John Edmonds, Louiqa Raschid, Hassan Sayyadi, & Shanchan Wu. (2010). Exploiting social media to provide humanitarian users with event search and recommendations. 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: Humanitarian decision makers rely on timely and accurate information for decision-making. Since satisfactory disaster response is key to building public trust and confidence, they need to monitor and track disaster related discourse to gauge public perception and to avert public relations disasters. Social media, e.g., the blogosphere, has empowered citizens to provide content and has increased information diversity. The challenge is to make sense of this diverse and noisy data and interpret results in context. For example, search results can be clustered around an event or occurrence at some geo-location and time. Personalization and recommendations can further filter content and focus on the most relevant and important data. We apply our research on event detection and recommendation to support event based search and apply it to a large blog collection (blog.spinn3r.com).
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Edward J. Glantz, Frank E. Ritter, Don Gilbreath, Sarah J. Stager, Alexandra Anton, & Rahul Emani. (2020). UAV Use in Disaster Management. 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. 914–921). Blacksburg, VA (USA): Virginia Tech.
Abstract: Unmanned aerial vehicles (UAV) provide multiple opportunities to first responders and disaster managers, especially as they continue to improve in affordability as well as capabilities. This paper provides a brief review of how UAV capabilities have been used in disaster management, examples of current use within disaster management, as well as adoption considerations. Example disaster domains include fires, tornadoes, flooding, building and dam collapses, crowd monitoring, search and rescue, and post disaster monitoring of critical infrastructures. This review can increase awareness and issues when considering UAVs by those challenged with the management of crisis and disaster events.
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Kevin Fall, Gianluca Iannaccone, Jayanthkumar Kannan, Fernando Silveira, & Nina Taft. (2010). A disruption-tolerant architecture for secure and efficient disaster response communications. 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: We consider the problem of providing situational awareness when citizens in a disaster are willing to contribute their own devices, such as laptops and smart phones, to gather data (text, images, audio or video) and to help forward data gathered by others. A situational awareness service processes all received data and creates annotated maps to visualize a disaster site (e.g., the status of the disaster, such as fires or floods, the location of people, food, or water). We discuss the challenges imposed on such an application when 1) the communications infrastructure in the disaster area can only provide intermittent connectivity, 2) anxious victims generate large amounts of redundant content congesting the network, and 3) the sharing of personal devices creates security and privacy threats. We present an architecture that addresses the requirements to support such a service.
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Zeno Franco, Syed Ahmed, Craig E. Kuziemsky, Paul A. Biedrzycki, & Anne Kissack. (2013). Using social network analysis to explore issues of latency, connectivity, interoperability & sustainability in community disaster response. 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. 896–900). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Community-based disaster response is gaining attention in the United States because of major problems with domestic disaster recovery over the last decade. A social network analysis approach is used to illustrate how community-academic partnerships offer one way to leverage information about existing, mediated relationships with the community through trusted actors. These partnerships offer a platform that can be used to provide entré into communities that are often closed to outsiders, while also allowing greater access to community embedded physical assets and human resources, thus facilitated more culturally appropriate crisis response. Using existing, publically available information about funded community-academic partnerships in Wisconsin, USA, we show how social network analysis of these meta-organizations may provide critical information about both community vulnerabilities in disaster and assist in rapidly identifying these community resources in the aftermath of a crisis event that may provide utility for boundary spanning crisis information systems.
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Lucy T. Gunawan, Martin Voshell, Stijn Oomes, & David D. Woods. (2007). Envisioning collaboration at a distance for the evacuation of walking wounded. 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. 431–437). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The “walking wounded” is a category of disaster victims that can help themselves in finding their way to safety. The problem we address here is how first responders, walking wounded, and other rescue personnel can coordinate their joint activities more efficiently in order to accomplish the evacuation as quickly as possible. We focus our design on the “coordination loops” in the disaster response organization, both vertically across levels of authority, and horizontally among responders in the same echelon. In our envisioned scenario of a chemical accident we identify the most important interactions through which activities are coordinated that are crucial for a successful evacuation. We propose three different “coordination devices” that can be used by the walking wounded, the rescuers in the fields, and the people in the command center. We believe our approach, explicitly designing support systems for coordination first, will lead to important improvements in the daily practice of disaster response.
