基于改进遗传算法的C-TRDM压缩存储

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

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。

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.