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Arif Cagdas Aydinoglu, Elif Demir, & Serpil Ates. (2011). Designing a harmonized geo-data model for Disaster Management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: There are problems for managing and sharing geo-data effectively in Turkey. The key to resolving these problems is to develop a harmonized geo-data model. General features of this model are based on ISO/TC211 standards, INSPIRE Data Specifications, and expectations of Turkey National GIS actions. The generic conceptual model components were defined to harmonize geo-data and to produce data specifications. In order to enable semantic interoperability, application schemas were designed for data themes such as administrative unit, address, cadastre/building, hydrographic, topography, geodesy, transportation, and land cover/use. The model, as base and the domain geo-data model, is a starting point to create sector models in different thematic areas. Disaster Management Geo-data Model model was developed as an extension of base geo-data model to manage geo-data collaborate on disaster management activities. This model includes existing geo-data special for disaster management activities and dynamic data collecting during disaster.
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Ben Ortiz, Laura Kahn, Marc Bosch, Philip Bogden, Viveca Pavon-Harr, Onur Savas, et al. (2020). Improving Community Resiliency and Emergency Response With Artificial Intelligence. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 35–41). Blacksburg, VA (USA): Virginia Tech.
Abstract: New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina.
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Gary Berg-Cross. (2008). Improving situational ontologies to support adaptive crisis management knowledge architecture. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 537–545). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: There is considerable interest in advance technologies to support crisis and disaster management as they face the challenges of designing, building, and maintaining large-scale distributed systems able to adapt to the dynamics and complexity of crises. Candidate technologies include Service Oriented Architecture (SOA), related Semantic Web technology, agent-based architecture and cognitive architectures. Each embodies some principles of the Adaptive Architecture-including modularity, openness, standards-based development, runtime support and importantly explicitness. However, truly adaptive architectures for crisis management will require some deepening the knowledge architecture's content and not just its representation. Light and more robust ontological models of situations are discussed to show how better formalization of conceptual patterns like “participation” can be developed to support cognitive architectures. The feasibility of an ontological design pattern approach is described as an avenue for future research and development describing specific types of situations.
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Aaron Burgman, Nikhil Kalghatgi, Erika Darling, Chris M. Newbern, Kristine Recktenwald, Shawn Chin, et al. (2006). Emergency data analysis via semantic lensing. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 334–338). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Emergency situations often play out over extended geographic regions and can present response personnel with numerous types of data at various level of detail. Such data may be displayed in mapping software tools that organize the data into layers. Sufficiently complex scenarios can result in dense, occluded, and cluttered map displays. We investigated a localized, detail-on-demand filtering strategy called semantic lensing that in certain situations provides a more efficient and desirable approach than filtering global layers for mitigating clutter and occlusion. An initial formal user study with these semantic lenses has shown their value in aiding decision makers during tasks that might occur during detection of and response to emergency situations. Completion times are significantly faster when using lenses, and workloads are significantly lower. Future work will evaluate additional features and task-specific applicability, and may support the distribution of such a lens tool to emergency preparedness and response personnel.
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Carsí, J. A., Canós, J. H., Penadés, M. ª C., Sánchez-Díaz, J., & Borges, M. R. S. (2023). Towards a Generic Metamodel for Urban Resilience Assessment. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1059–1068). Omaha, USA: University of Nebraska at Omaha.
Abstract: The proliferation of natural and artificial disasters in the last decades has made urban resilience enforcement a strategic goal of city governments worldwide and a hot research topic for academics and practitioners. Consequently, several urban resilience assessment and improvement frameworks have been proposed. Some frameworks have associated operational tools, but these systems are not interoperable with other frameworks' utilities, forcing cities to use different tools for evaluating various aspects of resilience. Since data must be converted manually from one tool to another, the conversion may be error-prone and tedious. In this paper, we report the steps toward defining an urban resilience metamodel that intends to be at the core of a multi-framework urban resilience management portal. Our goal is to provide city administrators with a single operational tool able to evaluate resilience according to different frameworks, thanks to the definition of semantic interoperability mechanisms between the frameworks and the metamodel
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Claire Laudy. (2017). Rumors detection on Social Media during Crisis Management. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 623–632). Albi, France: Iscram.
