Knowledge and Data Management in Grids: Empowering Collaborative Research and Innovation
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Grid computing is a distributed computing paradigm that enables the sharing of computing resources across multiple administrative domains. Grids are used to support a wide range of applications, including scientific research, data analysis, and business intelligence. One of the key challenges in grid computing is the management of knowledge and data.
4.5 out of 5
Language | : | English |
File size | : | 4236 KB |
Text-to-Speech | : | Enabled |
Print length | : | 272 pages |
Knowledge management is the process of creating, storing, and sharing knowledge within an organization. Data management is the process of collecting, storing, and processing data. Both knowledge management and data management are essential for effective grid computing.
This book provides a comprehensive overview of the state-of-the-art in knowledge and data management in grid environments. The book covers a wide range of topics, including:
- Data models
- Metadata
- Data quality
- Data security
- Data mining
The book also provides case studies of successful knowledge and data management implementations in grid environments.
Benefits of Knowledge and Data Management in Grids
There are many benefits to using knowledge and data management in grid environments. These benefits include:
- Improved collaboration: Knowledge and data management can help to improve collaboration between researchers and scientists. By sharing knowledge and data, researchers can avoid duplication of effort and can build upon the work of others.
- Increased efficiency: Knowledge and data management can help to increase the efficiency of grid computing. By automating the management of knowledge and data, researchers can free up time to focus on their research.
- Reduced costs: Knowledge and data management can help to reduce the costs of grid computing. By sharing knowledge and data, researchers can avoid the need to Free Download or develop their own resources.
- Improved decision-making: Knowledge and data management can help to improve decision-making by providing researchers with access to the information they need.
Challenges of Knowledge and Data Management in Grids
There are also some challenges to using knowledge and data management in grid environments. These challenges include:
- Data heterogeneity: Grid environments often contain data from a variety of sources. This data can be in different formats and may have different levels of quality.
- Data security: Grid environments are often shared by multiple users. This can pose a security risk to sensitive data.
- Data privacy: Grid environments can contain data that is subject to privacy regulations. This can make it difficult to share data without violating privacy laws.
Knowledge and data management are essential for effective grid computing. By addressing the challenges of knowledge and data management, we can unlock the full potential of grids to support collaborative research and innovation.
References
[1] Foster, I., Kesselman, C., Tuecke, S., Foster, I., Kesselman, C., Tuecke, S., & Limehouse, J. (2004). The Grid: Blueprint for a New Computing Infrastructure. Elsevier. [2] Hey, T., & Trefethen, A. (eds.). (2003). The Grid: Computer Frontiers and Their Applications. Cambridge University Press. [3] Berman, F., Fox, G., & Hey, T. (eds.). (2003). Grid Computing: Making the Global Infrastructure a Reality. John Wiley & Sons. [4] Foster, I., Voeckler, J., Wilde, M., & Zhao, Y. (2005). Chimera: A virtual data system for representing, querying, and managing data integration metadata. In Proceedings of the 14th International Conference on Scientific and Statistical Database Management (SSDBM 2005). [5] Moreau, L., Foster, I., Voeckler, J., Wilde, M., & Zhao, Y. (2005). Semantic web technologies for representing and managing scientific metadata. In Proceedings of the 1st International Workshop on Semantic Web Technologies for E-Science (SWES 2005).
4.5 out of 5
Language | : | English |
File size | : | 4236 KB |
Text-to-Speech | : | Enabled |
Print length | : | 272 pages |
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4.5 out of 5
Language | : | English |
File size | : | 4236 KB |
Text-to-Speech | : | Enabled |
Print length | : | 272 pages |