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    A collective intelligence resource management dynamic approach for disaster management: a density survey of disasters occurrence

    Asimakopoulou, E. and Bessis, N. and Sotiriadis, Stelios and Xhafa, F. and Barolli, L. (2011) A collective intelligence resource management dynamic approach for disaster management: a density survey of disasters occurrence. In: UNSPECIFIED (ed.) 2011 Third International Conference on Intelligent Networking and Collaborative Systems. IEEE. ISBN 9781457719080.

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    Abstract

    Currently, there is a growing interest in developing methods, systems and tools for managing disasters in a computational and integrated manner. This is due to the development of several next generation emerging technologies, which seem to be more fit for purpose. Various emerged distributed and computational paradigms include collective intelligence, Internet of things, social networking, context-aware, sensors and collaborative technologies such as grids, clouds and crowds, these are just few to name here. In our previous works, we have discussed and demonstrated the potential of these technologies for disaster management in a manner, which seems to be more synergetic towards an integrated and more informed decision-making. In this paper, the aim is two-fold: firstly, to demonstrate quantitative evidence supporting the increasing occurrence of disasters in terms of costs during the preparedness and recovery disaster stages. Secondly, there is current stagnation in resource matters, that is to say, it would be of particular importance to develop a more focused and resource balanced disaster management approach. Due to the data complexity and volume, our survey is predominantly focused on evidence from disasters occurred in 27 European countries.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Stelios Sotiriadis
    Date Deposited: 15 Jun 2018 11:00
    Last Modified: 03 Jul 2021 17:56
    URI: https://eprints.bbk.ac.uk/id/eprint/21884

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