Abstract: Social Media monitoring has become a major issue in crisis and emergencies management. Indeed, social media may ease the sharing of information between citizens and Public Safety Organizations, but it also enables the rapid spreading of inaccurate information. As information is now provided and shared by anyone to anyone, information credibility is a major issue. We propose an approach to detect rumor in social media. This paper describes our work on semantic graph based information fusion, enhanced with uncertainty management capabilities. The uncertainty management capability enables managing the dierent level of credibility of actors of an emergency (dierent PSO oÿcers and citizens). Functions for information synthesis, conflicting information detection and information evaluation were developed and test during experimentation campaigns. The synthesis and conflicting information detection functionalities are very welcome by end-users. However, the uncertainty management is a combinatorial approach which remains a limitation for use with large amount of information.
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Kelli de Faria Cordeiro, Maria Luiza M Campos, & Marcos R. S. Borges. (2014). Adaptive integration of information supporting decision making: A case on humanitarian logistic. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 225–229). University Park, PA: The Pennsylvania State University.
Abstract: There is an urgent demand for information systems to gather heterogeneous information about needs, donations and warehouse stocks to provide an integrated view for decision making in humanitarian logistics. The dynamic flow of information, due to the unpredicted events, requires adaptive features. The traditional relational data model is not suitable due to its schema rigidity. As an alternative, Graph Data models complemented by semantic representations, like Linked Open Data on the Web, can be used. Based on both, this research proposes an approach for the adaptive integration of information and an associated architecture. An application example is discussed in a real scenario where relief goods are managed through a dynamic and multi-perspective view.
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Paola Di Maio. (2008). Ontologies for networked centric emergency mangement operations. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 177–188). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Emergency Management, like other fields of Operations, consists of information, communication and decision making. Thanks to the pervasiveness of real time networked infrastructures, such as the internet and the web, new models of operations are emerging, designed to leverage the aggregate the power of 'collective intelligence' and 'distributed action' facilitated by 'open world' systems environments. In order to develop effective information systems capable of supporting the distributed nature of emerging 'architectures of participation', it is necessary to devise adequate 'semantic structures', which in turn rely on sound and explicit conceptual frameworks, such as ontologies. However, there aren't enough 'ontologies' in the public domain that can be referenced to establish compatibility of architectures and serve as guidelines for the development of open, neutral and accountable information systems. In this paper we a) describe and analyse the 'distributed' and 'networked' nature of emergency operations b) put forward the notion information systems to support of emergency management today should be modeled on 'distributed' and networked organizational structures, and that ontologies in this domain should be built accordingly.
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André Dittrich, & Christian Lucas. (2013). A step towards real-time analysis of major disaster events based on tweets. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 868–874). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data.
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Efstratios Kontopoulos, Panagiotis Mitzias, Jürgen Moßgraber, Philipp Hertweck, Hylke van der Schaaf, Désirée Hilbring, et al. (2018). Ontology-based Representation of Crisis Management Procedures for Climate Events. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1064–1073). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: One of the most critical challenges faced by authorities during the management of a climate-related crisis is the overwhelming flow of heterogeneous information coming from humans and deployed sensors (e.g. cameras, temperature measurements, etc.), which has to be processed in order to filter meaningful items and provide crisis decision support. Towards addressing this challenge, ontologies can provide a semantically unified representation of the domain, along with superior capabilities in querying and information retrieval. Nevertheless, the recently proposed ontologies only cover subsets of the relevant concepts. This paper proposes a more “all-around” lightweight ontology for climate crisis management, which greatly facilitates decision support and merges several pertinent aspects: representation of a crisis, climate parameters that may cause climate crises, sensor analysis, crisis incidents and related impacts, first responder unit allocations. The ontology could constitute the backbone of the decision support systems for crisis management.
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Elmhadhbi Linda, Karray Mohamed Hedi, & Archimède Bernard. (2018). Towards an Operational Emergency Response System for Large Scale Situations: POLARISC. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 778–785). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: After a lot of recent natural and human-made disasters all over the word, the large scale emergency response process is becoming very critical and challenging. Lives can be lost and property can be harmed. To respond to these major threats, an effective operational emergency response system needs to address the necessity of data sharing, information exchange and correlation between different Emergency Responders (ERs) including firefighters, police, health care services, army, municipality and so on to successfully respond to large scale disasters. Therefore, the goal of this paper is to introduce POLARISC, an interoperable software solution based on a common and modular ontology shared by all the ERs. Its main objective is to solve the problem of semantic difference and heterogeneity of data to guarantee a common understanding among the various ERs in order to coordinate and to obtain a real time operational picture of the situation.
