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Arif Cagdas Aydinoglu, Elif Demir, & Serpil Ates. (2011). Designing a harmonized geo-data model for Disaster Management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: There are problems for managing and sharing geo-data effectively in Turkey. The key to resolving these problems is to develop a harmonized geo-data model. General features of this model are based on ISO/TC211 standards, INSPIRE Data Specifications, and expectations of Turkey National GIS actions. The generic conceptual model components were defined to harmonize geo-data and to produce data specifications. In order to enable semantic interoperability, application schemas were designed for data themes such as administrative unit, address, cadastre/building, hydrographic, topography, geodesy, transportation, and land cover/use. The model, as base and the domain geo-data model, is a starting point to create sector models in different thematic areas. Disaster Management Geo-data Model model was developed as an extension of base geo-data model to manage geo-data collaborate on disaster management activities. This model includes existing geo-data special for disaster management activities and dynamic data collecting during disaster.
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Fiona McNeill, Andriana Gkaniatsou, & Alan Bundy. (2014). Dynamic data sharing for facilitating communication during emergency responses. 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. 369–373). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the CHAIn system, which is designed to facilitate data sharing between disparate organisations during emergency response situations by resolving mismatches in their data. It uses structured data matching to reformulate failed queries in cases where these failed because of incompatibilities between the query (derived from the source schema) and the schema of the queried datasource (the target schema). This reformulation is done by developing matches between the source schema and the target schema. These matches are then used to reformulate the query and retrieve responses relevant to those expected by the original query. Despite the growing interest in intelligent query answering, integration of data matching into query answering is novel, and allows users to successfully query datasources even if they do not know how the data in that source is organized, which is often the case during emergency responses. We describe the proof-of-concept system we have developed and an encouraging initial evaluation.
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