The Research On Streaming Data Compression Algorithm Based On Wavelet Transform

Many streaming data are generated as the rapid development of network communication technologies in recent years. Lots of real-time application systems need to deal with online and continuous streaming data. Streaming data can hardly be saved completely in conventional way because of their characteristic of infinite. And transmitting streaming data takes much in the limited network bandwidth, so it is very important to compress streaming data.This paper focuses on time series data streams dislocation similarity, clustering compression and multi-wavelet transform, including:First, data stream process method based on dynamic time warping technology. The streaming data collected in a certain period is processed as a time series. Since in the same period the factors causing data streams changes are approximately the same, so there exists dislocation similarity among data streams waves. It behaves as, the, data streams waves are similar to each other in shape, but forward or backward in time. Trandition Euclidean distance measure method can not identify the similarity of dislocation data streams, but dynamic time warping technology can do well. Based on the dynamic time warping path method to calculate corresponding points of two time series, an algorithm using prediction to estimate relationship of two time series and then find out their best match point is proposed in this paper.Second, clustering compression algorithm based on similarity of multivariate time series. Firstly, analyze the similarity among data streams with dynamic time warping distance; then do fuzzy clustering according to the similarity degree and select cluster center as the characteristic time series; lastly, save the data stream numbers for each cluster, the wavelet coefficients of characteristic time series and the match point and coefficient between every other data stream series and characteristic series as compression data. The algorithm to decompress data streams based on the best match point algorithm is also given. Simulation shows, this compression algorithm is effective, and gains higher precision than Euclidean distance measure method.Third, streaming data compression algorithm based on multi-wavelet transform. A multiattribute data stream can be broken into four sub-data matrixes in different dimension and resolution by multi-wavelet transform, and every sub-data matrix can also be decomposition. The energy of data stream mostly concentrated in low-frequency matrix. This feature is used to encode and compress the wavelet coefficients in order to compress streaming data. Experiment results show, this compression algorithm has a high compression rate, and stores characteristics of the data perfectly which ensures the original data stream can be recovered approximately after decompression.