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Adopting the Grid Computing & Semantic Web Hybrid for Global Knowledge Sharing

Mirghani Mohamed, Michael Stankosky, Vincent Ribière

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Abstract

The purpose of this chapter is to examine the requirements of Knowledge Management (KM) services deployment in a Semantic Grid environment. A wide range of literature on Grid Computing, Semantic Web, and KM have been reviewed, related, and interpreted. The benefits of the Semantic Web and the Grid Computing convergence have been investigated, enumerated and related to KM principles in a complete service model. Although the Grid Computing model significantly contributed to the shared resources, most of KM tools obstacles within the grid are to be resolved at the semantic and cultural levels more than at the physical or logical grid levels. The early results from academia, where grid computing still in testing phase, show a synergy and the potentiality of leveraging knowledge, especially from voluminous data, at a wider scale. However, the plethora of information produced in this environment will result in a serious information overload, unless proper standardization, automated relations, syndication, and validation techniques are developed.

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What this paper is about

The purpose of this chapter is to examine the requirements of Knowledge Management (KM) services deployment in a Semantic Grid environment. A wide range of literature on Grid Computing, Semantic Web, and KM have been reviewed, related, and interpreted. The benefits of the Semantic Web and the Grid Computing convergence have been investigated, enumerated and related to KM principles in a complete service model. Although the Grid Computing model significantly contributed to the shared resources, most of KM tools obstacles within the grid are to be resolved at the semantic and cultural levels more than at the physical or logical grid levels. The early results from academia, where grid computing still in testing phase, show a synergy and the potentiality of leveraging knowledge, especially from voluminous data, at a wider scale. However, the plethora of information produced in this environment will result in a serious information overload, unless proper standardization, automated relations, syndication, and validation techniques are developed.

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Available abstract

The purpose of this chapter is to examine the requirements of Knowledge Management (KM) services deployment in a Semantic Grid environment. A wide range of literature on Grid Computing, Semantic Web, and KM have been reviewed, related, and interpreted. The benefits of the Semantic Web and the Grid Computing convergence have been investigated, enumerated and related to KM principles in a complete service model. Although the Grid Computing model significantly contributed to the shared resources, most of KM tools obstacles within the grid are to be resolved at the semantic and cultural levels more than at the physical or logical grid levels. The early results from academia, where grid computing still in testing phase, show a synergy and the potentiality of leveraging knowledge, especially from voluminous data, at a wider scale. However, the plethora of information produced in this environment will result in a serious information overload, unless proper standardization, automated relations, syndication, and validation techniques are developed.

Key concepts: Semantic grid, Computer science, Grid computing, Grid, DRMAA, Semantic Web, Semantic Web Stack, Social Semantic Web

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