[AISWorld] Due Oct 10, 2016: Indexed IEEE Big data analytics in emergency management & public safety conference (Washington DC Dec 5-8, 2016)

[Chief Editor]

*IMPORTANT DATES*

·         Oct 10, 2016: Due date for paper​s
​;​

·         Nov 1, 2016: Notification of paper acceptance to authors;

·         Nov 15, 2016: Camera-ready final proofed version of accepted
​ ​
papers;

·         Dec 5-8, 2016: IEEE Big Data conference to present paper in
Washington DC USA;

·         Jan 1, 2017: Final version of selected papers due for
​ ​
IJRCM
​ jour
nals
​.​
​

*SPECIAL ISSUE TITLE*:* Big data analytics in emergency management and
public safety*

*International Journal of Risk and Contingency Management (IJRCM)*



Guest Editors: *Dr Laura Irina Rusu, IBM Research Australia;* and *Gandhi
Sivakumar, Watson CoC, Master Inventor, IBM Australia.*

*INTRODUCTION*

The workshop on *Big Data and Analytics for Emergency Management and Public
Safety *is part of the *IEEE International Conference on Big Data* to be
held during December 5-8, 2016, in Washington DC, USA. The city is a
walkable safe place to visit when the fall colors are visible and since it
is the capitol of USA there are numerous attractions with excellent public
transit. This conference is sponsored by the National Science Foundation,
IEEE and IBM Research. Conference paper presentations will be published in
the proceedings and indexed. Selected papers will be revised and published
in IJRCM. For researchers, scholars and practitioners, this is a strategic
opportunity to publish a preliminary paper at the Big Data conference and
then revise it to obtain a peer reviewed journal publication.

This topic is important! Evidence suggests natural disasters, such as
wildfires, floods, storms, heat waves, tornados, hurricanes, cyclones,
earthquakes, and landslides have occurred more frequently and with more
devastating impact (Block, 2013; Garrett, 2015). The World Bank (2016)
asserts the increased impact from disasters stem from a rising trend in
population density, economic development and urbanization. Additionally,
pandemics, lethal fast-growing diseases such as Ebola and viruses like
Hendra are occurring that frequently result in death (WHO, 2016; NIH,
2011). Furthermore, man-made disasters including terrorism, violence,
infrastructure sabotages and the spread of diseases are compounding the
increased risk that emergency management responders, public safety
administrators and insurance companies must address (Strang, 2015).

Some researchers leveraged big data to perform descriptive analysis on
man-made disasters (Vajjhala, Strang & Sun, 2015). However, the literature
mostly contains descriptive accounts (Strang & Alamieyeseigha, 2015; Strang
& Nersesian, 2014) or models about disasters (Strang, 2014a; 2014b) so
there is a need for more prescriptive research using big data or big data
analytics (Strang, 2016).

Therefore we invite practitioners and researchers to share their knowledge
for how to leverage big data and big data analytics to prevent or at least
minimize all types of disasters. We will also accept papers that explore
disasters using big data or papers that show how big data analytics may be
used to forecast, plan, prepare and recover from any type of emergency or
public safety event.

*References*

Block, B. (2013). *Natural disasters becoming more frequent*. Washington,
DC: Worldwatch Institute. Retrieved August 11, 2016 from:
http://www.worldwatch.org/node/5825

Garrett, S. (2015). Are Natural Disasters Increasing? Seattle, WA: Borgen
Group. Retrieved August 11, 2016 from: http://borgenproject.org/
natural-disasters-increasing/

NIH. (2011). Scientists embrace the 'one world' approach [National
Institute of Health]. *World Health Organization Bulletin*, 89(12),
860-861. Retrieved August 11, 2016 from: http://www.ncbi.nlm.nih.gov/
pmc/articles/PMC3260892/

Strang, K. D. (2014a). Assessing natural disaster survivor evacuation
attitudes to inform social policy. *International Journal of Sociology and
Social Policy*, 34(7/8), 485-510. Retrieved August 11, 2016 from:
http://dx.doi.org/10.1108/IJSSP-04-2013-0040

Strang, K. D. (2014b). Planning for hurricanes using probability theory in
linear programming models. In Information Resources Management Association
(Ed.), *Crisis management: Concepts, methodologies, tools and applications*
(Vol. 2, pp. 1056-1072). Hershey, PA: Information Science Reference.
Retrieved August 11, 2016 from: https://books.google.com/
books?id=-R9HAgAAQBAJ&printsec=frontcover#
<https://books.google.com/books?id=-R9HAgAAQBAJ&printsec=frontcover>

