Applications of EMD in generating synopses of data stream
Liu Huizuo, Zhiwei Ni · Computer Engineering and Applications Journal · 2010
Because of the infinite growth characteristic of data stream,the data stream that has been scanned can not be all saved in memory.Maintaining a synopsis data structure dynamically from data stream is vital for many streaming data applications,so the paper will focus on technology to generate synopses of data stream.Firstly,use empirical mode decomposition to extract the trend of data stream,and filter out noise embedded in the data.Then use concise sampling method to generate synopses.Use synopses generation method presented in this paper,only synopses of those data,which are included in a sliding window,need to be saved in memory.Meanwhile,as synopses is generated based on trend sequences,which is smoother than its original sequence,so the number of the sequence's elements that have the same value increases,and this can further save amount of storage space.