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Lucy T. Gunawan, Siska Fitrianie, Willem-Paul Brinkman, & Mark A. Neerincx. (2012). Utilizing the potential of the affected population and prevalent mobile technology during disaster response: Propositions from a literature survey. 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: Despite the growing awareness of the untapped potential of the affected population in a disaster situation, their inclusion in a disaster management is extremely limited. This study aims to survey the literature to see whether utilizing the affected people and prevalent mobile technology can be used during disaster response. The idea is to provide the affected with a way to lead themselves to safety and empower them to serve as distributed active sources of information. This way, those people will reach safety by themselves, while at the same time helping to construct a clear image of the disaster situation without burdening the already overwhelmed emergency services. This study examines knowledge derived from disaster sociology, draws on experience from recent disasters, and extrapolates current technological solutions. By establishing that such a solution is feasible, it offers a basis for empirical studies on a mobile technology that can be used during disaster response. © 2012 ISCRAM.
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Hafiz Budi Firmansyah, Jesus Cerquides, & Jose Luis Fernandez-Marquez. (2022). Ensemble Learning for the Classification of Social Media Data in Disaster Response. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 710–718). Tarbes, France.
Abstract: Social media generates large amounts of almost real-time data which has proven valuable in disaster response. Specially for providing information within the first 48 hours after a disaster occurs. However, this potential is poorly exploited in operational environments due to the challenges of curating social media data. This work builds on top of the latest research on automatic classification of social media content, proposing the use of ensemble learning to help in the classification of social media images for disaster response. Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Experimental results show that ensemble learning is a valuable technology for the analysis of social media images for disaster response,and could potentially ease the integration of social media data within an operational environment.
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Hans Betke. (2015). Structure and Elements of Disaster Response Processes ? A General Meta-Model. 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).
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Erich Heumüller, Sebastian Richter, & Ulrike Lechner. (2012). Towards a framework for command post exercises. 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: The paper describes work-in-progress of an action research approach investigating command post exercises in disaster response organizations. The empirical basis of our approach is three command post exercises in Germa-ny. The main contribution of this paper is the framework for the conceptualization of command post exercises and an analysis of challenges in the conceptualization of command post exercises. © 2012 ISCRAM.
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Marlen Hofmann, Stefan Sackmann, & Hans Betke. (2013). A novel architecture for disaster response workflow management systems. 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. 338–343). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Due to the shared focus of disaster response management (DRM) and business process management on activities as well as the obvious similarity of disaster response processes (DRP) and business processes, the application of workflow management systems (WfMS) has been discussed as a promising approach to manage DRP. However, the application of WfMS in DRM has not yet been realized in practice. One reason for this is the lack of methods and tools in WfMS for taking interdependencies between activities, time, resource, and place into consideration. This considerably restricts the variety of DRP. Therefore, a novel architecture for a disaster response workflow management system is discussed. A special focus lies on the management and analysis of interdependencies that is seen as very promising to improve future DRM.
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Hongmin Li, Nicolais Guevara, Nic Herndon, Doina Caragea, Kishore Neppalli, Cornelia Caragea, et al. (2015). Twitter Mining for Disaster Response: A Domain Adaptation Approach. 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: Microblogging data such as Twitter data contains valuable information that has the potential to help improve the speed, quality, and efficiency of disaster response. Machine learning can help with this by prioritizing the tweets with respect to various classification criteria. However, supervised learning algorithms require labeled data to learn accurate classifiers. Unfortunately, for a new disaster, labeled tweets are not easily available, while they are usually available for previous disasters. Furthermore, unlabeled tweets from the current disaster are accumulating fast. We study the usefulness of labeled data from a prior source disaster, together with unlabeled data from the current target disaster to learn domain adaptation classifiers for the target. Experimental results suggest that, for some tasks, source data itself can be useful for classifying target data. However, for tasks specific to a particular disaster, domain adaptation approaches that use target unlabeled data in addition to source labeled data are superior.
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Humaira Waqas, & Muhammad Imran. (2019). #CampFireMissing: An Analysis of Tweets About Missing and Found People From California Wildfires. 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: Several research studies have shown the importance of social media data for humanitarian aid. Among others,
the issue of missing and lost people during disasters and emergencies is crucial for disaster managers. This work
analyzes Twitter data from a recent wildfire event to determine its usefulness for the mitigation of the missing and
found people issue. Data analysis performed using various filtering techniques, and trend analysis revealed that
Twitter contains important information potentially useful for emergency managers and volunteers to tackle this
issue. Many tweets were found containing full names, partial names, location information, and other vital clues
which could be useful for finding missing people.
