Abstract
In order to explore the information management, the cloud computing technology is applied to the field of geographic information system, and remote sensing data storage and management system based on Hadoop is studied and realized. The main function of this system includes that the remote sensing data storage module provides the remote sensing data download function for data administrator, supports HTTP protocol and FTP protocol multi-threaded distributed HTTP download. The parallel constructing algorithm of remote sensing image of Pyramid based on Map Reduce is realized by the module, and layered cutting and block storage of massive remote sensing data are carried out. The GDAL open source library suitable for fast read raster data is used and it provides data resource for remote sensing data parallel cutting. In addition, the Geo Web Cache open source tile map service middleware is adopted and HBase is introduced as the storage support of tiles, which can deal with a large number of users’ visit, including loading and drag of maps. The system test is carried out to verify the effectiveness and practicability of the method proposed. The test results can show that the remote sensing data storage and management system based on Hadoop can effectively handle remote sensing data and improve the user’s experience. It is concluded that the information management system has high effectiveness and good practicability.





Similar content being viewed by others
References
Liroz-Gistau, M., Akbarinia, R., Agrawal, D., & Valduriez, P. (2016). Fp-Hadoop: Efficient processing of skewed mapreduce jobs. Information Systems, 60, 69–84.
He, H., Du, Z., Zhang, W., & Chen, A. (2016). Optimization strategy of Hadoop small file storage for big data in healthcare. Journal of Supercomputing, 72(10), 1–12.
Park, D., Wang, J., & Kee, Y. S. (2016). In-storage computing for Hadoop MapReduce framework: Challenges and possibilities. IEEE Transactions on Computers, PP(99), 1.
Magana-Zook, S., Gaylord, J. M., Knapp, D. R., Dodge, D. A., & Ruppert, S. D. (2016). Large-scale seismic waveform quality metric calculation using Hadoop. Computers & Geosciences, 94, 18–30.
Li, Z., & Shen, H. (2017). Measuring scale-up and scale-out Hadoop with remote and local file systems and selecting the best platform. IEEE Transactions on Parallel and Distributed Systems, PP(99), 3201–3214.
Hodor, P., Chawla, A., Clark, A., & Neal, L. (2016). Cl-dash: Rapid configuration and deployment of Hadoop clusters for bioinformatics research in the cloud. Bioinformatics, 32(2), 301–303.
Um, J. H., Lee, S., Kim, T. H., Jeong, C. H., Song, S. K., & Jung, H. (2016). Distributed RDF store for efficient searching billions of triples based on Hadoop. Journal of Supercomputing, 72(5), 1825–1840.
Li, C., Chen, T., He, Q., Zhu, Y., & Li, K. (2016). Mruninovo: An efficient tool for de novo peptide sequencing utilizing the Hadoop distributed computing framework. Bioinformatics, 33(6), 944.
Ferraro, P. U., Roscigno, G., Cattaneo, G., & Giancarlo, R. (2017). Fastdoop: A versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applications. Bioinformatics, 33(10), 1575.
Fu, X., Gao, Y., Luo, B., Du, X., & Guizani, M. (2017). Security threats to Hadoop: Data leakage attacks and investigation. IEEE Network, PP(99), 12–16.
Cai, X., Li, F., Li, P., Ju, L., & Jia, Z. (2017). SLA-aware energy-efficient scheduling scheme for Hadoop YARN. Journal of Supercomputing, 73(8), 3526–3546.
Nguyen, M. C., Won, H., Son, S., Gil, M. S., & Moon, Y. S. (2017). Prefetching-based metadata management in advanced multitenant Hadoop. Journal of Supercomputing, 73(2), 1–21.
Acknowledgements
The study was supported by “Science and Technology Plan of Ministry of Housing and Urban–Rural Development of China (Grant No. 2016-R4-014)”.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, Z., Huo, Z. Information Intelligent Management System Based on Hadoop. Wireless Pers Commun 102, 3803–3812 (2018). https://doi.org/10.1007/s11277-018-5411-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-018-5411-4