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Computer Engineering ›› 2009, Vol. 35 ›› Issue (21): 178-180. doi: 10.3969/j.issn.1000-3428.2009.21.059

• Artificial Intelligence and Recognition Technology • Previous Articles     Next Articles

Compressed Storage for C-Temporal Relation Data Model Based on Improved Genetic Algorithm

WANG Zhi-wen1,2, LIU Mei-zhen3, CAI Qi-xian1, XIE Guo-qing4   

  1. (1. Dept. of Computer & Engineering, Guangxi University of Technology, Liuzhou 545006; 2. Cognitive Science Department, Xiamen University, Xiamen 361005; 3. Library of Guangxi University of Technology, Liuzhou 545006; 4. Faculty of Software, Fujian Normal University, Fuzhou 350007)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-11-05 Published:2009-11-05

基于改进遗传算法的C-TRDM压缩存储

王智文1,2,刘美珍3,蔡启先1,谢国庆4   

  1. (1. 广西工学院计算机工程系,柳州 545006;2. 厦门大学智能科学与技术系,厦门 361005; 3. 广西工学院图书馆,柳州 545006;4. 福建师范大学软件学院,福州 350007)

Abstract: There is data redundancy temporal database and the quantities of temporal database are increasing fleetly, aiming at these problems, this paper puts forward compressed storage tactics based on improved genetic algorithm for temporal data which combine compress technology in existence in order to settle data redundancy in the course of temporal data storage. Temporal relation data at any moment is decomposed into least granularity data and be coded meanwhile. Optimized storage data are figured out by using improved genetic algorithm, and the ratio of compression is enhanced. Celerity astringency of the algorithm can heighten speed of removing data redundancy largely.

Key words: Temporal Relation Data Model(TRDM), improved genetic algorithm, compressed storage

摘要: 针对时态数据库中存在数据冗余、数据量快速增长等问题,结合现有压缩技术,提出基于改进遗传算法的C-TRDM压缩存储技术。将各个时刻的时态关系数据分解为最小粒度的数据并进行编码,采用改进的遗传算法来计算待压缩数据中的最优存储数据以提高压缩比。算法的快速收敛性使去除数据冗余的速度得到提高。

关键词: 时态关系数据模型, 改进遗传算法, 压缩存储

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