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Soumia Ichoua. (2010). Humanitarian logistics network design for an effective disaster response. 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: In this paper we address the problem of pre-positioning emergency supplies prior to a disaster onset. The goal is to ensure a fast and effective response when the disaster strikes. Pre-positioning of emergency supplies is a strategic decision aimed at determining the number and location of local distribution centers as well as their inventory levels for emergency supplies. These decisions must be made in a highly disruption-prone environment where a timely response is vital and resources are scarce. We present and discuss a scenario-based model that integrates location, inventory and routing decisions.
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Muhammad Imran, Carlos Castillo, Jesse Lucas, Patrick Meier, & Jakob Rogstadius. (2014). Coordinating human and machine intelligence to classify microblog communications in crises. 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. 712–721). University Park, PA: The Pennsylvania State University.
Abstract: An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowd sourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real world datasets.
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Murray E. Jennex. (2007). Reflections on strong angel III: Some lessons learned. 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. 537–544). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Strong Angel III was a civilian military disaster response demonstration held in San Diego in /August, 2006. This demonstration resulted in the generation of a great deal of knowledge that can potentially benefit disaster response efforts world wide. This paper attempts to capture this knowledge and to reflect on the demonstration for its value to the community.
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Jeremy Diaz, Lise St. Denis, Maxwell B. Joseph, Kylen Solvik, & Jennifer K. Balch. (2020). Classifying Twitter Users for Disaster Response: A Highly Multimodal or Simple Approach? 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. 774–789). Blacksburg, VA (USA): Virginia Tech.
Abstract: We report on the development of a classifier to identify Twitter users contributing first-hand information during a disaster. Identifying such users helps social media monitoring teams identify critical information that might otherwise slip through the cracks. A parallel study (St. Denis et al., 2020) demonstrates that Twitter user filtering creates an information-rich stream of content, but the best way to approach this task is unexplored. A user's profile contains many different “modalities” of data, including numbers, text, and images. To integrate these different data types, we constructed a multimodal neural network that combines the loss function of all modalities, and we compared the results to many individual unimodal models and a decision-level fusion approach. Analysis of the results suggests that unimodal models acting on Twitter users' recent tweets are sufficient for accurate classification. We demonstrate promising classification of Twitter users for crisis response with methods that are (1) easy to implement and (2) quick to both optimize and infer.
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José J. González, José H. Canós-Cerdá, Tony Norris, & Reem Abbas. (2018). Towards Disaster e-Health Support Systems. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 438–443). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Disaster management and the health sector ought to be natural allies, but their different origins, culture, and priorities of the various agencies tasked with disaster response mean that communication and coordination between them is often lacking, leading to delayed, sub-standard, or inappropriate care for disaster victims. The potential of the new e-health technologies, such as the electronic health record, telehealth and mobile health, that are revolutionizing non-disaster healthcare, is also not being realised. These circumstances have led to an international project to develop a disaster e-health framework for the objectives of intelligent adaption to changing scenarios, presentation and management of information, and communication and collaboration. In this paper, we describe characteristics of disaster e-health support systems to achieve such set of objectives.
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Julian Zobel, Patrick Lieser, Tobias Meuser, Lars Baumgärtner, Mira Mezini, & Ralf Steinmetz. (2021). Modeling Civilian Mobility in Large-Scale Disasters. 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. 119–132). Blacksburg, VA (USA): Virginia Tech.
Abstract: When disasters destroy critical communication infrastructure, smartphone-based Delay-Tolerant Networks (DTNs) can provide basic communication for civilians. Although field tests have shown the practicability of such systems, real-world experiments are expensive and hardly repeatable. Simulations are therefore required for the design and extensive evaluation of novel DTN protocols, but meaningful assertions require realistic mobility models for civilians. In this paper, trace files from a large-scale disaster field test are analyzed to identify typical human behavior patterns in a disaster area. Based on this, we derive a novel civilian disaster mobility model that incorporates identified behaviors such as group-based movement and clustering around points-of-interests such as hospitals and shelters. We evaluate the impact of mobility on DTN communication performance by comparing our model with other established mobility models as well as the trace file dataset in a simulative evaluation based on the field test scenario. In general, our mobility model leads to a more realistic assessment of DTN communication performance compared to other mobility models.
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Kiran Zahra, Muhammad Imran, & Frank O Ostermann. (2018). Understanding eyewitness reports on Twitter during disasters. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 687–695). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other uses. However, identification of eyewitness reports on Twitter is challenging for many reasons. This work investigates the sources of tweets and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitness, and (iii) vulnerable accounts. Moreover, we investigate various characteristics associated with each kind of eyewitness account. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We believe these characteristics can help make more efficient computational methods and systems in the future for automatic identification of eyewitness accounts.
