Research on Compression Storage of Massive Agricultural Data Based on Cloud Environment

With the development of information technology, agriculture data show large amount of data, distributed, heterogeneous characteristics. It is difficult to access and management with the massive data are continuously increasing which affect the large-scale use of agricultural information data. In thi...

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Published inApplied Mechanics and Materials Vol. 441; pp. 1025 - 1029
Main Authors Wu, Hua Rui, Gao, Rong Hua
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 01.12.2013
Subjects
Cloud Storage
Compression Storage
Huffman Tree
Massive Data
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Abstract With the development of information technology, agriculture data show large amount of data, distributed, heterogeneous characteristics. It is difficult to access and management with the massive data are continuously increasing which affect the large-scale use of agricultural information data. In this paper, the method of compression algorithm is proposed which based on real-time and time space correlation characteristics. All data is divided into several categories by Huffman compression algorithm combines parallel processing cloud platform. Then, the massive agricultural data is compressed and reducing the data storage. Exprienment result show that cloud storage platform with dynamic scalability. Under the same experimental data, the method of this paper has higher compress ratio, and compression consuming less when a larger amount data, compared with the Huffman compress and dictionary-based data compression algorithm.
AbstractList With the development of information technology, agriculture data show large amount of data, distributed, heterogeneous characteristics. It is difficult to access and management with the massive data are continuously increasing which affect the large-scale use of agricultural information data. In this paper, the method of compression algorithm is proposed which based on real-time and time space correlation characteristics. All data is divided into several categories by Huffman compression algorithm combines parallel processing cloud platform. Then, the massive agricultural data is compressed and reducing the data storage. Exprienment result show that cloud storage platform with dynamic scalability. Under the same experimental data, the method of this paper has higher compress ratio, and compression consuming less when a larger amount data, compared with the Huffman compress and dictionary-based data compression algorithm.
Author Wu, Hua Rui
Gao, Rong Hua
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Cites_doi 10.1109/tit.1977.1055714
10.1109/tmm.2012.2191270
10.1109/tifs.2011.2119314
10.1109/cloudcom.2010.20
10.1109/tmm.2013.2239629
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