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Bruce D. Campbell, Konrad E. Schroder, & Chris E. Weaver. (2010). RimSim visualization : An interactive tool for post-event sense making of a first response effort. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Upon developing a software agent-based simulator for training roles in emergency response scenarios, the PARVAC team at the University of Washington has pursued building a tool for better investigative review and insight generation on the performance of an emergency response game session team. While our RimSim Response software included the opportunity to re-run a simulated team performance in order to review player and agent behavior, we did not provide our trainees the ability to visually query their performance outside of a sequential review of the emergency response effort. By integrating our RSR visualization components with an existing visual query software package called Improvise, we were able to construct highly-coordinated visualizations of our data model for the ability to apply a sense making approach in the investigation of live player and software agent-based behavior – both as individual players and as combinations of players working on tasks associated with an emergency response scenario. The resultant tool is now our primary visualization tool for discussing first responder team performance and supports the overall RSR objective of training teams to make the most effective, recognition-primed decisions when a real emergency crisis occurs in their community. This paper reviews our visualization tool and demonstrates its use.
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Francisco J. Quesada Real, Fiona McNeill, Gábor Bella, & Alan Bundy. (2017). Improving Dynamic Information Exchange in Emergency Response Scenarios. 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. 824–833). Albi, France: Iscram.
Abstract: Emergency response scenarios are characterized by the participation of multiple agencies, which cooperate to control the situation and restore normality. These agencies can come from diverse areas of expertise which entails that they represent knowledge dierently, using their own vocabularies and terminologies. This fact complicates the automation of the information-sharing process, creating problems such as ambiguity or specialisation. In this paper we present an approach to tackle these problems by domain-aware semantic matching. This method requires the formalisation of domain-specific terminologies which will be added to an existing system oriented to emergency response. Concretely, we have formalised terms from the UK Civil and Protection Terminology lexicon, which gathers some of the most common terms that UK agencies use in these scenarios.
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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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Fiona McNeill, Andriana Gkaniatsou, & Alan Bundy. (2014). Dynamic data sharing for facilitating communication during emergency responses. 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. 369–373). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the CHAIn system, which is designed to facilitate data sharing between disparate organisations during emergency response situations by resolving mismatches in their data. It uses structured data matching to reformulate failed queries in cases where these failed because of incompatibilities between the query (derived from the source schema) and the schema of the queried datasource (the target schema). This reformulation is done by developing matches between the source schema and the target schema. These matches are then used to reformulate the query and retrieve responses relevant to those expected by the original query. Despite the growing interest in intelligent query answering, integration of data matching into query answering is novel, and allows users to successfully query datasources even if they do not know how the data in that source is organized, which is often the case during emergency responses. We describe the proof-of-concept system we have developed and an encouraging initial evaluation.
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Pereira, J., Fidalgo, R., Lotufo, R., & Nogueira, R. (2023). Crisis Event Social Media Summarization with GPT-3 and Neural Reranking. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 371–384). Omaha, USA: University of Nebraska at Omaha.
Abstract: Managing emergency events, such as natural disasters, requires management teams to have an up-to-date view of what is happening throughout the event. In this paper, we demonstrate how a method using a state-of-the-art open-sourced search engine and a large language model can generate accurate and comprehensive summaries by retrieving information from social media and online news sources. We evaluated our method on the TREC CrisisFACTS challenge dataset using automatic summarization metrics (e.g., Rouge-2 and BERTScore) and the manual evaluation performed by the challenge organizers. Our approach is the best in comprehensiveness despite presenting a high redundancy ratio in the generated summaries. In addition, since all pipeline components are few-shot, there is no need to collect training data, allowing us to deploy the system rapidly. Code is available at https://github.com/neuralmind-ai/visconde-crisis-summarization.
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Stephen Potter, & Gerhard Wickler. (2008). Model-based query systems for emergency response. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 495–503). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this paper we describe the approach adopted and experiences gained during a project to develop a general architecture that aims to harness advanced sensor, modelling and Grid technologies to assist emergency responders in tackling emergencies (specifically fire emergencies). Here we focus on the command and control aspects of this architecture, and in particular, on a query-based approach that has been adopted to allow end users to interact with available models of physical and other phenomena. The development of this has provided a number of insights about the use of such models, which along with the approach itself, should be of interest to any considering similar applications.
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Schreiber. (2007). Automatic generation of sensor queries in a WSN for environmental monitoring. 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. 245–254). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The design of a WSN for environmental data monitoring is a largely ad-hoc human process. In this paper, we propose the automatic generation of queries for sensor data extraction, based on the collection of a number of parameters concerning the physical phenomenon to be controlled, the relevant physical variables, the types of sensors to be deployed and their allocation, the data collection frequencies, and other features.
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Zoha Sheikh, Hira Masood, Sharifullah Khan, & Muhammad Imran. (2017). User-Assisted Information Extraction from Twitter During Emergencies. 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. 684–691). Albi, France: Iscram.
Abstract: Disasters and emergencies bring uncertain situations. People involved in such situations look for quick answers to their rapid queries. Moreover, humanitarian organizations look for situational awareness information to launch relief operations. Existing studies show the usefulness of social media content during crisis situations. However, despite advances in information retrieval and text processing techniques, access to relevant information on Twitter is still a challenging task. In this paper, we propose a novel approach to provide timely access to the relevant information on Twitter. Specifically, we employee Word2vec embeddings to expand initial users queries and based on a relevance feedback mechanism we retrieve relevant messages on Twitter in real-time. Initial experiments and user studies performed using a real world disaster dataset show the significance of the proposed approach.
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