The Construction of the Knowledge Management System in the Cloud Computing Environment

Article Preview

Abstract:

In order to make the implementation of the KMS more easily and handle the knowledge more efficiently, we propose the KMS in the cloud computing environment. Firstly, we proposed the document, where the explicate knowledge exists in, processing method based on the Map reduce. Then the explicate knowledge can be processed parallel and faster. Afterwards we give the architecture of the KBS in the environments. The organization can rent the different kinds of services according to their requirements. Since the infrastructure is provided by the cloud computing providers, the organization can focus more on the functions of the KMS. It makes the implementation of the KMS more easily with lower costs.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2366-2369

Citation:

Online since:

January 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Li, M., Jin, L., & Wang, J. (2014). A new MCDM method combining QFD with TOPSIS for knowledge management system selection from the user's perspective in intuitionistic fuzzy environment. Applied Soft Computing, 21, 28-37.

DOI: 10.1016/j.asoc.2014.03.008

Google Scholar

[2] Bontis, N. (2001). Assessing knowledge assets: a review of the models used to measure intellectual capital. International journal of management reviews, 3(1), 41-60.

DOI: 10.1111/1468-2370.00053

Google Scholar

[3] Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS quarterly, 107-136.

DOI: 10.2307/3250961

Google Scholar

[4] Xiaoqiang, Y., & Yuejin, D. (2010, June). Exploration of cloud computing technologies for geographic information services. In Geoinformatics, 2010 18th International Conference on (pp.1-5). IEEE.

DOI: 10.1109/geoinformatics.2010.5567515

Google Scholar

[5] Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., . & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.

DOI: 10.1145/1721654.1721672

Google Scholar

[6] Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.

DOI: 10.1145/1327452.1327492

Google Scholar

[7] M.F. Porter. An algorithm for suffix stripping. Program, 14(3): 130-137, July (1980).

Google Scholar

[8] Zewen, C., & Yao, Z. (2012, November). Parallel text clustering based on mapreduce. In Cloud and Green Computing (CGC), 2012 Second International Conference on (pp.226-229). IEEE.

DOI: 10.1109/cgc.2012.128

Google Scholar

[9] Soucy, P., & Mineau, G. W. (2001). A simple KNN algorithm for text categorization. In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on (pp.647-648). IEEE.

DOI: 10.1109/icdm.2001.989592

Google Scholar