文章基本信息

标题：Multi-Disciplinary Approaches to Intelligently Sharing Large-Volumes of Real-Time Sensor Data During Natural Disasters
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作者：Stuart Middleton ; Zoheir Sabeur ; Peter Löwe 等
期刊名称：Data Science Journal
电子版ISSN：1683-1470
出版年度：2015
卷号：12
DOI：10.2481/dsj.WDS-018
语种：English
出版社：Ubiquity Press
摘要：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.
关键词：Natural disaster; Tsunami; Semantics; Data fusion; OGC; W3C; TRIDEC