22-28
Number of pages	7
Journal	IEEE Computer Graphics and Applications
Volume	33
Issue number	4
DOIs	https://doi.org/10.1109/MCG.2013.39
Publication status	Published - 2013

Keywords

big data
clustering
computer graphics
dimension reduction
iteration-level visualization
large-scale data
low-precision computation
visual analytics

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

Access to Document

10.1109/MCG.2013.39

Cite this

@article{09097cf08c434df5a68f0d513ff31274,

title = "Customizing computational methods for visual analytics with big data",

abstract = "The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.",

keywords = "big data, clustering, computer graphics, dimension reduction, iteration-level visualization, large-scale data, low-precision computation, visual analytics",

author = "Jaegul Choo and Haesun Park",

year = "2013",

doi = "10.1109/MCG.2013.39",

language = "English",

volume = "33",

pages = "22--28",

journal = "IEEE Computer Graphics and Applications",

issn = "0272-1716",

publisher = "IEEE Computer Society",

number = "4",

}

TY - JOUR

T1 - Customizing computational methods for visual analytics with big data

AU - Choo, Jaegul

AU - Park, Haesun

PY - 2013

Y1 - 2013

N2 - The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.

AB - The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.

KW - big data

KW - clustering

KW - computer graphics

KW - dimension reduction

KW - iteration-level visualization

KW - large-scale data

KW - low-precision computation

KW - visual analytics

UR - http://www.scopus.com/inward/record.url?scp=84880820335&partnerID=8YFLogxK

U2 - 10.1109/MCG.2013.39

DO - 10.1109/MCG.2013.39

M3 - Article

AN - SCOPUS:84880820335

SN - 0272-1716

VL - 33

SP - 22

EP - 28

JO - IEEE Computer Graphics and Applications

JF - IEEE Computer Graphics and Applications

IS - 4

M1 - 6506085

ER -

Customizing computational methods for visual analytics with big data

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this