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Eva Blomqvist, Vitaveska Lanfranchi, Suvodeep Mazumdar, Tomi Kauppinen, & Carsten Kessler. (2015). Workshop summary: Workshop on Semantics and Analytics for Emergency Response (SAFE2015). In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: The Emergency Response domain is a highly challenging domain, requiring the active collaboration of several experts and authorities on the one hand and large-scale data analysis on the other. This poses significant challenges in sharing and analysing highly dynamic data describing highly evolving situations. This paper provides a brief summary for the first workshop in the SAFE workshop series. The workshop is aimed at bringing together analysts, practitioners, researchers and enthusiasts and provides a discussion ground for practical problems, solutions and projects that exploit Semantic Web, Linked Data analytics for Emergency Response. Following a round of thorough reviews, four papers are accepted and a keynote will complement the paper presentations along with a few discussion sessions.
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Dirk Fahland, Timo Mika Gläßer, Bastian Quilitz, Stephan Weißleder, & Ulf Leser. (2007). HUODINI-flexible information integration for disaster management. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 255–262). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Fast and effective disaster management requires access to a multitude of heterogeneous, distributed, and quickly changing data sets, such as maps, satellite images, or governmental databases. In the last years, also the information created by affected persons on web sites such as flickr.com or blogger.com became an important and very quickly adapting source of information. We developed HUODINI, a prototype system for the flexible integration and visu-alization of heterogeneous data sources for disaster management. HUODINI is based on Semantic Web technologies, and in particular RDF, to offer maximal flexibility in the types of data sources it can integrate. It supports a hybrid push/pull approach to cater for the requirements of fast-changing sources, such as news feeds, and maximum performance for querying the integrated data set. In this paper, we describe the design goals underlying our approach, its architecture, and report on first experiences with the system.
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Fiona Jennet McNeill, Diana Bental, Jeremy Bryan, Paolo Missier, Jannetta S. Steyn, & Tom Kumar. (2019). Communication in Emergency Management through Data Integration and Trust: an introduction to the CEM-DIT system. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper discusses the development of the CEM-DIT (Communication in Emergency Management through Data
Integration and Trust) system, which allows decision makers in crises to send out automated data requests to multiple
heterogeneous and potentially unknown sources and interactively determine how reliable, relevant and trustworthy
the responses are. We describe the underlying technology, which is based partially on data integration and matching,
and partly on utilisation of provenance data. We describe our cooperation with the Urban Observatory (UO), which
allows us to develop the system in collaboration with developers of the kind of crisis-relevant data which the system
is designed for. The system is currently in development, and we describe which parts are fully implemented and
which are currently being developed.
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Francisco José Quesada Real, Fiona McNeill, Gábor Bella, & Alan Bundy. (2018). Identifying Semantic Domains in Emergency Scenarios. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1130–1132). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Emergency scenarios are characterised by the participation of multiple and diverse organisations which come from different areas. This diversity is enriching in terms of expertise and approaches to tackle problems, however, it also provokes misunderstandings caused by semantic interoperability problems. There are some approaches which propose tackling these problems by using domain adaptation algorithms. Nevertheless, it is not trivial their application in emergency scenarios where the term “domain” is used in many different ways, not being clear either what it means or which domains are involved in these scenarios. In this paper, we identify semantic domains involved in emergency scenarios by analysing papers published in proceedings of ISCRAM and ISCRAM-med conferences. As a result, a categorisation of these domains has been developed, with the aim of providing a resource that can be used by domain adaptation algorithms to tackle problems such as those involving semantic interoperability.
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Aygul Gabdulkhakova, Birgitta König-Ries, & Dmitry Rizvanov. (2012). Rational resource allocation in mass casualty incidents – Adaptivity and efficiency. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Mass casualty incidents (MCI) are highly dynamic situations in which limited available resources need to be quickly and efficiently allocated. In this paper, we suggest considerable extensions to an allocation method that we presented in earlier work. The extensions address two major challenges: First, the need to balance real-world resource usage and second, the need to adapt to changing situations. Additionally, a theoretical evaluation of the efficiency of the suggested approach is described. © 2012 ISCRAM.
