||Human-Centered Situation Awareness Platform for Disaster Response and Recovery
||Associate Professor, Digital Content and Media Sciences Research Division, National Institute of Informatics
The project aims at solving the problem of information scarcity and overloads that decision makers need to tackle with during disaster response, and developing human-centered situational awareness platform to support collection, analysis, and sharing of information among stakeholders.
The Japanese team focuses on two challenges; 1) an analytics engine that routes information streams by three dimensions of space, time and theme, 2) a platform for improving situational awareness by seamless communication among offsite disaster personnel and onsite commander and crowd workers. The US team focuses on two challenges: 1) timely and effective collection of imagery and video data by spatial crowdsourcing using mobile devices, 2) automatic spatiotemporal annotation and indexing of images and videos collected by spatial crowdsourcing.
Roles of two teams are complementary, and the integration of developed tools through collaboration is expected to produce a situational awareness prototype that improves decision making process during disaster response and recovery.
||Director, Integrated Media Systems Center, University of Southern California
||Efficient and Scalable Collection, Analytics and Processing of Big Data for Disaster Applications
||Associate Professor, Graduate School of Information Science and Technology, Osaka University
||This project aims to develop fundamental technologies of collection, analytics, and processing of big data for disaster applications. Specifically, it will address the four issues; (i) collaborative compression of highly multi-dimensional data from multi-streams, (ii) indexing of highly multi-dimensional data, (iii) monitoring of highly multi-dimensional data generated from multiple data sources, and (iv) applications of microblog for social sensing.
The issues (i) and (ii) will be addressed mainly by the US team during the first and second fiscal years, while (iii) and (iv) mainly by the Japan team during the first to third fiscal years. In the third year, both teams will collaboratively work on implementation of the proposed technologies and experiments on a practical platform.
Since the principal investigators of the two teams have complementary expertise in the research topics which are essential to conduct this project, significant synergistic effects are expected by their collaboration.
|Sanjay Kumar MADRIA
||Professor, Department of Computer Science, Missouri University of Science and Technology
||A Big-Data Computational Laboratory for the Optimization of Olfactory Search Algorithms in Turbulent Environments
||Lecturer, Institute of Industrial Science, The University of Tokyo
||In the present project, we develop efficient algorithms for scalar source searching in turbulent environments based on finite and noisy sensing information.
Specifically, the US side will conduct large-scale direct numerical simulation of canonical turbulent flows, i.e., homogeneous turbulence, fully developed turbulent channel flow, and turbulent boundary layer. In the simulated turbulent environments, a scalar source is placed at different locations and the resultant spatio-temporal evolutions of scalar fields are reproduced and stored as databases. The Japanese side applies various optimization techniques to the databases for developing efficient searching algorithms of scalar sources based on information obtained by stationary and/or moving sensors.
The resultant algorithm is installed to the autonomous underwater vehicle developed in the Japanese side and its searching performance is evaluated in water-tank experiment.
||Associate Professor, Department of Mechanical Engineering, Johns Hopkins University
||Dynamic Evolution of Smart-Phone Based Emergency Communications Network
||Associate Professor, University-Business Innovation Center, the University of Aizu
||The project will develop solutions to construct an emergency communication network, and optimize coverage, communications capability, etc. in the emergency communication network, based on analysis of big data from Twitter and emergency apps.
Specifically, researchers at the Japan site will consider disaster related data and experience in the local area and propose novel solutions, e.g., understanding network evolution through data analysis and reconstruction of separated networks. Researchers at the US site will extend current research by considering real needs in a disaster scenario and develop solutions on monitoring change in the distribution of affected users and network evolution through data analysis and construction of Wi-Fi tethering based ad hoc network.
Through mutual complementarities of both sites in disaster experiences, culture, research strengths, etc., we will propose a set of novel solutions for emergency communications network that can be adopted in different countries, and cultivate excellent international researchers and research partnerships.
||Professor, Computer and Information Sciences (CIS) department, Temple University
||Disaster Preparation and Response via Big Data Analysis and Robust Networking
||Professor, Information Systems Architecture Research Division, National Institute of Informatics
||Disasters are events that require multiple-agency responses, and resources beyond the capability of a community. Natural disasters, such as the 2011 Great East Japan Earthquake, put tremendous threats to the lives of many people, in addition to economic loss. However, during a disaster, many infrastructures may be damaged, and the traffic patterns are very different from normal time. There is a great need for an efficient and robust information system for emergency applications during disasters.
In this project, we study novel approaches to disaster preparation, response and recovery using survivable communication networks and big data analysis of social media data, etc. The project involves a complementary mix of survivable network design, data collection and analysis before the disaster, and decision-making and information dissemination during the disaster. The outcomes of this research are expected to provide useful means for the preparation, response, and recovery of large-scale disasters.
||Professor, School of Computing, Informatics, Decision systems engineering, Arizona State University
||Data-Driven Critical Information Exchange in Disaster Affected Public-Private Networks
||Project Associate Professor, Center for Spatial Information Science, The University of Tokyo
||In this project, we plan to address the above challenges and design data-driven solutions to address critical information exchange needs in disaster affected public-private networks by leveraging the initial studies and ongoing research efforts in both the FIU team and the Japan Team.
The intellectual merit of this project consists of the following specific research aims: (1) design and develop effective information integration and summarization methods to help users improve situational awareness; (2) design and develop intelligent information delivery techniques to help users quickly identify the information they need; and (3) design and develop automatic techniques for dynamic community generation.
These research components constitute a holistic effort to effectively organize, discover, search and disseminate real-time disaster information.
||Professor, School of Computer Science, Florida International University