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Towards a Knowledge (Experience)-Based Recommender System for Crisis Management

Negre, Elsa (2013), Towards a Knowledge (Experience)-Based Recommender System for Crisis Management, 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), IEEE : Piscataway, NJ, p. 713-718. 10.1109/3PGCIC.2013.121

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SNDE_2013_Elsa.pdf (360.4Kb)
Type
Communication / Conférence
Date
2013
Conference title
Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)
Conference date
2013-10
Conference city
Compiègne
Conference country
France
Book title
2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)
Publisher
IEEE
Published in
Piscataway, NJ
ISBN
978-0-7695-5094-7
Pages
713-718
Publication identifier
10.1109/3PGCIC.2013.121
Metadata
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Author(s)
Negre, Elsa
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
An early warning system can be defined as a chain of information communication systems comprising sensor, detection, decision, and broker subsystems, in the given order, working in conjunction, forecasting and signaling disturbances adversely affecting the stability of the physical world, and giving sufficient time for the response system to prepare resources and response actions to minimize the impact on the stability of the physical world. In this paper, we present a framework for a recommender system for crisis management. This framework uses the actions already implemented to manage former crises to enhance the management of a given crisis. The main idea is to recommend the actions already implemented in those former crises that are similar (the similarity between two crises is based on some indicators such as the gap (hurricane, tsunami) as the actions to be implemented. Finally, this paper proposes to exploit the knowledge gained from past experiences to make the best decision (i.e. the best actions to implement) in order to better manage a crisis ready to occur.
Subjects / Keywords
Recommender system; Crisis management; Decision support; Early warning systems; Experience; Knowledge

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