Research on interval skyline over data stream

In many applications, monitor equipments produce a large number of time series which need to be analyzed. Segments of time series demonstrating dominating advantages over others are often of particular interest. Based on the measurement of volume attribute, the existing research about interval skyline was proposed. The query returns the time series which are not dominated by any other time series in the interval. We find such the query can not satisfy application requirements in several scenarios. So we extend the concept to interval differential skyline which focusing on the attribute of increasing rate of data. Meanwhile, currently most of the time series are generated as data streams, In this background we propose the algorithms to get the two kinds of interval skyline based on the common used wavelet synopsis. The extensive experiments on the Trojan dataset demonstrate the effective of the proposed methods.