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Aygul Gabdulkhakova, Birgitta König-Ries, Mareike Mähler, Yeliz Yildirim-Krannig, & Fabian Wucholt. (2011). Identifying and supporting information needs in mass casualty incidents – An interdisciplinary approach. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In mass casualty incidents (MCIs) different authorities and organizations with safety responsibilities (BOS) act in highly dynamic situations. BOS operating in MCI-scenarios have a large demand of different information. SpeedUp, a German government-funded research project, wants to support this information demand. From an IT-perspective, our basic concept is to model available resources (e.g., sources of information and communicative devices) as services and flexibly combine them to the information demand of the BOS. To achieve this, we have to know which kind of information is needed by whom and explore the structures, tasks and roles of the BOS involved. In this paper we employed an interdisciplinary and user – centered approach. It is the result of a close cooperation of two research groups: one from the Intercultural Business Communication (IWK) and one from the chair of computer sciences. While the IWK focused on identifying information needs via expert interviews and observations, the computer scientists were looking at the possibilities for technical support of these needs. Only both disciplines together can achieve viable solutions.
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Gerasimos Antzoulatos, Panagiotis Giannakeris, Ilias Koulalis, Anastasios Karakostas, Stefanos Vrochidis, & Ioannis Kompatsiaris. (2020). A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 75–89). Blacksburg, VA (USA): Virginia Tech.
Abstract: Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of risk-based decision support systems which encapsulate the technological achievements in Geographical Information Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand, the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of large amounts of multimedia in social media or other online repositories has increased the interest in the image understanding domain. Recent computer vision techniques endeavour to solve several societal problems with security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist online in social media or news articles a great deal of them might include the existence of a crisis or emergency event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a) an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion level so as to showcase our method's applicability.
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Beibei Hu, Jan Hidders, Marc De Lignie, & Philipp Cimiano. (2011). A rule-based system for contextualized information delivery. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: When carrying out tasks, police officers need up-to-date information contextualized to their current situation to support them in decision making. The results of a previous user study with the aim of capturing the information requirements of police officers have led to the implementation of a rule-based system for contextualized information delivery. In this paper, we present the overall system and discuss how the various sources of information are modelled using ontologies. Our focus is on the formalism for expressing the rules and the engine executing those rules to decide which information is relevant for specific users. These declarative rules can be modified independently of the code executing them, thus providing a principled way to adapt the system to new domains. Quantitative evaluations on scenarios constructed in cooperation with police officers show that precision and recall levels of our system are satisfactory compared to other systems and that our system can be adapted to new scenarios with reasonable efforts.
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Humasak Simanjuntak, & Fabio Ciravegna. (2019). Semantic Understanding of Human Mobility Lifestyle to support Crisis Management. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: In this paper, we propose a method for understanding the semantics of mobility (mainly related to lifestyle)
patterns based on stay point detection from tracking data. The method identifies the context (trip purpose and
visited point of interest) of tracking data by using large-scale data collection infrastructure. We evaluate our
method with a tracking dataset in Birmingham (European project SETA) generated by 534 users from
September 2017 to September 2018. To this end, we compare insights from the tracking data with check-in
mobility in social media. The results show that both data capture rich human lifestyle features related to the
visited point of interest. Our study provides solid evidence that lifestyle patterns from tracking and social media
data can indeed be useful for understanding and gauging the level of disruption after a crisis, as it is possible to
check the deviation of habits from normal conditions and post-crisis.
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Yasir Javed, Tony Norris, & David Johnston. (2011). Ontology-based inference to enhance team situation awareness in emergency management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this paper, we propose the use of an ontology-based and semantic technologies approach to improving shared situation awareness amongst teams dealing with emergency situations. We have also identified that shared and team situation awareness tends to be viewed only in terms of cooperative task completion and so we have tried to describe their important relationship with team decision making. The applicability of our approaches is demonstrated by a case study of mass evacuation in the case of a tsunami event. We show how ontology can be used to represent context-based situations and how the axioms and rules can improve team situation awareness.
