Sign In

Communications of the ACM

ACM TechNews

New U.s.-Japan Collaborations Bring Big Data Approaches to Disaster Response

View as: Print Mobile App Share:
USC's spatial crowdsourcing platform, dubbed MediaQ.

The U.S. National Science Foundation and Japan's Science and Technology Agency will jointly support six projects to improve future disaster management

Credit: Cyrus Shahabi, University of Southern California

The U.S. National Science Foundation and the Japan Science and Technology Agency announced they will jointly fund six collaborative big data projects to enhance future disaster management.

The projects target solutions to the challenges of capturing and processing disaster-associated data and improving the resilience and responsiveness of emerging computer systems and networks in the face of catastrophes to enable real-time data analytics in their aftermath.

One project will entail researchers designing a computer platform for decision-makers to employ during disasters to analyze incoming data and coordinate responses. Another will focus on smartphone-based emergency communications networks that evolve dynamically, and a third will investigate resilient networks, social-media mining, and information dissemination during disasters. New techniques to compress, transmit, and query sensor network data for disaster applications also will be developed, as will olfactory search algorithms that use sensors to trace the source of airborne or seaborne pollutants or other hazardous agents. The sixth joint project will involve the design of context-aware and user-specific information-delivery systems deployed during disasters to provide citizens with accurate information.

Recent studies suggest big data and data analytics for disaster management will need new strategies for analyzing heterogeneous data so timely decisions can be made amid fluctuating demands.

From National Science Foundation
View Full Article


Abstracts Copyright © 2015 Information Inc., Bethesda, Maryland, USA


No entries found

Sign In for Full Access
» Forgot Password? » Create an ACM Web Account