The Key Requirements for Deploying Knowledge Management Services in a Semantic Grid Environment

2007 ◽  
Vol 3 (3) ◽  
pp. 104-118 ◽  
Author(s):  
Mirghani Mohamed ◽  
Michael Stankosky ◽  
Vincent Ribière
Keyword(s):  
Knowledge Management ◽  
Grid Environment ◽  
Semantic Grid ◽  
Management Services

The Key Requirements for deploying Knowledge Management Services in a Semantic Grid Environment

2011 ◽  
pp. 363-379
Author(s):  
Mirghani Mohamed ◽  
Michael Stankosky ◽  
Vincent Ribière
Keyword(s):  
Knowledge Management ◽  
Semantic Web ◽  
Grid Computing ◽  
Information Overload ◽  
Service Model ◽  
Shared Resources ◽  
Grid Environment ◽  
Semantic Grid ◽  
Early Results ◽  
Wide Range

The purpose of this article is to investigate 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 enumerated and related to KM principles in a complete service model. Although the Grid Computing contributed the shared resources, most of the KM tool 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 show a synergy and the potentiality of leveraging knowledge 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.

Understanding knowledge exchange processes among diverse users of coworking-spaces

2019 ◽  
Vol 23 (10) ◽  
pp. 2067-2085 ◽  
Author(s):  
Ricarda Bouncken ◽  
Muhammad Mahmood Aslam
Keyword(s):  
Knowledge Management ◽  
Knowledge Sharing ◽  
Knowledge Exchange ◽  
Practical Importance ◽  
Positive Outcomes ◽  
Content Type ◽  
Shared Spaces ◽  
Inductive Research ◽  
Coworking Spaces ◽  
Management Services

Purpose Coworking spaces use the idea of spatial co-location that improves communication and knowledge sharing among independent knowledge professionals. Fluid work structures and a sense of community can facilitate work satisfaction, creativity and entrepreneurship. Fundamentals to those positive outcomes are the knowledge sharing processes between users of coworking spaces. The purpose of this study is to explore the knowledge sharing processes in this setting where researchers still have very little understanding. Design/methodology/approach Based on an inductive research methodology, qualitative data were collected through observations and interviews with a variety of users (including freelancers, entrepreneurs and firms) incumbent in various coworking spaces in Germany. Findings Co-location of individuals in coworking spaces is first about physical proximity and second about socialization and collaboration opportunities, which then advance cognitive proximity. Thus, co-location can facilitate tacit knowledge exchange, ignite the social disembodiment of ideas, synthesize domain-related knowledge sharing and promote inter-domain learning. The institutionalization of knowledge management services will allow coworking spaces to increase these positive outcomes. Practical implications Findings of this study are interesting for managers of shared spaces and traditional firms that use spatial co-location. The authors propose institutionalized knowledge management services to enable multifaceted and multidisciplinary knowledge creation in organizations. Originality/value This paper sheds light on the role of spatial co-location in knowledge sharing processes among independent knowledge professionals in shared office spaces. Thereby, this study provides valuable insights into a phenomenon that has received little attention even though its practical importance is high.

A methodology for ontology update in the semantic grid environment

2012 ◽  
Vol 27 (4) ◽  
pp. 782-808 ◽  
Author(s):  
Andrew Flahive ◽  
David Taniar ◽  
Wenny Rahayu ◽  
Bernady O. Apduhan
Keyword(s):  
Grid Environment ◽  
Semantic Grid

scholarly journals Towards Context-Aware Knowledge Management in e-Enterprises

2005 ◽  
Vol 9 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Weihong Huang ◽  
Keyword(s):  
Knowledge Management ◽  
Intelligent Systems ◽  
Reference Model ◽  
Negative Impact ◽  
Global Network ◽  
Context Aware ◽  
Contextual Knowledge ◽  
Semantic Grid ◽  
Knowledge Grid ◽  
Context Knowledge

To reduce the negative impact of knowledge loss and to improve knowledge reuse effectiveness in knowledge management in e-Enterprises, this paper presents a context-aware approach to facilitate managing various types of static enterprise information and dynamic process information. Proposed approach features representing and integrating information at different conceptual levels to present contextual knowledge in an open environment. In this paper, we redefine the concept of context in intelligent systems and propose a set of meta-information elements for context description in business environments. In realising the context-awareness in knowledge management, we present a context knowledge structure model and look into the corresponding context knowledge storage and reuse solutions. To enhance context-aware knowledge management for e-Businesses over the global network, we introduce a new concept of Context Knowledge Grid with a layered knowledge interoperation reference model, which are supposed to leverage the contextual knowledge in e-Enterprises and enable interoperation with other knowledge frameworks such as the Semantic Web and the Semantic Grid.

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

2011 ◽  
pp. 365-381
Author(s):  
Mirghani Mohamed ◽  
Michael Stankosky ◽  
Vincent Ribière
Keyword(s):  
Knowledge Management ◽  
Semantic Web ◽  
Grid Computing ◽  
Knowledge Sharing ◽  
Information Overload ◽  
Global Knowledge ◽  
Shared Resources ◽  
Semantic Grid ◽  
Early Results ◽  
Wide Range

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.

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

2012 ◽  
pp. 1721-1736
Author(s):  
Mirghani Mohamed ◽  
Michael Stankosky ◽  
Vincent Ribière
Keyword(s):  
Knowledge Management ◽  
Semantic Web ◽  
Grid Computing ◽  
Knowledge Sharing ◽  
Information Overload ◽  
Global Knowledge ◽  
Shared Resources ◽  
Semantic Grid ◽  
Early Results ◽  
Wide Range

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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