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Disaster planning using automated composition of semantic OGC web services: A case study in sheltering

Farnaghi, Mahdi LU and Mansourian, Ali LU (2013) In Computers, Environment and Urban Systems 41. p.204-218
Abstract
Spatial data are crucial in disaster planning. However, because of the dynamic, urgent and uncertain nature of disasters, certain data and functionalities may be inaccessible to decision makers when they are required. Web service composition offers a possible solution whereby disaster planners can integrate spatial web services to generate new spatial data and functionalities, quickly, from existing ones. This paper proposes an automatic solution for composing OWSs (Open Geospatial Consortium Web Services) for disaster planning. A semantic annotation approach based on the Resource Description Framework (RDF) and SPARQL languages is used to describe OWSs semantically. A conceptual model for AI (Artificial Intelligence) planning is also... (More)
Spatial data are crucial in disaster planning. However, because of the dynamic, urgent and uncertain nature of disasters, certain data and functionalities may be inaccessible to decision makers when they are required. Web service composition offers a possible solution whereby disaster planners can integrate spatial web services to generate new spatial data and functionalities, quickly, from existing ones. This paper proposes an automatic solution for composing OWSs (Open Geospatial Consortium Web Services) for disaster planning. A semantic annotation approach based on the Resource Description Framework (RDF) and SPARQL languages is used to describe OWSs semantically. A conceptual model for AI (Artificial Intelligence) planning is also proposed that works based on RDF and SPARQL. An AI planning algorithm was implemented based on the proposed conceptual model to compose semantic OWSs. The applicability of the proposed solution is investigated through a case study in evacuation sheltering. The case study demonstrates that the proposed automatic composition approach can enhance the efficiency of OWS integration and thereby improve the disaster management process. (c) 2013 Elsevier Ltd. All rights reserved. (Less)
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publication status
published
subject
keywords
Disaster planning, OGC web service, Semantic annotation, Automatic web, service composition, AI planning, Artificial Intelligence (AI)
in
Computers, Environment and Urban Systems
volume
41
pages
204 - 218
publisher
Elsevier
external identifiers
  • wos:000325122300017
  • scopus:84880695762
ISSN
0198-9715
DOI
10.1016/j.compenvurbsys.2013.06.003
project
Automatic Composition of Geospatial Web Services using Intelligent Agents
language
English
LU publication?
yes
id
660ecb6e-fd42-4af9-a02b-8e4a5587dfb4 (old id 4172227)
date added to LUP
2016-04-01 10:29:29
date last changed
2023-09-05 13:33:42
@article{660ecb6e-fd42-4af9-a02b-8e4a5587dfb4,
  abstract     = {{Spatial data are crucial in disaster planning. However, because of the dynamic, urgent and uncertain nature of disasters, certain data and functionalities may be inaccessible to decision makers when they are required. Web service composition offers a possible solution whereby disaster planners can integrate spatial web services to generate new spatial data and functionalities, quickly, from existing ones. This paper proposes an automatic solution for composing OWSs (Open Geospatial Consortium Web Services) for disaster planning. A semantic annotation approach based on the Resource Description Framework (RDF) and SPARQL languages is used to describe OWSs semantically. A conceptual model for AI (Artificial Intelligence) planning is also proposed that works based on RDF and SPARQL. An AI planning algorithm was implemented based on the proposed conceptual model to compose semantic OWSs. The applicability of the proposed solution is investigated through a case study in evacuation sheltering. The case study demonstrates that the proposed automatic composition approach can enhance the efficiency of OWS integration and thereby improve the disaster management process. (c) 2013 Elsevier Ltd. All rights reserved.}},
  author       = {{Farnaghi, Mahdi and Mansourian, Ali}},
  issn         = {{0198-9715}},
  keywords     = {{Disaster planning; OGC web service; Semantic annotation; Automatic web; service composition; AI planning; Artificial Intelligence (AI)}},
  language     = {{eng}},
  pages        = {{204--218}},
  publisher    = {{Elsevier}},
  series       = {{Computers, Environment and Urban Systems}},
  title        = {{Disaster planning using automated composition of semantic OGC web services: A case study in sheltering}},
  url          = {{http://dx.doi.org/10.1016/j.compenvurbsys.2013.06.003}},
  doi          = {{10.1016/j.compenvurbsys.2013.06.003}},
  volume       = {{41}},
  year         = {{2013}},
}