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Jens Kersten, Jan Bongard, & Friederike Klan. (2021). Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 744–754). Blacksburg, VA (USA): Virginia Tech.
Abstract: Twitter is an immediate and almost ubiquitous platform and therefore can be a valuable source of information during disasters. Current methods for identifying and classifying crisis-related content are often based on single tweets, i.e., already known information from the past is neglected. In this paper, the combination of tweet-wise pre-trained neural networks and unsupervised semantic clustering is proposed and investigated. The intention is to (1) enhance the generalization capability of pre-trained models, (2) to be able to handle massive amounts of stream data, (3) to reduce information overload by identifying potentially crisis-related content, and (4) to obtain a semantically aggregated data representation that allows for further automated, manual and visual analyses. Latent representations of each tweet based on pre-trained sentence embedding models are used for both, clustering and tweet classification. For a fast, robust and time-continuous processing, subsequent time periods are clustered individually according to a Chinese restaurant process. Clusters without any tweet classified as crisis-related are pruned. Data aggregation over time is ensured by merging semantically similar clusters. A comparison of our hybrid method to a similar clustering approach, as well as first quantitative and qualitative results from experiments with two different labeled data sets demonstrate the great potential for crisis-related Twitter stream analyses.
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Joao Moreira, Luis Ferreira Pires, & Marten Sinderen. (2019). SEMIoTICS: Semantic Model-Driven Development for IoT Interoperability of Emergency Services. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Modern early warning systems (EWSs) use Internet-of-Things (IoT) technologies to realize real-time data acquisition, risk detection and message brokering between data sources and warnings? destinations. Interoperability is crucial for effective EWSs, enabling the integration of components and the interworking with other EWSs. IoT technologies potentially improve the EWS efficiency and effectiveness, but this potential can only be exploited if interoperability challenges are properly addressed. The three main challenges for interoperability are: (1) achieving semantic integration of a variety of data sources and different representations; (2) supporting time- and safety-critical applications with performance and scalability; and (3) providing data analysis for effective responses with personalized information requirements. In this paper, we describe the ?SEmantic Model-driven development for IoT Interoperability of emergenCy serviceS? (SEMIoTICS) framework, which supports the development of semantic interoperable IoT EWSs. The framework has been validated with a pilot performed with accident use cases at the port of Valencia. The validation results show that it fulfils the requirements that we derived from the challenges above.
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Fahem Kebair, & Frédéric Serin. (2008). Towards an intelligent system for risk prevention and emergency management. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 526–535). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Making a decision in a changeable and dynamic environment is an arduous task owing to the lack of information, their uncertainties and the unawareness of planners about the future evolution of incidents. The use of a decision support system is an efficient solution for this issue. Such a system can help emergency planners and responders to detect possible emergencies, as well as to suggest and evaluate possible courses of action to deal with the emergency. We are interested in our work to the modelling of a monitoring preventive and emergency management system, wherein we stress the generic aspect. In this paper we propose an agent-based architecture of this system and we describe a first step of our approach which is the modeling of information and their representation using a multiagent system.
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Rita Kovordanyi, Rudolf Schreiner, Jelle Pelfrene, Johan Jenvald, Henrik Eriksson, Amy Rankin, et al. (2012). Real-time support for exercise managers' situation assessment and decision making. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Exercise managers and instructors have a particularly challenging task in monitoring and controlling on-going exercises, which may involve multiple response teams and organizations in highly complex and continuously evolving crisis situations. Managers and instructors must handle potentially incomplete and conflicting field-observation data and make decisions in real-time in order to control the flow of the exercise and to keep it in line with the training objectives. In simulation-based exercises, managers and instructors have access to a rich set of real-time data, with an increased potential to closely monitor the trainees' actions, and to keep the exercise on track. To assist exercise managers and instructors, data about the on-going exercise can be filtered, aggregated and refined by real-time decision-support systems. We have developed a model and a prototype decision-support system, using stream-based reasoning to assist exercise managers and instructors in real-time. The approach takes advantage of topic maps for ontological representation and a complex-event processing engine for analyzing the data stream from a virtual-reality simulator for crisis-management training. Aggregated data is presented both on-screen, in Twitter, and in the form of topic maps. © 2012 ISCRAM.
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