基于三层存储模型的RFID数据压缩存储方法

doi:10.3724/SP.J.1087.2012.00625

计算机应用 ›› 2012, Vol. 32 ›› Issue (03): 625-628.DOI: 10.3724/SP.J.1087.2012.00625

基于三层存储模型的RFID数据压缩存储方法

夏秀峰,赵龙

沈阳航空航天大学计算机学院,沈阳 110136

收稿日期:2011-09-14 修回日期:2011-11-10 发布日期:2012-03-01 出版日期:2012-03-01
通讯作者: 赵龙
作者简介:夏秀峰(1964-),男,山东胶南人,教授,博士,CCF高级会员,主要研究方向:数据库、RFID数据管理;赵龙(1986-),男,辽宁盘锦人,硕士研究生,主要研究方向:管理信息系统、数据库、RFID数据管理。

RFID data compression storage method based on three-level storage model

^{XIA Xiu-feng, ZHAO Long}

School of Computer, Shenyang Aerospace University, Shenyang Liaoning 110136, China

Received:2011-09-14 Revised:2011-11-10 Online:2012-03-01 Published:2012-03-01

摘要/Abstract

摘要： 针对物联网技术中亟待解决的海量数据存储问题,提出了一种基于射频识别(RFID)的三层数据存储压缩模型。该模型将数据分为当前数据层、临时数据层和历史数据层,利用每一层中数据的特点分别设计了相应的数据汇总算法,最终实现RFID数据的压缩存储。在该模型的基础之上,提出了针对路径的编码算法,用于对路径进行压缩存储。实验结果表明,该三层存储模型可以有效地压缩存储RFID数据,同时数据汇总算法具有较低的时间复杂度与较高的数据压缩比。

关键词: 射频识别, 海量数据, 路径编码, 数据压缩, 三层存储模型

Abstract: Concerning the problem of massive data storage in the technology of the Internet of things, this paper proposed a three-level Radio Frequency IDentification (RFID) data compression storage model. This model divided the data into the current level, the temporary level and the historical level. Corresponding data collection algorithm was designed for every level according to the features of data in each level. And a coding algorithm for paths based on the model was proposed to store the compressed paths. The experimental results show the three-level storage model can effectively store compression data, and demonstrate the algorithm of data gathering has higher data compression rate as well as lower time complexity.

Key words: Radio Frequency IDentification (RFID), massive data, path coding, data compression, three-level storage model

中图分类号:

夏秀峰赵龙. 基于三层存储模型的RFID数据压缩存储方法[J]. 计算机应用, 2012, 32(03): 625-628.

XIA Xiu-feng ZHAO Long. RFID data compression storage method based on three-level storage model[J]. Journal of Computer Applications, 2012, 32(03): 625-628.

参考文献

[1]GUSTAVO R, MARIO M, CARLOS D. Early infrastructure of an Internet of things in spaces for learning [C]// Proceedings of the 8th IEEE International Conference on Advanced Learning Technologies. Piscataway, NJ: IEEE Press,2008:381-383.

[2]HU YING, SUNDARA S, CHORMA T, et al. Supporting RFID-based item tracking applications in Oracle DBMS using a bitmap datatype [C]// Proceedings of the 31st International Conference on Very Large Data Bases. New York: ACM Press,2005:1140-1151.

[3]COCCI R, TRAN T, DIAO Y, et al. Efficient data interpretation and compression over RFID streams [C]// Proceedings of the 24th International Conference on Data Engineering. Piscataway, NJ: IEEE Press, 2008:1445-1447.

[4]DERAKHSHAN R, ORLOWSKA M, LI X. RFID data management: challenges and opportunities [C]// IEEE International Conference on RFID. Piscataway, NJ: IEEE Press,2007:175-182.

[5]FAZZINGA B, FLESCA S, MASCIARI E, et al. Efficient and effective RFID data warehousing [C]// IDEAS'09: Proceedings of the 2009 International Database Engineering & Applications Symposium. New York: ACM Press,2009:251-258.

[6]GONZALEZ H, HAN J W, LI X L. Mining compressed commodity workflows from massive RFID data sets [C]// Proceedings of the 15th ACM International Conference on Information and Knowledge Management. New York: ACM Press,2006:162-171.

[7]BAI Y, WANG F S, LIU P Y, et al. RFID data processing with a data stream query language [C]// ICDE 2007: IEEE the 23rd International Conference on Data Engineering. Piscataway, NJ: IEEE Press,2007:1184-1193.

[8]GONZALEZ H, HAN J W, LI X L, et al. Warehousing and analyzing massive RFID data sets [C]// ICDE'06: Proceedings of the 22nd International Conference on Data Engineering. Piscataway, NJ: IEEE Press, 2006:83-92.

[9]王霞.RFID数据存储和管理技术综述[J].计算机应用与软件,2008,24(12):175-176.

[10]陈竹西,孙艳,胡孔法,等. 基于路径编码的RFID数据压缩技术研究[J].扬州大学学报:自然科学版,2008,11(2):53-56.

[11]CHAWATHE S, KRISHNAMURTHY V, RAMACHANDRAN S, et al. Managing RFID data [C]// Proceedings of the 30th Very Large Data Bases Conference. Piscataway, NJ: IEEE Press,2004:1189-1195.

[12]WANG F S, LIU P Y. Temporal management of RFID data [C]// Proceedings of the 31st International Conference on Very Large Data Bases. New York: ACM Press,2005:1128-1139.

[13]GONZALEZ H, HAN J W, LI X L. FlowCube: Constructing RFID FlowCubes for multi-dimensional analysis of commodity flows [C]// VLDB'06: Proceedings of the 32nd International Conference on Very Large Data Bases. New York: ACM Press,2006:834-845.

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基于三层存储模型的RFID数据压缩存储方法

RFID data compression storage method based on three-level storage model

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