Visualizing chemicals detection VAST 2017 Mini Challenge 2

Visualizing chemicals detection VAST 2017 Mini Challenge 2

Analysis of sensor data is currently a popular topic in visual analytics, as visual representations can efficiently uncover insights in huge and noisy datasets.

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Bibliographic Details
Main Authors: ZHANG, Jiaqi, LIU, Ximeng, QIAN, Hongjun, KAM, Tin Seong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Chemical
Data visualizing
Visual analytics
Production facilities
Tools
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/6823
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Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-7826
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spelling sg-smu-ink.sis_research-78262022-01-27T03:48:03Z Visualizing chemicals detection VAST 2017 Mini Challenge 2 ZHANG, Jiaqi LIU, Ximeng QIAN, Hongjun KAM, Tin Seong Analysis of sensor data is currently a popular topic in visual analytics, as visual representations can efficiently uncover insights in huge and noisy datasets. 2017-10-06T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/6823 info:doi/10.1109/VAST.2017.8585723 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Chemical Data visualizing Visual analytics Production facilities Tools Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Chemical
Data visualizing
Visual analytics
Production facilities
Tools
Databases and Information Systems
spellingShingle Chemical
Data visualizing
Visual analytics
Production facilities
Tools
Databases and Information Systems
ZHANG, Jiaqi
LIU, Ximeng
QIAN, Hongjun
KAM, Tin Seong
Visualizing chemicals detection VAST 2017 Mini Challenge 2
description Analysis of sensor data is currently a popular topic in visual analytics, as visual representations can efficiently uncover insights in huge and noisy datasets.
format text
author ZHANG, Jiaqi
LIU, Ximeng
QIAN, Hongjun
KAM, Tin Seong
author_facet ZHANG, Jiaqi
LIU, Ximeng
QIAN, Hongjun
KAM, Tin Seong
author_sort ZHANG, Jiaqi
title Visualizing chemicals detection VAST 2017 Mini Challenge 2
title_short Visualizing chemicals detection VAST 2017 Mini Challenge 2
title_full Visualizing chemicals detection VAST 2017 Mini Challenge 2
title_fullStr Visualizing chemicals detection VAST 2017 Mini Challenge 2
title_full_unstemmed Visualizing chemicals detection VAST 2017 Mini Challenge 2
title_sort visualizing chemicals detection vast 2017 mini challenge 2
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/6823
_version_ 1770576076152504320

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