We recently publish a study  to IEEE Big Data 2015 with application in crisis response. We have been studying an important problem of obtaining real-time information during disasters. This is a complex, new problem, in an area that is attracting increasingly more interest (live streaming of mobile video by end users). We propose some interesting ideas, e.g., small amount of metadata is transmitted to a server in real-time while video content will remain on the device; the server will ping the device when it wants the user to upload the video.
In the problem addressed, a limited amount of bandwidth is available for people to upload videos about a crisis situation, and an automatic system, with the help of human analysts, should decide a priority for the upload of such videos. The objective is to enhance situational awareness of a crisis.
We propose to an optimization problem of selecting a set of videos that maximizes the video coverage given a budget constraint. As videos might produce a lot of noisy content and the analysts might not be sufficient, we identify the need to prioritize the use of data in big data settings, where transmission or analysis resources are limited, and/or there is time critical pressure to gain meaningful value from the data.
We propose to upload keyframes and metadata to the server and minimize their redundant coverage. The problem becomes selecting a set of frames that maximizes total Visual Awareness without exceeding budget constraint. This results in higher visual awareness in data acquisition. We also propose efficient spatial decomposition techniques that consider the spatial distribution of the videos (e.g., kd-tree, quadtree). These partitioning techniques distribute the data equally to all analysts.
 Hien To, Seon Ho Kim, and Cyrus Shahabi, Effectively Crowdsourcing the Acquisition and Analysis of Visual Data for Disaster Response, In proceeding of 2015 IEEE International Conference on Big Data, Santa Clara, CA, USA, October 29-November 1, 2015Afsin Akdogan, Hien To, Seon Ho Kim, and Cyrus Shahabi,
 Sasan Tavakkol, Hien To, Seon Ho Kim, Patrick Lynett, and Cyrus Shahabi, An Entropy-based Framework for Efficient Post-disaster Assessment Based on Crowdsourced Data, The 2nd Workshop on Emergency Management using GIS (EM-GIS 2016), San Francisco, USA, October 2016