基于小波和偶合特征的多数据流压缩算法

A Compression Algorithm for Multi-Streams Based on Wavelets and Coincidence

提出了基于Haar小波技术和偶合特征的多数据流压缩方法．主要研究成果包括：（1）证明了Haar小波变换服从能量守恒规律，并用于压缩数据流；（2）揭示了数据流的偶合度与变化趋势的相关性、偶合度的平移不变性及等价规律，采用特征流序列的小波系数和流能量近似表示流的趋势，达到压缩的目的；（3）提出了多尺度能量分解模型，提高了表示精度；（4）设计了多尺度能量分解压缩算法以及多尺度重构算法：（5）在真实数据集上的实验表明，新方法的压缩比是传统小波方法的2-4倍．

Methods based on Haar wavelets and coincidence characteristics are proposed to compress multi-streams. The main contributions include： （1） Energy conservation law of Haar wavelets transform is proved to compress data streams. （2） The relation between the coincidence measure and trend of streams is revealed as along with the invariability under parallel shift and the equivalence law over coincidence measure to approximately express data-streams by the wavelet coefficient of the characteristic stream and its energy. （3） Multi-Scales energy decomposition model is proposed to improve the compression precision. （4） The multi-scales compression algorithm and the energy conservation reconstruction algorithm are designed. （5） Extended experiments show that the compression ratio of the new methods is 2-4 times as the traditional method.