Similarity search in streaming time series with the support of Skyline index

Abstract

The similarity search problem in streaming time series has become an interesting research topic because such data arise in so many applications of various areas. In this problem, the fact that data streams are updated continuously as new data arrive in real time is a challenge because of dimensionality reduction recalculation and index update costs. In this paper, using ideas of a delayed update policy on R*-tree proposed by Kontaki et al., we proposed an improved method in which indexable piecewise linear approximation PLA dimensionality reduction method with the support of Skyline index can be used to perform effectively the similarity search task in streaming time series. Experimental results show that the similarity search in streaming time series with the support of Skyline index is more efficient than the case of using R*-tree.