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Proceedings Papers

Multi-Disciplinary Approaches to Intelligently Sharing Large-Volumes of Real-Time Sensor Data During Natural Disasters

Authors:

Stuart E Middleton ,

University of Southampton IT Innovation Centre, UK, GB
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Zoheir A Sabeur,

University of Southampton IT Innovation Centre, UK, GB
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Peter Löwe,

GFZ German Research Centre for Geosciences, Germany, DE
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Martin Hammitzsch,

GFZ German Research Centre for Geosciences, Germany, DE
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Siamak Tavakoli,

Queen Mary University of London, UK, GB
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Stefan Poslad

Queen Mary University of London, UK, GB
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Abstract

We describe our knowledge-based service architecture for multi-risk environmental decision-support, capable of handling geo-distributed heterogeneous real-time data sources. Data sources include tide gauges, buoys, seismic sensors, satellites, earthquake alerts, Web 2.0 feeds to crowd source 'unconventional' measurements, and simulations of Tsunami wave propagation. Our system of systems multi-bus architecture provides a scalable and high performance messaging backbone. We are overcoming semantic interoperability between heterogeneous datasets by using a self-describing 'plug-in' data source approach. As crises develop we can agilely steer the processing server and adapt data fusion and mining algorithm configurations in real-time.

DOI: http://doi.org/10.2481/dsj.WDS-018
How to Cite: Middleton, S.E. et al., (2013). Multi-Disciplinary Approaches to Intelligently Sharing Large-Volumes of Real-Time Sensor Data During Natural Disasters. Data Science Journal. 12, pp.WDS109–WDS113. DOI: http://doi.org/10.2481/dsj.WDS-018
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Published on 24 Feb 2013.
Peer Reviewed

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