Strang, K. D. (2015). Developing prescriptive environmental protection
models from descriptive human accident behavior. *International Journal of
Disaster Resilience in the Built Environment*, 6(4), 81-96. Retrieved
August 11, 2016 from: http://www.emeraldinsight.com/toc/ijdrbe/6/4

Strang, K. D. (2016). Ethiopia child labor, India banks and Argentina
disasters: Could big data analytics help? [editorial preface]. *International
Journal of Risk and Contingency Management*, 5(3), iv-vi. Retrieved August
11, 2016 from: http://www.igi-global.com/pdf.aspx?tid%3d158015%26ptid%
3d132195%26ctid%3d15%26t%3dethiopia+child+labor%2c+
india+banks+and+argentina+disasters%3a+could+big+data+analytics+help%3f

Strang, K. D., & Alamieyeseigha, S. (2015). What and where are the risks of
international terrorist attacks: A descriptive study of the evidence.
*International
Journal of Risk and Contingency Management*, 4(1), 1-18. Retrieved August
11, 2016 from: http://www.igi-global.com/article/what-and-where-are-
the-risks-of-international-terrorist-attacks/127538

Strang, K. D., & Nersesian, R. (2014). Nonparametric estimation of
petroleum accident risk to improve environmental protection. *Journal of
Environment, Systems and Decisions*, 34(1), 150-159. Retrieved August 11,
2016 from: http://link.springer.com/article/10.1007%2Fs10669-013-9476-z

Vajjhala, N. R., Strang, K. D., & Sun, Z. (2015). Statistical modeling and
visualizing of open big data using a terrorism case study. *Proceedings of
the IEEE Open Big Data Conference*, Rome, Italy. Retrieved August 11, 2016
from: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=
7300857&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%
2Fabs_all.jsp%3Farnumber%3D7300857

WHO. (2016). *Ebola virus disease* (Report). Geneva, Switzerland: World
Health Organization [WHO]. Retrieved August 11, 2016 from:
http://www.who.int/mediacentre/factsheets/fs103/en/

Worldbank. (2016). Disaster risk management overview (Context report).
Washington, DC: The World Bank. Retrieved August 11, 2016 from:
http://www.worldbank.org/en/topic/disasterriskmanagement/overview



*OBJECTIVE OF THE SPECIAL ISSUE*

This workshop will be the first at IEEE Big Data conference to address
Emergency Management and Public Safety. The results will be 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?

*RECOMMENDED TOPICS*

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.

·         big data uses for studying EMS/public safety

·         climate/weather uncertainty

·         disease risk

·         unexpected issues in demographics

·         scalable predictive analytics for big data

·         quantifying and visualizing critical insights from big data

·         uncertainty propagation in connected big data models

·         unsupervised machine learning for big data

·         predictive analytics of workflows using big data

·         extracting and analyzing meta-data from big data

·         uncertainty propagation in connected big data models

·         real time analytics for heterogeneous spatio-temporal big data
streams.

*SUBMISSION PROCEDURE*

IEEE Big Data 2016 conference papers must be first submitted to IEEE using
the required format (see ftp://pubftp.computer.org/
press/outgoing/proceedings/instruct8.5x11x2.doc?cm_mc_
uid=89215813930114705994914&cm_mc_sid_50200000=1470939056 ) and uploaded to
IEEE conference at this URL:

https://wi-lab.com/cyberchair/2016/bigdata16/scripts/submit.
php?subarea=S08&undisplay_detail=1&wh=/cyberchair/2016/
bigdata16/scripts/ws_submit.php



Selected big data conference papers will be revised to APA format (see
below), extended if needed and submitted to the IGI-Global URL:
http://www.igi-global.com/submission/manuscripts/?jid=53135



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 receive at least three double-blind peer
reviews.  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.

INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT
SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/
before-you-write/. Papers submitted to the above journals must follow APA
style for reference citations.

Questions related to this CFP should be should be directed to
corresponding *IJRCM
guest editor* *Dr. Laura Irina Rusu* at email: laurusu AT au1.ibm.com or
IJRCM Editor-in-Chief Dr. Kenneth David Strang (chief.editor at gmail.com).

Best wishes,

-Ken  http://ken.multinations.org/

Dr Kenneth David Strang, *Doctorate, MBA, BS, BT, FLMI, CNA, PMP*
Business Program Coordinator & Professor, State University of New York, USA
CEO, Not-For-Profit APPC Research, Australia
Editor/founder,
*International Journal of Risk and Contingency Management*Associate Editor
on other peer-reviewed journals & book author.