Efficient Similarity Searching Approach for Streaming Time Series

摘要

Similarity searching is a method for measuring the correlation of the pair of subsequences in streaming time series, which also aims to find all subsequences which are similar to the given one. However, in the burgeoning of IoE (Internet of Everything), massive numbers of IoT devices in entensive fields are continuously producing huge number of time series, named as streaming time series (STS). The high dimensionality and dynamic uncertainty of STS lead to the main challenge for similarity searching efficiency. In this paper, we propose an efficient searching approach for STS and our approach is more effective than traditional methods by utilizing the dimensionality reduction based representation and the optimized index on STS.