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

• Published in: IOP conference series. Earth and environmental science Vol. 18; no. 1; pp. 12129 - 5
• Main Authors: Guo, D, Li, J, Cao, H, Zhou, Y
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.01.2014
• Subjects: Algorithms; Analytics; Architecture; Cloud computing; Collaboration; Computation; Data analysis; Emergencies; Emergency management; Emergency preparedness; Emergency response; Environmental management; Government agencies; Management systems; Sand; Spatiotemporal data; Unstructured data; Visual
• ISSN: 1755-1315; 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/18/1/012129

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.