2016 Workshop on Big Data and Analytics for Emergency Management and Public Safety - overview

Workshop on Big Data and Analytics for Emergency Management and Public Safety

As part of the:

2016 IEEE International Conference on Big Data (IEEE Big Data 2016)

December 5-8, 2016, Washington DC, USA


Dear authors, due to insufficient submissions to run this workshop as a standalone one, it has been joined with the 2nd International Workshop on Big Data for Sustainable Development @ IEEE Big Data 2016, and all submitted papers are being transferred to that workshop.

We apologise for the slight delay in notifications of paper acceptance status, and we aim to do that as soon as possible.

Natural disasters, such as wildfires, floods, storms, heat waves, earthquakes, landslides and many others have occurred in ever-increasing numbers in recent years.


Moreover, the World Bank estimates that economic developments, population growth and rapid urbanization will drive an increase in disaster losses over coming years1.

Traditionally, public discourse on emergency management and response has considered such natural disasters as the primary focus, but recent years have shown that fast spreading human diseases (e.g. Ebola), pests or animal diseases (e.g. Hendra virus), telecommunication systems failures, and acts of violence and terrorism have far reaching consequences requiring a similar framework of emergency response. As with natural disasters, such health, infrastructure and security incidents can critically impact communities and jeopardize public safety.

With a current focus on moving from reacting to these events as they happen towards preventing and minimizing them, big data and analytics play a critical role in societal ability to plan, prepare and recover from emergency events.

This workshop will be the first at IEEE Big Data conference to address a number of acute questions in Emergency Management and Public Safety, which are of interest and applicable to a worldwide audience, for example:

  • How can we make use of massive amounts of data (weather, demographics, urbanism, climate, natural resources etc.) to predict the risk and the possible impact of disasters?
  • How can we make use of big open data to better predict disease outbreaks and their impact on communities (health), governments (spending) and economies (losses)?
  • What can we learn by analyzing big data contributing to past emergency events, to learn and use that knowledge intelligently to build up community resilience to such events?

Research topics:
Note: the topics proposed below have a focus on big data for emergency management and public safety, for example weather, social networks data, climate, diseases, demographics, however the list is not exhaustive and papers on other related topics are welcome.

  • Real time analytics for heterogeneous spatio-temporal big data streams
  • Unsupervised machine learning for big data
  • Scalable predictive analytics workflows for big data
  • Extracting and visualizing critical insights from big data
  • Uncertainty propagation in connected big data models

Contributions are invited from prospective authors with interests in the indicated session topics and related areas of application. All contributions should be high quality, original and not published elsewhere or submitted for publication during the review period.
Submitted contributions will be reviewed by three members of the PC.

Based on relevance, selected papers will be published in a Special Issue of the International Journal of Risk and Contingency Management (IJRCM) or a Special Issue of the International Journal of Data Warehousing and Mining (IJDWM), in 2017. More information will be provided soon.

Call for papers in PDF format: download here


1 http://www.worldbank.org/en/topic/disasterriskmanagement/overview