José H. Canós-Cerdá, Carmen Penadés, Carlos Solís, Marcos R. S. Borges, & Manuel Llavador. (2010). Using spatial hypertext to visualize composite knowledge in emergency responses. 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: Having the right information at the right time is crucial to make decisions during emergency responses. To fulfill this requirement, emergency management systems must provide emergency managers with knowledge management and visualization tools. The goal is twofold: on one hand, to organize knowledge coming from different sources, mainly the emergency response plans (the formal knowledge) and the information extracted from the emergency development (the contextual knowledge); on the other hand, to enable effective access to information. Formal and contextual knowledge sets are mostly disjoint; however, there are cases in which a formal knowledge piece may be updated with some contextual information, constituting what we call the composite knowledge. In this paper, we extend a knowledge framework with the notion of composite knowledge, and use spatial hypertext to visualize this type of knowledge. We illustrate our proposal with a case study on accessing to information during an emergency response in an underground transportation system.
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Craig E. Kuziemsky, Ahsan Hadi, Tracey L. O'Sullivan, Daniel E. Lane, & Wayne Corneil. (2014). An ontology for contextual information system design. 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. 165–169). University Park, PA: The Pennsylvania State University.
Abstract: Collaborative teamwork is becoming more common in several domains including healthcare and disaster management. While collaborative teamwork can benefit from information system (IS) support, designing IS models to support collaboration is a significant challenge owing to the variations in tasks and people that must be supported, and the different contexts within which collaboration takes place. Collaborative teamwork can vary greatly because of context, which is the integration of diverse, dynamic, and heterogeneous needs for groups to achieve a specific goal. However in the literature there has been limited emphasis on how contextual underpinnings can be incorporated into IS design. This paper uses a case study of the design of a user-driven prototype disaster management IS. We used the think aloud method to capture participant thoughts while interacting with the IS prototype. The think aloud data was analyzed and used to develop an ontology of contextual considerations to support IS design.
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Angel Ruiz-Zafra, Ana-Gabriela Núñez, Carmen Penadés, José H. Canós-Cerdá, & Marcos R. S. Borges. (2014). SUCRE: Supporting users, controllers and responders in emergencies. 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. 255–259). University Park, PA: The Pennsylvania State University.
Abstract: We use the term “Personal Safety Assistants” (PSAs) to refer to a family of mobile information systems that intend to reduce the risks of both citizens and responders in emergency responses. Using their mobile devices, they can access to personalized views of the emergency plans including context-aware evacuation instructions or real time guidance to specific locations for rescue operations, among others. Additionally, both responders and citizens act as context sources sending fresh information (e.g. pictures of damaged areas) to the command and control center, increasing situational awareness. In this paper, we show how the SUCRE infrastructure collects and processes contextual information to improve the information infrastructure during responses. We describe the current status of the system and outline the incoming enhancements.
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Evan A. Sultanik, & Clayton Fink. (2012). Rapid geotagging and disambiguation of social media text via an indexed gazetteer. 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: Microblogging services like Twitter afford opportunities for real time determination of situation awareness during crises as people report, via their statuses, information about events on the ground. An important component of the information included in a tweet are mentions of place names that may be sites of damage, injuries, or relief efforts. Methods for extracting these place names and determining the actual location being referenced are an essential part of the suite of tools required for automated extraction of situation awareness from tweets. Extracting and disambiguating place name mentions from text have been areas of extensive research. Twitter, however, presents challenges given the 140 character restriction on status and the informal, abbreviated language that are a norm in this communication channel. Named entity recognizers, which are dependent on labeled training data, may not be useful in this medium for extracting location mentions because the typical training domains for these taggers are absent the noise found in Twitter statuses. Additionally, the contextual information that is necessary for disambiguating place names is not always present. In this paper, we demonstrate a new technique, RapidGeo, for extracting and disambiguating place names from a location specific Twitter feed using an unsupervised technique for tagging location mentions and relying on the known geographic context of the feed for disambiguation. Our location tagging technique performs much better than an off-the-shelf named entity recognizer and we achieve reasonable precision in disambiguating extracted place names. We argue that such fast, high precision, unsupervised approaches are needed when important, actionable information is required from noisy data sources such as Twitter. © 2012 ISCRAM.
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