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Author Audrey Fertier; Aurélie Montarnal; Anne-Marie Barthe-Delanoë; Sébastien Truptil; Frédérick Bénaben
Title Adoption of Big Data in Crisis Management Type Conference Article
Year 2016 Publication ISCRAM 2016 Conference Proceedings – 13th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2016
Volume Issue Pages
Keywords Crisis Management; Big Data; Decision Support; Agility
Abstract Most agree that the innate complexity and uncertainty of a crisis compel the stakeholders to coordinate in a hurry, despite their heterogeneity or the volume of data to process. Supporting their coordination is now possible, thanks to a mediation system combined with big data management tools. The GéNéPi1 project explores this possibility and proposes to improve the generation of collaborative processes offered by the MISE2’s solution. The idea is to increase the number of usable data sources. To do that, in a fixed time-frame, the situation models have to be instantly generated upon sets of raw data. This new methodology holds the key to a new big data era: an age where global understanding reigns.
Address
Corporate Author Thesis
Publisher Federal University of Rio de Janeiro Place of Publication Rio de Janeiro, Brasil Editor A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3395 ISBN 978-84-608-7984-16 Medium
Track Intelligent Decision Support in the Networked Society Expedition Conference 13th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 1334
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Author Audrey Fertier; Aurélie Montarnal; Sébastien Truptil; Anne-Marie Barthe-Delanoë; Frédérick Bénaben
Title A situation model to support collaboration and decision-making inside crisis cells, in real time Type Conference Article
Year 2017 Publication Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2017
Volume Issue Pages 1020-1028
Keywords Crisis Management; Situation Model; Situation Awareness; Big Data
Abstract Natural and man-made hazards have many unexpected consequences that concern as many heterogeneous services. The GéNéPi project offers to support officials in addressing those events: its purpose is to support the collaboration in the field and the decision-making in the crisis cells. To succeed, the GéNéPi system needs to be aware of the ongoing crisis developments. For now, its best chance is to benefit from the ever growing number of available data sources. One of its goals is, therefore, to learn how to manage numerous, heterogeneous, more or less reliable data, in order to interpret them, in time, for the officials. The result consists on a situation model in the shape of a common operational picture. This paper describes every stage of modelling from the raw data selection, to the use of the situation model itself.
Address Centre Génie Industriel, Université de Toulouse, Albi, France; Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France
Corporate Author Thesis
Publisher Iscram Place of Publication Albi, France Editor Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN Medium
Track Response and Recovery Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management
Notes Approved no
Call Number Serial 2085
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Author Hendrik Stange; Sylvia Steenhoek; Sebastian Bothe; François Schnitzler
Title Insight-driven Crisis Information – Preparing for the Unexpected using Big Data Type Conference Article
Year 2015 Publication ISCRAM 2015 Conference Proceedings – 12th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2015
Volume Issue Pages
Keywords Big data; crisis information; incident detection; Reality Monitoring; uncertainty
Abstract National information and situation centers are faced with rising information needs and the question of how to prepare for unexpected situations. One promising development is the access to vastly growing data produced by distributed sensors and a socially networked society. Current emergency information systems are limited in the amount of complex data they can process and interpret in real-time and provide only partially integrated prediction and alarming capabilities. In this paper we present a novel approach to build a new type of automated and scalable information systems that intelligently make use of massive streams of structured and unstructured data and incorporate human feedback for automated incident detection and learning. Big data technologies, uncertainty handling and privacy-by-design are employed to match end-user system requirements. We share first experiences analyzing data from the centennial flood in Germany 2013.
Address
Corporate Author Thesis
Publisher University of Agder (UiA) Place of Publication Kristiansand, Norway Editor L. Palen; M. Buscher; T. Comes; A. Hughes
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9788271177881 Medium
Track Planning, Foresight and Risk Analysis Expedition Conference ISCRAM 2015 Conference Proceedings – 12th International Conference on Information Systems for Crisis Response and Management
Notes Approved yes
Call Number Serial 1309
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Author Nilani Algiriyage; Raj Prasanna; Emma E H Doyle; Kristin Stock; David Johnston
Title Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images Type Conference Article
Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020
Volume Issue Pages 100-109
Keywords CCTV Big Data, YOLOv3, Traffic Flow Estimation.
Abstract Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as \quotes{left-lane} and \quotes{right-lane}. This paper presents the early results and discusses the next steps.
