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Author Loïc Bidoux; Jean-Paul Pignon; Frédérick Bénaben
Title A model driven system to support optimal collaborative processes design in crisis management Type Conference Article
Year 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014
Volume Issue Pages 245-249
Keywords Algorithms; Benchmarking; Decision making; Inference engines; Optimization; Process design; Collaborative process; Crisis management; Inter-agencies coordination; Key performance indicators; Model-driven; Multi-criteria decision analysis; Optimization algorithms; Technical design; Information systems
Abstract This paper presents a system dedicated to support crises managers that is focused on the collaboration issues of the actors involved in the response. Based on context knowledge, decision makers' objectives and responders' capabilities, the system designs in a semi-automatic way a set of collaborative process alternatives that can optimize coordination activities during an ongoing crisis resolution. The technical design of the system mixes optimization algorithms with inference of logical rules on an ontology. Candidate processes are evaluated through multi-criteria decision analysis and proposed to the decision-makers with associated key performance indicators to help them with their choice. The overall approach is model driven through a crisis meta-model and an axiomatic theory of crisis management.
Address Mines Albi – Université de Toulouse, France; Thales Communications and Security, France
Corporate Author Thesis
Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9780692211946 Medium
Track Decision Support Systems Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 325
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Author Olof Görnerup; Per Kreuger; Daniel Gillblad
Title Autonomous accident monitoring using cellular network data Type Conference Article
Year 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013
Volume Issue Pages 638-646
Keywords Bayesian networks; Carrier mobility; Inference engines; Information systems; Sensor networks; Traffic congestion; Anomaly detection; Bayesian inference; Cellular network; Crisis management; Emergency response; Large scale sensor network; Mobile communication networks; Vehicular traffic scenarios; Accidents
Abstract Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions.
Address Swedish Institute of Computer Science, Sweden
Corporate Author Thesis
Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9783923704804 Medium
Track Analytical Modelling and Simulation Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 537
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Author Rob Grace; Jess Kropczynski; Scott Pezanowski; Shane Halse; Prasanna Umar; Andrea Tapia
Title Social Triangulation: A new method to identify local citizens using social media and their local information curation behaviors 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 902-915
Keywords community preparedness; geolocation inference; information infrastructure; social media
Abstract Local citizens can use social media such as Twitter to share and receive critical information before, during, and after emergencies. However, standard methods of identifying local citizens on Twitter discover only a small proportion of local users in a geographic area. To better identify local citizens and their social media sources for local information, we explore the information infrastructure of a local community that is constituted prior to emergencies through the everyday social network curation of local citizens. We hypothesize that investigating social network ties among local organizations and their followers may be key to identifying local citizens and understanding their local information seeking behaviors. We describe Social Triangulation as a method to identify local citizens vis-à-vis the local organizations they follow on Twitter, and evaluate our hypothesis by analyzing users' profile location information. Lastly, we discuss how Social Triangulation might support community preparedness by informing emergency communications planning.
Address The Pennsylvania State University
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 Prevention and Preparation Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management
Notes Approved no
Call Number Serial 2075
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Author Yan Wang; Hong Huang; Wei Zhu
Title Stochastic source term estimation of HAZMAT releases: algorithms and uncertainty 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 Bayesian inference; emergency response; hazardous material releases; source term estimation; uncertainty
Abstract Source term estimation (STE) of hazardous material (HAZMAT) releases is critical for emergency response. Such problem is usually solved with the aid of atmospheric dispersion modelling and inversion algorithms accompanied with a variety of uncertainty, including uncertainty in atmospheric dispersion models, uncertainty in meteorological data, uncertainty in measurement process and uncertainty in inversion algorithms. Bayesian inference methods provide a unified framework for solving STE problem and quantifying the uncertainty at the same time. In this paper, three stochastic methods for STE, namely Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC) and ensemble Kalman filter (EnKF), are compared in accuracy, time consumption as well as the quantification of uncertainty, based on which a kind of flip ambiguity phenomenon caused by various uncertainty in STE problems is pointed out. The advantage of non-Gaussian estimation methods like SMC is emphasized.
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 Analytical Modelling and Simulation Expedition Conference ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 1194
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Author Gian Piero Zarri
Title Representing and managing 'narrative' terrorism information Type Conference Article
Year 2008 Publication Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2008
Volume Issue Pages 208-218
Keywords Information systems; Knowledge representation; Complete solutions; Inference; Narratives; Philippines; Terrorism
Abstract In this paper, we evoke first the ubiquity and the importance of the so-called 'nonfictional narrative' information, with a particular emphasis on the terrorism- and crime-related data. We show that the usual knowledge representation and 'ontological' techniques have difficulties in finding complete solutions for representing and using this type of information. We supply then some details about NKRL, a representation and inferencing environment especially created for an 'intelligent' exploitation of narrative information. We will also supply some examples concerning a “terrorism in Southern Philippines” general context to illustrate our approach.
Address University Paris4, Sorbonne, France
Corporate Author Thesis
Publisher Information Systems for Crisis Response and Management, ISCRAM Place of Publication Washington, DC Editor F. Fiedrich, B. Van de Walle
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9780615206974 Medium
Track Ontologies for Crisis Management Expedition Conference 5th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 1140
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