Research on the Key Technology of Agricultural Production and Market Information Matching System under the Cloud Computing Background

Article Preview

Abstract:

The thesis firstly takes advantage of open source search engine (Nutch) to build the market space data warehouse and data sources, collecting agricultural products market information from the whole internet, on the basis of analyzing the existing agricultural production and market information matching system, then it puts forward the interest-model and combination matching algorithm more suitable for users in agricultural fields. At the same time, this article improves the existing matching system in the cloud computing background under the support of Hadoop distributed platform to solve the bottleneck problems such as the expanding ability of storage space and the efficiency of analysis and calculation confronted by the agricultural products market information matching system. Finally, the system makes a comparative analysis of recall ratio and precision ratio to prove its effectiveness and practical range. The aim of this paper is to make the agricultural production and market information matching system more suitable for Chinese agricultural production and marketing information system which supports the matching management decision of agricultural production circulation consumption and provides beneficial reference for the application of cloud computing technology.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2141-2147

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Zhuang, J., Wang, M.: Leafy Content-based Filtering Agricultural Information Recommendation System. J. Comput. Eng. 11, 38 -41 (2012).

Google Scholar

[2] Huang, L., Dai, L., Wei, Y.: A Recommendation System Based on Multi-agent. J. Expert Systems with Applications. In: Second International Conference on Genetic and Evolutionary Computing, 2008. WGEC '08, pp.223-226. IEEE Press, Hubei (2008).

DOI: 10.1109/wgec.2008.45

Google Scholar

[3] Li, X., Zhao, L., Wu, L.: A Feature Extraction Method Using Base Phrase and Keyword in Chinese Text. In: 3rd International Conference on Intelligent System and Knowledge Engineering, 2008. ISKE 2008, vol. 1, pp.680-684. IEEE Press, Xiamen (2008).

DOI: 10.1109/iske.2008.4731016

Google Scholar

[4] Foster, I., Kesselman, C., Nick, J., Tuecke, S.: A SOM combined with KNN for classification task. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp.2368-2373. IEEE Press, San Jose, CA (2011).

Google Scholar