Address Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Institute of Natural and Mathematical Sciences, Massey University; Joint Centre for Disaster Research, Massey University;
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-27-10 ISBN 2411-3396 Medium
Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management
Notes r.nilani@massey.ac.nz Approved no
Call Number Serial 2211
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Author Rachel L. Finn; Hayley Watson; Kush Wadhwa
Title Exploring big ‘crisis’ data in action: potential positive and negative externalities Type Conference Article
Year 2015 Publication ISCRAM 2015 Conference Proceedings – 12th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2015
Volume Issue Pages
Keywords Big data; crisis data; humanitarian aid; social impacts; social media
Abstract As growing emphasis is placed on engaging with big ‘crisis’ data, including data from social media, GPS, and satellite, adequate policies and measures must be in place in order to use this data in an ethically and legally responsible manner. The current working paper introduces the BYTE study, which is working towards identifying and understanding the various positive and negative externalities, or impacts, associated with the use of big ‘crisis’ data. This insight paper provides a preliminary discussion of various externalities that may be encountered in this study. By doing so, the authors highlight the need for additional research in this area to promote ethically and legally responsible crisis data practices.
Address
Corporate Author Thesis
Publisher University of Agder (UiA) Place of Publication Kristiansand, Norway Editor L. Palen; M. Buscher; T. Comes; A. Hughes
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9788271177881 Medium
Track Ethical, Legal and Social Issues Expedition Conference ISCRAM 2015 Conference Proceedings – 12th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 1206
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Author Robin Gandhi; Deepak Khazanchi; Daniel Linzell; Brian Ricks; Chungwook Sim
Title The Hidden Crisis : Developing Smart Big Data pipelines to address Grand Challenges of Bridge Infrastructure health in the United States Type Conference Article
Year 2018 Publication ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018
Volume Issue Pages 1016-1021
Keywords Big Data, Bridge Health, Infrastructure, Disaster
Abstract The American Society of Civil Engineers (ASCE) Report Card for America's Infrastructure gave bridges a C+ (mediocre) grade in 2017. Approximately, 1 in 5 rural bridges are in critical condition, which presents serious challenges to public safety and economic growth. Fortunately, during a series of workshops on this topic organized by the authors, it has become clear that Big Data could provide a timely solution to these critical problems. In this work in progress paper we describe a conceptual framework for developing SMart big data pipelines for Aging Rural bridge Transportation Infrastructure (SMARTI). Our framework and associated research questions are organized around four ingredients: o Next-Generation Health Monitoring: Sensors; Unmanned Aerial Vehicle/System (UAV/UAS); wireless networks o Data Management: Data security and quality; intellectual property; standards and shared best practices; curation o Decision Support Systems: Analysis and modeling; data analytics; decision making; visualization, o Socio-Technological Impact: Policy; societal, economic and environmental impact; disaster and crisis management.
Address
Corporate Author Thesis
Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszeski
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium
Track Open Track Expedition Conference ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 2173
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Author Yoshiki Ogawa; Yuki Akiyama; Ryosuke Shibasaki
Title Extraction of significant scenarios for earthquake damage estimation using sparse modeling Type Conference Article
Year 2017 Publication Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2017
Volume Issue Pages 150-163
Keywords Big data; Mobile phone GPS logs; People flow; Micro geodata; Damage distribution
Abstract The recent diversification and accumulation of data from GPS equipped mobile phones, building sensors, and other resources in Japan has caused a large increase in the number of earthquake disaster scenarios that can be identified. Disaster prevention planning requires us to contemplate which scenario should be focused on and the required response to various scenarios. As a means to solve this problem, the damage distribution of building collapse and fire from GPS data can be used to estimate future damage based on people flow and various hypocenter models of earthquakes. We propose a method that uses sparse modeling to extract scenarios that are important for disaster estimation and prevention. As a result, this paper makes it possible to quickly grasp the scenario distribution, which was previously impossible to do, and to extract the significant scenarios.
Address The University of Tokyo
Corporate Author Thesis
Publisher Iscram Place of Publication Albi, France Editor Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN Medium
Track Analytical Modeling and Simulation Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management
Notes Approved no
Call Number Serial 2007
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Author Yossi Nygate; William Johnson; Mark Indelicato; Miguel Bazdresch; Clark Hochgraf
Title Intelligent Wireless Infrastructure Management for Emergency Communications Type Conference Article
Year 2018 Publication ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018
Volume Issue Pages 1156-1160
Keywords communications, LTE, deployable, QoS, big data analytics
Abstract This poster describes the research of a collaborative faculty-led research that will enable first responders to identify and visualize geo-located quality of service and coverage gaps in wireless and deployable networks during an emergency event and support the deployment additional LTE base stations within FirstNet to augment network coverage and capacity. Our crowd sourced cellular metrics system uses big data analytics to detect changes in coverage and usage patterns and recommends where to deploy additional communication assets. The approach uses machine learning methods to measure and model coverage gaps and automatically implement bandwidth prioritization on whatever communication assets are available.
Address
Corporate Author Thesis
Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszeski
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium
Track Poster Expedition Conference ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 2195
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