NDLI: A collaborative large spatio-temporal data visual analytics architecture for emergence response

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A collaborative large spatio-temporal data visual analytics architecture for emergence response

Content Provider	Scilit
Author	Guo, D. Li, J. Cao, H. Zhou, Y.
Copyright Year	2014
Description	Journal: Iop Conference Series: Earth and Environmental Science The unconventional emergency, usually outbreaks more suddenly, and is diffused more quickly, but causes more secondary damage and derives more disaster than what it is usually expected. The data volume and urgency of emergency exceeds the capacity of current emergency management systems. In this paper, we propose a three-tier collaborative spatio-temporal visual analysis architecture to support emergency management. The prototype system, based on cloud computation environment, supports aggregation of massive unstructured and semi-structured data, integration of various computing model sand algorithms; collaborative visualization and visual analytics among users with a diversity of backgrounds. The distributed data in 100TB scale is integrated in a unified platform and shared with thousands of experts and government agencies by nearly 100 models. The users explore, visualize and analyse the big data and make a collaborative countermeasures to emergencies.
Related Links	http://iopscience.iop.org/article/10.1088/1755-1315/18/1/012129/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/18/1/012129
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	1
Volume Number	18
Language	English
Publisher	IOP Publishing
Publisher Date	2014-02-25
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Computer Science Data Visual Collaborative Large Large Spatio Analytics Architecture Temporal Data Emergence Response Visual Analytics
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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