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Axel Schulz, Heiko Paulheim, & Florian Probst. (2012). Crisis information management in the Web 3.0 age. 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 effectiveness of emergency response largely depends on having a precise, up-to-date situational picture. With the World Wide Web having evolved from a small read-only text collection to a large-scale collection of socially created data accessible both to machines and humans alike, with the advent of social media and ubiquitous mobile applications, new sources of information are available. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. In this paper, we show an approach for turning massive amounts of unstructured citizen-generated content into relevant information supporting the command staff in making better informed decisions. We leverage Linked Open Data and crowdsourcing for processing data from social media, and we show how the combination of human intelligence in the crowd and automatic approaches for enhancing the situational picture with Linked Open Data will lead to a Web 3.0 approach for more efficient information handling in crisis management. © 2012 ISCRAM.
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Daniel Stein, Barbara Krausz, Jobst Löffler, Robin Marterer, Rolf Bardeli, Jochen Schwenninger, et al. (2012). Enriching an intelligent resource management system with automatic event recognition. 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: Event recognition systems have high potential to support crisis management and emergency response. Given the vast amount of possible input channels, automatic processing of raw data is crucial. In this paper, we describe several components integrated in an overall intelligent resource management system, namely abnormal event detection in audio and video material, as well as automatic speech recognition within a public safety network. We elaborate on the challenges expected from real life data and the solutions that we applied. The overall system, based on Event-Driven Service-Oriented Architecture, has been implemented and partly integrated into the end users' infrastructures. The system is continuously running since almost two years, collecting data for research purposes. © 2012 ISCRAM.
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