JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (5): 104-110.doi: 10.6040/j.issn.1671-9352.2.2016.217

Previous Articles    

A method of storing heterogeneous monitoring data for cloud platform

JU Rui1,2, WANG Li-na1,2, YU Rong-wei1*, XU Lai1   

  1. 1. Computer School, Wuhan University, Wuhan 430072, Hubei, China;
    2. Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education, Wuhan University, Wuhan 430072, Hubei, China
  • Received:2016-09-02 Online:2017-05-20 Published:2017-05-15

Abstract: Considering that the monitoring data was heterogeneous, large in amount, time-ordered and of great redundancy; a method to store heterogeneous monitoring data was proposed. This method was based on anti-normalization. It adapted united data organization manner to make source data compatible. At the same time, through a combination of mechanisms like label separation, data compression, caching and index, it can also realized the high-efficiency storage of monitoring data, increased the utilization of storage space, enhanced the scalability of data, simplified the data management and reduced the development and maintenance cost of data. The results of experiment showed that this method could satisfy the actual application requirement of high-efficiency write, real-time update and quick query of monitoring data in cloud platform.

Key words: data storage, anti-normalization, cloud platform monitoring

CLC Number: 

  • TP274
[1] ROCHWERGER B, BEITGAND D, EPSTEIN A, et al. Reservoir-When one cloud is not enough[J]. Computer, 2011, 44(3):44-51.
[2] CHEN Min, JIN Hai, WEN Yonggang, et al. Enabling technologies for future data center networking: A primer[J]. IEEE Network, 2013, 27(4):8-15.
[3] YANG Hui, QIN Yong, FENG Gefei, et al. Online monitoring of geological CO2 storage and leakage based on wireless sensor networks[J]. IEEE Sensors Journal, 2013, 13(2):556-562.
[4] CHANG P H, WANG T P. Supporting personal mobility with integrated RFID in VoIP system[C] // Proceedings of the International Conference on New Trends in Information and Service Science. USA: IEEE, 2009: 1353-1359.
[5] CORNELIA G, ROBERT G, GEORGE P, et al. A comparative study: MongoDB vs MySQL[C] // International Conference on Engineering of Modern Electric Systems Oradea: IEEE, 11-12 June. 2015.
[6] SCHRAM A, ANDERSON K M. MySQL to NoSQL: data modeling challenges in supporting scalability[C] // Proceedings of the 3rd annual conference on Systems, Programming, Languages and Applications: Software for Humanity. New York: ACM, 2012: 191-202.
[7] 丁治明, 高需. 面向物联网海量传感器采样数据管理的数据库集群系统框架[J]. 计算机学报, 2012, 35(6):1175-1191. DING Zhiming, GAO Xu. A database cluster system framework for managing massive sensor sampling data in the internet of things[J]. Chinese Journal of Computers, 2012, 35(6):1175-1191.
[8] CAO Wei, YU Feng, XIE Jiasen. Realization of the low cost and high performance MySQL cloud database[J]. Proceedings of the Vldb Endowment, 2014, 7(13):1742-1747.
[9] Cattell, Rick. Scalable SQL and NoSQL Data Stores[J]. Acm Sigmod Record, 2010, 39(4):12-27.
[10] LI Yishan, MANOHARAN S. A performance comparison of SQL and NoSQL databases[C] //IEEE Pacific Rim Conference on Communications, Computers & Signal Processing. USA: IEEE, 2013: 15-19.
[11] STONEBRAKER M. SQL databases v. NoSQL databases[J]. Communications of the Acm, 2010, 53(4):10-11.
[12] SCHREINER GA, DUARTE D, RONALDO DSM. SQLtoKeyNoSQL: a layer forrelational to key-based NoSQL database mapping[C] // International Conference on Information Integration and Web-Based Applications & Services. New York: ACM, 2015.
[13] ANICETO R, XAVIER R, HOLANDA M, et al. Genomic data persistency on a NoSQL database system[C] // IEEE International Conference on Bioinformatics and Biomedicine. USA: IEEE, 2014: 8-14.
[14] FRANCESCO M D, LI Na, RAJ M, et al. A storage infrastructure for heterogeneous and multimedia data in the internet of things[C] // IEEE International Conference on Green Computing and Communications. USA: IEEE, 2012: 487-487.
[15] JEONG S, BYUN J, KIM D, et al. A data management infrastructure for bridge monitoring[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2015, 9435.
[16] 田野, 袁博, 李廷力. 物联网海量异构数据存储与共享策略研究[J]. 电子学报, 2016, 44(2): 247-257. TIAN Ye, YUAN Bo, LI Tingli. A massive and heterogeneous data storage and sharing strategy for internet of things[J]. Chinese Journal of Electronics, 2016, 44(2):247-257.
[1] ZHAO Guan-bao, LIU Yun. An efficient bittable based frequent itemsets mining algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(05): 23-29.
[2] ZHANG Yue-yun. Random function inverse P-sets and its characteristics depending on attributes [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(10): 90-94.
Full text



No Suggested Reading articles found!