The workshop on Big Data Analytics for Humanitarian Crises is organized by the Information Technology and Web Science Program at Rensselaer Polytechnic Institute in conjunction with the IEEE International Conference on Big Data (IEEE BigData 2022). This workshop will be held online.
Important Dates and Information
Due date for full workshop papers submission: October 1, 2022
Notification of paper acceptance to authors: November 1, 2022
Camera-ready of accepted papers: November 20, 2022
Workshops: December 17-20, 2022
Submission details: https://www.ieee.org/conferences/publishing/templates.html
We invite full-length paper submissions that report ongoing or finished research (up to 10 pages), or short papers of early stage work (up to 6 pages).
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines using Letter page format (8.5” x 11”).
All accepted papers will be included in the IEEE Big Data 2022 Conference Proceedings and forwarded for inclusion in the IEEE Xplore Digital Library. At least one author of each accepted paper must register for the conference and present the paper in order to include the paper in the proceedings.
The United Nations Office for the Coordination of Humanitarian Affairs’s Humanitarianism in the Network Age report states “finding ways to make big data useful to humanitarian decision makers is one of the great challenges, and opportunities, of the network age.” Two key elements to this are: one, the quality, ease of access, and ease of understanding of datasets produced by agencies in the midst of humanitarian crises; and, two, the speed in which data scientists analyze these datasets so that proper outputs are able to be put on the desks of decision makers as quickly as possible. This work has the ability to save lives if it can be performed quickly enough. This workshop presents real-world efforts in analyzing data emerging from ongoing humanitarian crises, such as the Ukrainian refugee crisis, in an effort to demonstrate the opportunities and challenges in data analysis during humanitarian crises.
Big Data Analytics for Humanitarian Crises workshop provides a platform to bring research scientists from academia, government agencies, and non-governmental organizations together to share how the insights obtained from data analytics, machine learning, and deep learning techniques can be applied to come up with solutions and design tools that can advance our understanding of the complexities of challenges caused by humanitarian crises around the world.
These challenges include but are not limited to climate change-infused humanitarian crisis risks, ongoing wars, human conflict-induced forced displacements, human trafficking, food insecurity, and human rights violations.
Big data analytics and practices seek to include research work from a wide range of technologies and domains, including (but not limited to) machine learning, applications of privacy-preserving federated learning, causal models, spatiotemporal analytics, collaborative visual analytics that help policymakers, humanitarian crisis event exploration using data visualization techniques that assist equitable and policy decision-making, predictive analytics, data modeling, data mining, and simulations on data collected from various sources, including but not limited to:
- Geospatial data: GPS location data, aerial images, and satellite observation data used to tackle humanitarian crises.
- Supply chain and operational data: to inform and manage uninterrupted humanitarian operations during crisis events.
- Survey and crowdsourcing datasets collected via digital platforms such as mobile Apps, SMS-based surveys, and data collected through web-based portals directly from beneficiaries of humanitarian assistance to assess needs during relief efforts and crisis mitigation.
- Data collected from official government sources and organizations such as the United Nations, International Red Cross, UNICEF, UN Refugee Agency/UNHCR, World Bank, etc., where Big Data is used for development and humanitarian actions.
For general inquiries please contact the workshop chair: Thilanka Munasinghe at munast (at) rpi.edu