Big Data Storage Method in Wireless Communication Environment

Abstract:

Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive data sets. Big data service is very attractive in the field of wireless communication environment, especially when we face the spatial applications, which are typical applications of big data. Because of the complexity to ingest, store and analyze geographical information data, this paper reflects on a few of the technical problems presented by the exploration of big data, and puts forward an effective storage method in wireless communication environment, which is based on the measurement of moving regularity through proposing three key techniques: partition technique, index technique and prefetch technique. Experimental results show that the performance of big data storage method using these new techniques is better than the other storage methods on managing a great capacity of big data in wireless communication environment.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 899-904
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.899
• Online since: September 2013
• Authors: Ye Liang
• Keywords: Big Data, Cache Prefetch, Indexing, Partition, Storage Service, Wireless Communication Environment

References

[1] G. Jung, N. Gnanasambandam, and T. Mukherjee, Synchronous Parallel Processing of Big-Data Analytics Services to Optimize Performance in Federated Clouds, 2012 IEEE 5th International Conference on Cloud Computing (CLOUD 2012), 2012, pp.811-818.

DOI: 10.1109/cloud.2012.108

[2] Edmon Begoli, and James Horey, Design Principles for Effective Knowledge Discovery from Big Data, 2012 Joint Working IEEE/IFIP Conference on Software Architecture (WICSA) and European Conference on Software Architecture (ECSA 2012), 2012, pp.215-218.

DOI: 10.1109/wicsa-ecsa.212.32

[3] M. Diaz, G. Juan, O. Lucas, and A. Ryuga, Big Data on the Internet of Things: An Example for the E-health, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2012), 2012, pp.898-900.

DOI: 10.1109/imis.2012.198

[4] E. Kohlwey, A. Sussman, J. Trost, and A. Maurer, Leveraging the Cloud for Big Data Biometrics: Meeting the Performance Requirements of the Next Generation Biometric Systems, 2011 IEEE World Congress on Services (SERVICES 2011), 2011, pp.597-601.

DOI: 10.1109/services.2011.95

[5] Shao Xiao-Dong, and Li Qiang, A strategy for continuously big capacity data transmit/receive process handling in real-time, 2010 2nd International Conference on Advanced Computer Control (ICACC 2010), 2010, pp.37-40.

DOI: 10.1109/icacc.2010.5487001

[6] Rukui Tao, Baochen Jiang, and Chengyou Wang, Sampling rate conversion and data synchronization in big merging unit, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2011), 2011, pp.531-534.

DOI: 10.1109/drpt.2011.5993949

[7] Yunqin Zhong, Jizhong Han, Tieying Zhang, Zhenhua Li, Jinyun Fang, and Guihai Chen , Towards Parallel Spatial Query Processing for Big Spatial Data, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW 2012), 2012, pp.2085-2094.

DOI: 10.1109/ipdpsw.2012.245

[8] B. Chandramouli, J. Goldstein, and S. Duan, Temporal Analytics on Big Data for Web Advertising, 2012 IEEE 28th International Conference on Data Engineering (ICDE 2012), 2012, pp.90-101, doi: 10. 1109/ICDE. 2012. 55.

DOI: 10.1109/icde.2012.55