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Trends in life science grid: from computing grid to knowledge grid

BACKGROUND: Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. RESULTS: This survey reviews the latest grid technologies fro...

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Detalles Bibliográficos
Autor principal: Konagaya, Akihiko
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1764466/
https://www.ncbi.nlm.nih.gov/pubmed/17254294
http://dx.doi.org/10.1186/1471-2105-7-S5-S10
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author Konagaya, Akihiko
author_facet Konagaya, Akihiko
author_sort Konagaya, Akihiko
collection PubMed
description BACKGROUND: Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. RESULTS: This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. CONCLUSION: Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.
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spelling pubmed-17644662007-01-09 Trends in life science grid: from computing grid to knowledge grid Konagaya, Akihiko BMC Bioinformatics Review BACKGROUND: Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. RESULTS: This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. CONCLUSION: Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community. BioMed Central 2006-12-18 /pmc/articles/PMC1764466/ /pubmed/17254294 http://dx.doi.org/10.1186/1471-2105-7-S5-S10 Text en Copyright © 2006 Konagaya; licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Konagaya, Akihiko
Trends in life science grid: from computing grid to knowledge grid
title Trends in life science grid: from computing grid to knowledge grid
title_full Trends in life science grid: from computing grid to knowledge grid
title_fullStr Trends in life science grid: from computing grid to knowledge grid
title_full_unstemmed Trends in life science grid: from computing grid to knowledge grid
title_short Trends in life science grid: from computing grid to knowledge grid
title_sort trends in life science grid: from computing grid to knowledge grid
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1764466/
https://www.ncbi.nlm.nih.gov/pubmed/17254294
http://dx.doi.org/10.1186/1471-2105-7-S5-S10
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