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Koki Asami, Shono Fujita, Kei Hiroi, & Michinori Hatayama. (2022). Data Augmentation with Synthesized Damaged Roof Images Generated by GAN. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 256–265). Tarbes, France.
Abstract: The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
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Uwe Krüger, Fabian Wucholt, & Clemens Beckstein. (2012). Electronic checklist support for disaster response. 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: Requirements analysis of IT-support for rescue management showed that electronic checklist support is a vital function of any IT-based assistance system. Although checklists are a simple approach, their successful implementation and use depends on many factors. We nevertheless believe that Intelligent Electronic Checklist Sup-port Systems (IECSS) are especially helpful for the (inter-) organizational cooperation in disaster scenarios like mass casualty incidents (MCIs). In this paper we describe why, when, and how electronic checklists can be used to coordinate the work of the geographically dispersed rescue forces. For this purpose we will have a look at safety-critical and complex tasks in aviation and medicine where checklists already are successfully used and try to profit from this experience for the MCI domain. © 2012 ISCRAM.
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Jay Lickfett, Naveen Ashish, Sharad Mehrotra, Nalini Venkatasubramanian, & Jacob Green. (2008). The RESCUE Disaster Portal for disasters and 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. 787–796). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper describes the Disaster Portal, a multi-faceted information portal for use by citizens and emergency personnel during disasters and emergency response. This portal is the result of a collaboration between the RESCUE project at UC-Irvine and the City of Ontario, California Fire Department (OFD). The portal provides a wide range of real-time information in disaster situations, such as situation summaries, announcements, shelter information, and aggregated services such as family reunification and donation management. A first version of this portal was developed and deployed by the City of Ontario in September 2007. The current system serves as a base to develop and refine results from several areas of research which are being incorporated into the existing system to provide additional or advanced capabilities. We provide an overview of the portal's capabilities, followed by technical details on the system architecture and implementation. We describe the experience of deployment of the system in the recent California wildfires. Finally we describe work in progress on several advanced capabilities.
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Linda Elmhadhbi, Mohamed-Hedi Karray, & Bernard Archimède. (2019). A Modular Ontology for Semantically Enhanced Interoperability in Operational Disaster Response. 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: Up to now, the world has witnessed how inadequate communication capabilities can adversely affect disaster response efforts. There are various Emergency Responders (ERs) that potentially must work together towards a successful resolution of the disaster. However, the different terminologies and technical vocabularies that are being exchanged between the ERs may lead to a misunderstanding and lack of semantic integrity. Yet, understanding the semantics of the exchanged data is one of the major challenges. The purpose of this work is to define the complex knowledge of the ERs by proposing a common and modular ontology shared by all the stakeholders so as to come up with a common shared vocabulary in order to ensure semantic interoperability between ERs. In this paper, we present POLARISCO and we discuss how it was developed using Basic Formal Ontology as an upper-level ontology and Common Core Ontology as a mid-level ontology to define each module.
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Marlen Hofmann, Hans Betke, & Stefan Sackmann. (2015). Automated Analysis and Adaptation of Disaster Response Processes with Place-Related Restrictions. 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: For recent years, disaster response management is considered as a promising field for applying methods and tools from business process management. Especially the development of adaptive workflow management systems (WfMS) brought a process-oriented management of highly dynamic disaster response processes (DRP) within tangible reach. However, time criticality, unpredictability or complex and changing disaster reality make it impossible to analyze and adapt ongoing DRP within reasonable time manually. Hence, to foster the application of disaster response WfMS in practice, it becomes mandatory to develop methods supporting an (semi-)automated analyses and adaption of ongoing DRP. Addressing this research gap, we present a novel method called DRP-ADAPT which analyzes given DRP models with respect to place-related conflicts and resolves inoperable response activities (semi-)automatically by process adaptation.
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Marlen Hofmann, Stefan Sackmann, & Hans Betke. (2015). Using Precedence Diagram Method in Process-Oriented Disaster Response Management. 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: When planning and modeling disaster response processes (DRP), the unpredictability of disasters precludes accounting for all eventualities in advance. DRPs are thus typically concretized and adapted after the disaster and during the process?s run-time. Since time is critical and uncertainty typical, planning of DRPs requires methods and tools that support disaster managers in process analysis, process adaptation, and decision making. This contribution presents an approach for identifying concurrent activities that, in needing identical resources at the same time in different locations, are jeopardized by such place-related conflicts. As solution, the approach allows managers to calculate valid execution sequences, eliminate place-related conflicts, and prioritize activities by total execution time. Results are shown to form a novel, reliable basis for contributing to disaster managers? decision support.
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