Abstract: In many applications, we need to analyze a large number of time series. Segments of time series demonstrating dominating advantages over others arc often of particular interest. Based on volume measure, the current interval skyline query returns the time series which are not dominated by any other time series in the interval. Some times this kind of query can not satisfy application rectuirements,and the "submerge" phenomenon may exist. So we proposed the concept of the interval differential skyline which focusing on the attribute of increasing rate of data to fix the shortage of the former kind of interval skyline query. Currently most of the time series are generated as data streams. Due to the limitation of the resource, people only maintain synopses which describe the main data characters. In this background we proposed the algorithm to implement the interval differential skyline query in different granularitics based on the common used wavelet synopsis and then we improved the efficiency of the naive a algorithm on the basis of keeping the accuracy of the results. Extensive experiments on the real stock price data set demonstrate the effectiveness of the proposed methods.

Key words: Time series,Interval differential skyline,Wavelet synopsis