Similarity Search in Streaming Time Series Based on MP_C Dimensionality Reduction Method

The similarity search problem in streaming time series has become a hot research topic since 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 due to expensive dimensionality reduction recomputation and index update costs. In this paper, adopting the same ideas of a delayed update policy and an incremental computation from IDC index (Incremental Discrete Fourier Transform (DFT) Computation - Index) we propose a new approach for similarity search in streaming time series by using MP_C as dimensionality reduction method with the support of Skyline index. Our experiments show that our proposed approach for similarity search in streaming time series is more efficient than the IDC-Index in terms of pruning power, normalized CPU cost and recomputation and update time.