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A Mathematical Analysis of a Disaster Management Data-Grid Push Service

A Mathematical Analysis of a Disaster Management Data-Grid Push Service

Nik Bessis, Antony Brown, Eleana Asimakopoulou
Copyright: © 2010 |Volume: 1 |Issue: 3 |Pages: 15
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781609609634|DOI: 10.4018/jdst.2010070104
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MLA

Bessis, Nik, et al. "A Mathematical Analysis of a Disaster Management Data-Grid Push Service." IJDST vol.1, no.3 2010: pp.56-70. http://doi.org/10.4018/jdst.2010070104

APA

Bessis, N., Brown, A., & Asimakopoulou, E. (2010). A Mathematical Analysis of a Disaster Management Data-Grid Push Service. International Journal of Distributed Systems and Technologies (IJDST), 1(3), 56-70. http://doi.org/10.4018/jdst.2010070104

Chicago

Bessis, Nik, Antony Brown, and Eleana Asimakopoulou. "A Mathematical Analysis of a Disaster Management Data-Grid Push Service," International Journal of Distributed Systems and Technologies (IJDST) 1, no.3: 56-70. http://doi.org/10.4018/jdst.2010070104

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Abstract

Much work is under way within the Grid technology community on issues associated with the development of services fostering the integration and exploitation of multiple autonomous, distributed data sources through a seamless and flexible virtualized interface. These developments involve fluid and dynamic, ad hoc based interactions between dispersed service providers and consumers. However, several obstacles arise in the design and implementation of such services. In this article, the authors examine a notable obstacle, namely how to keep service consumers informed of relevant changes about data committed in multiple and distributed service provider levels, and most importantly, when these changes can affect others’ well-being. To achieve this, the authors use aggregated case scenarios to demonstrate the need for a data-Grid push service in a disaster management situation. In this regard, the article describes in detail the service architecture, as well as its mathematical analysis for keeping interested stakeholders informed automatically about relevant and critical data changes.

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