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

Data Science Journal

Stuart E Middleton²  Peter Löwe³  Martin Hammitzsch³  Stefan Poslad¹  Zoheir A Sabeur²  Siamak Tavakoli¹ 

[1] Queen Mary University of London, UK;University of Southampton IT Innovation Centre, UK;GFZ German Research Centre for Geosciences, Germany

关键词: Natural disaster;    Tsunami;    Semantics;    Data fusion;    OGC;    W3C;    TRIDEC;   

DOI  :  10.2481/dsj.WDS-018

学科分类：计算机科学（综合）

来源: Ubiquity Press Ltd.

【 摘 要 】

References(19)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.

【 授权许可】

Unknown